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2954b4d8-08b6-40f7-9d9b-169e684eea1e	suds-scalable-urban-dynamic-scenes	2303.14536	null	https://arxiv.org/abs/2303.14536v1	https://arxiv.org/pdf/2303.14536v1.pdf	SUDS: Scalable Urban Dynamic Scenes	We extend neural radiance fields (NeRFs) to dynamic large-scale urban scenes. Prior work tends to reconstruct single video clips of short durations (up to 10 seconds). Two reasons are that such methods (a) tend to scale linearly with the number of moving objects and input videos because a separate model is built for each and (b) tend to require supervision via 3D bounding boxes and panoptic labels, obtained manually or via category-specific models. As a step towards truly open-world reconstructions of dynamic cities, we introduce two key innovations: (a) we factorize the scene into three separate hash table data structures to efficiently encode static, dynamic, and far-field radiance fields, and (b) we make use of unlabeled target signals consisting of RGB images, sparse LiDAR, off-the-shelf self-supervised 2D descriptors, and most importantly, 2D optical flow. Operationalizing such inputs via photometric, geometric, and feature-metric reconstruction losses enables SUDS to decompose dynamic scenes into the static background, individual objects, and their motions. When combined with our multi-branch table representation, such reconstructions can be scaled to tens of thousands of objects across 1.2 million frames from 1700 videos spanning geospatial footprints of hundreds of kilometers, (to our knowledge) the largest dynamic NeRF built to date. We present qualitative initial results on a variety of tasks enabled by our representations, including novel-view synthesis of dynamic urban scenes, unsupervised 3D instance segmentation, and unsupervised 3D cuboid detection. To compare to prior work, we also evaluate on KITTI and Virtual KITTI 2, surpassing state-of-the-art methods that rely on ground truth 3D bounding box annotations while being 10x quicker to train.	['Deva Ramanan', 'Francesco Ferroni', 'Jason Y. Zhang', 'Haithem Turki']	2023-03-25	null	http://openaccess.thecvf.com//content/CVPR2023/html/Turki_SUDS_Scalable_Urban_Dynamic_Scenes_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Turki_SUDS_Scalable_Urban_Dynamic_Scenes_CVPR_2023_paper.pdf	cvpr-2023-1	['3d-instance-segmentation-1']	['computer-vision']	[ 6.07305691e-02 -3.30806047e-01 7.53619801e-03 -2.13401958e-01 -1.15534198e+00 -9.07854795e-01 7.61591434e-01 -4.32816535e-01 -2.34178349e-01 6.12277567e-01 4.07843798e-01 -3.28597665e-01 2.00745553e-01 -1.04614246e+00 -8.71593356e-01 -5.19451737e-01 -2.69699723e-01 8.15166831e-01 3.88188481e-01 -3.73628974e-01 -5.63149042e-02 7.49453366e-01 -1.75742662e+00 1.93827855e-03 6.33746386e-01 1.08647990e+00 1.25783116e-01 1.06108189e+00 -6.19848371e-02 8.27373028e-01 -9.34327245e-02 -2.43260548e-01 9.28010404e-01 -6.16026334e-02 -5.45492530e-01 4.15000618e-01 1.11496985e+00 -8.71471763e-01 -7.80683219e-01 5.85153997e-01 1.97001204e-01 4.23434645e-01 4.86180842e-01 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a7bf589c-9dbb-4a4c-aeef-67720d1ee1ef	extracting-multi-valued-relations-from	2307.03122	null	https://arxiv.org/abs/2307.03122v2	https://arxiv.org/pdf/2307.03122v2.pdf	Extracting Multi-valued Relations from Language Models	The widespread usage of latent language representations via pre-trained language models (LMs) suggests that they are a promising source of structured knowledge. However, existing methods focus only on a single object per subject-relation pair, even though often multiple objects are correct. To overcome this limitation, we analyze these representations for their potential to yield materialized multi-object relational knowledge. We formulate the problem as a rank-then-select task. For ranking candidate objects, we evaluate existing prompting techniques and propose new ones incorporating domain knowledge. Among the selection methods, we find that choosing objects with a likelihood above a learned relation-specific threshold gives a 49.5% F1 score. Our results highlight the difficulty of employing LMs for the multi-valued slot-filling task and pave the way for further research on extracting relational knowledge from latent language representations.	['Gerhard Weikum', 'Simon Razniewski', 'Sneha Singhania']	2023-07-06	null	null	null	null	['slot-filling']	['natural-language-processing']	[ 3.41625512e-01 7.24563539e-01 -1.14507008e+00 -4.77796048e-01 -1.16999590e+00 -4.51941907e-01 8.96808267e-01 6.05462134e-01 -3.84840578e-01 9.18905139e-01 3.68231535e-01 -2.77009249e-01 -5.12734592e-01 -7.55204916e-01 -5.55278778e-01 -2.99147487e-01 5.85754178e-02 8.74363422e-01 3.20849776e-01 -8.22427794e-02 3.20173800e-01 3.84592682e-01 -1.53712201e+00 7.12838173e-01 7.94781446e-01 7.16490507e-01 2.91906685e-01 9.49860811e-02 -6.18206680e-01 1.10478950e+00 -6.53366387e-01 -4.81162637e-01 -1.65107578e-01 -2.18218461e-01 -1.23711145e+00 1.08613588e-01 2.17011094e-01 -1.35083899e-01 -1.32452697e-01 6.66664839e-01 6.67501464e-02 3.19928437e-01 8.38415027e-01 -1.00993180e+00 -8.30092788e-01 1.03145361e+00 -1.96243957e-01 2.65884936e-01 4.52163160e-01 -7.66083002e-01 1.57748401e+00 -1.13738525e+00 8.48690629e-01 1.31462252e+00 2.96472639e-01 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73e407ea-a611-428e-8d04-aab0ac28a912	movese-movable-and-moving-lidar-scene	2306.14812	null	https://arxiv.org/abs/2306.14812v1	https://arxiv.org/pdf/2306.14812v1.pdf	MOVESe: MOVablE and Moving LiDAR Scene Segmentation with Improved Navigation in Seg-label free settings	Accurate detection of movable and moving objects in LiDAR is of vital importance for navigation. Most existing works focus on extracting and removing moving objects during navigation. Movable objects like pedestrians, parked vehicles, etc. although static may move in the future. This leads to erroneous navigation and accidents. In such cases, it becomes necessary to detect potentially movable objects. To this end, we present a learning-based approach that segments movable and moving objects by generating static parts of scenes that are otherwise occluded. Our model performs superior to existing baselines on static LiDAR reconstructions using 3 datasets including a challenging sparse industrial dataset. We achieve this without the assistance of any segmentation labels because such labels might not always be available for less popular yet important settings like industrial environments. The non-movable static parts of the scene generated by our model are of vital importance for downstream navigation for SLAM. The movable objects detected by our model can be fed to a downstream 3D detector for aiding navigation. Though we do not use segmentation, we evaluate our method against navigation baselines that use it to remove dynamic objects for SLAM. Through extensive experiments on several datasets, we showcase that our model surpasses these baselines on navigation.	['Prem Kumar Kalra', 'Anurag Mittal', 'Dhruv Makwana', 'Onkar Susladkar', 'Prashant Kumar']	2023-06-26	null	null	null	null	['scene-segmentation']	['computer-vision']	[ 4.86750722e-01 4.89216447e-02 1.91221207e-01 -2.79039681e-01 -7.13136375e-01 -7.78025091e-01 3.35169971e-01 8.61289501e-02 -6.57426298e-01 7.48110831e-01 -5.22830188e-01 -5.89392126e-01 2.97580119e-02 -9.49100137e-01 -8.57414365e-01 -4.80678767e-01 -1.28985643e-01 8.76503944e-01 1.14710677e+00 -3.11723560e-01 2.82755286e-01 8.20045710e-01 -1.73893130e+00 1.32979080e-01 8.95701945e-01 6.93162799e-01 7.55086660e-01 5.05458891e-01 -1.47303104e-01 2.26082310e-01 -5.37243068e-01 -8.07527825e-02 8.05466115e-01 -3.91980037e-02 -4.38469946e-01 8.42449069e-02 8.61352026e-01 -3.50948364e-01 8.36333409e-02 9.02420938e-01 3.33774537e-01 2.52717376e-01 7.24544823e-01 -1.30246699e+00 2.02240989e-01 3.68249804e-01 -6.69434071e-01 1.47658467e-01 1.18864208e-01 1.26988798e-01 4.84351039e-01 -1.04035497e+00 9.36293125e-01 1.29300618e+00 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-1.79767445e-01 -4.86777097e-01 -3.41622889e-01 3.09393734e-01 4.97132152e-01 -9.67955887e-01 -4.43816513e-01 8.09424669e-02]	[7.948953628540039, -2.4138131141662598]
563e8e82-752f-4292-8f02-d147e568f091	bayesian-optimisation-for-sequential	2107.12809	null	https://arxiv.org/abs/2107.12809v3	https://arxiv.org/pdf/2107.12809v3.pdf	Bayesian Optimisation for Sequential Experimental Design with Applications in Additive Manufacturing	Bayesian optimization (BO) is an approach to globally optimizing black-box objective functions that are expensive to evaluate. BO-powered experimental design has found wide application in materials science, chemistry, experimental physics, drug development, etc. This work aims to bring attention to the benefits of applying BO in designing experiments and to provide a BO manual, covering both methodology and software, for the convenience of anyone who wants to apply or learn BO. In particular, we briefly explain the BO technique, review all the applications of BO in additive manufacturing, compare and exemplify the features of different open BO libraries, unlock new potential applications of BO to other types of data (e.g., preferential output). This article is aimed at readers with some understanding of Bayesian methods, but not necessarily with knowledge of additive manufacturing; the software performance overview and implementation instructions are instrumental for any experimental-design practitioner. Moreover, our review in the field of additive manufacturing highlights the current knowledge and technological trends of BO. This article has a supplementary material online.	['Alessio Benavoli', 'Dermot Brabazon', 'Andrew Parnell', 'Mimi Zhang']	2021-07-27	null	null	null	null	['bayesian-optimisation']	['methodology']	[ 2.60501504e-01 -1.24112040e-01 -1.76836908e-01 -1.54771626e-01 -5.71283221e-01 -4.02782321e-01 -1.01205949e-02 7.11957365e-02 -9.85163962e-04 1.02530360e+00 -1.33190945e-01 -4.29898202e-01 -5.90049624e-01 -7.54226983e-01 -9.30691659e-01 -1.05051947e+00 -1.22167863e-01 5.56332290e-01 5.77020086e-02 -1.08302450e-02 6.67643189e-01 8.28427970e-01 -1.76122451e+00 4.29613441e-02 6.94928527e-01 1.02360606e+00 7.96676993e-01 5.32684982e-01 9.06231478e-02 3.05772960e-01 -6.59536600e-01 -3.67363989e-01 -1.10240780e-01 -3.00152510e-01 -2.86451668e-01 -2.87737817e-01 1.12006471e-01 5.32266172e-03 2.87290752e-01 8.95066679e-01 8.18983614e-01 2.30434731e-01 8.97830248e-01 -7.14721799e-01 -7.95328259e-01 6.71185851e-01 2.59719770e-02 -2.31405258e-01 5.24101019e-01 2.90974587e-01 6.41616285e-01 -9.21298027e-01 5.21164894e-01 1.18007457e+00 4.27197158e-01 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a23aaaf8-d732-40ea-bf41-aaba1eeb326b	data-augmentation-vision-transformer-for-fine	2211.12879	null	https://arxiv.org/abs/2211.12879v2	https://arxiv.org/pdf/2211.12879v2.pdf	Data Augmentation Vision Transformer for Fine-grained Image Classification	Recently, the vision transformer (ViT) has made breakthroughs in image recognition. Its self-attention mechanism (MSA) can extract discriminative labeling information of different pixel blocks to improve image classification accuracy. However, the classification marks in their deep layers tend to ignore local features between layers. In addition, the embedding layer will be fixed-size pixel blocks. Input network Inevitably introduces additional image noise. To this end, we study a data augmentation vision transformer (DAVT) based on data augmentation and proposes a data augmentation method for attention cropping, which uses attention weights as the guide to crop images and improve the ability of the network to learn critical features. Secondly, we also propose a hierarchical attention selection (HAS) method, which improves the ability of discriminative markers between levels of learning by filtering and fusing labels between levels. Experimental results show that the accuracy of this method on the two general datasets, CUB-200-2011, and Stanford Dogs, is better than the existing mainstream methods, and its accuracy is 1.4\% and 1.6\% higher than the original ViT, respectively	['Weijie Wu', 'Weibin Qiu', 'Liqiang Zhu', 'Chao Hu']	2022-11-23	null	null	null	null	['fine-grained-image-classification']	['computer-vision']	[ 3.41338068e-01 9.21494793e-03 -1.70750409e-01 -3.40591908e-01 -2.67315090e-01 6.72130436e-02 2.54169375e-01 -1.78551912e-01 -5.43654859e-01 4.18338150e-01 7.77090341e-02 4.09997851e-02 2.54074723e-01 -7.92961299e-01 -7.82648027e-01 -8.66354823e-01 2.33697191e-01 1.42083550e-02 4.87343103e-01 -9.87350866e-02 8.38743672e-02 1.42758638e-01 -1.57893586e+00 4.70367432e-01 1.01943076e+00 1.38450325e+00 5.37381291e-01 1.59778848e-01 -2.96866059e-01 1.11308050e+00 -4.99202192e-01 -3.03214222e-01 2.35133454e-01 -3.29824895e-01 -6.25496626e-01 2.53089875e-01 5.88408291e-01 -4.48203534e-01 -4.97054309e-01 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53039bdf-677f-459a-a98e-58342185c9e3	improving-contextual-spelling-correction-by	2302.11192	null	https://arxiv.org/abs/2302.11192v1	https://arxiv.org/pdf/2302.11192v1.pdf	Improving Contextual Spelling Correction by External Acoustics Attention and Semantic Aware Data Augmentation	We previously proposed contextual spelling correction (CSC) to correct the output of end-to-end (E2E) automatic speech recognition (ASR) models with contextual information such as name, place, etc. Although CSC has achieved reasonable improvement in the biasing problem, there are still two drawbacks for further accuracy improvement. First, due to information limitation in text only hypothesis or weak performance of ASR model on rare domains, the CSC model may fail to correct phrases with similar pronunciation or anti-context cases where all biasing phrases are not present in the utterance. Second, there is a discrepancy between the training and inference of CSC. The bias list in training is randomly selected but in inference there may be more similarity between ground truth phrase and other phrases. To solve above limitations, in this paper we propose an improved non-autoregressive (NAR) spelling correction model for contextual biasing in E2E neural transducer-based ASR systems to improve the previous CSC model from two perspectives: Firstly, we incorporate acoustics information with an external attention as well as text hypotheses into CSC to better distinguish target phrase from dissimilar or irrelevant phrases. Secondly, we design a semantic aware data augmentation schema in training phrase to reduce the mismatch between training and inference to further boost the biasing accuracy. Experiments show that the improved method outperforms the baseline ASR+Biasing system by as much as 20.3% relative name recall gain and achieves stable improvement compared to the previous CSC method over different bias list name coverage ratio.	['Sheng Zhao', 'Jinyu Li', 'Yanqing Liu', 'Xiaoqiang Wang']	2023-02-22	null	null	null	null	['spelling-correction']	['natural-language-processing']	[ 4.58429545e-01 1.19727597e-01 -6.80435076e-02 -5.70752800e-01 -9.93030488e-01 -3.48241806e-01 4.65666652e-01 -8.83429646e-02 -7.68784523e-01 6.22614205e-01 7.10784197e-01 -4.92849141e-01 1.51250079e-01 -4.97675538e-01 -7.96822667e-01 -3.82253826e-01 7.34442711e-01 5.19325972e-01 4.31395411e-01 -7.23734915e-01 1.90864936e-01 3.15700531e-01 -1.40853882e+00 4.81877476e-01 8.59477997e-01 6.55590057e-01 7.98063576e-01 6.66432321e-01 -3.14029753e-01 5.88260055e-01 -9.16511416e-01 -2.71351248e-01 -5.52154183e-02 -2.67292827e-01 -6.85887814e-01 -2.65379816e-01 4.33942944e-01 -1.36245340e-01 -4.88391608e-01 1.05956399e+00 9.05100882e-01 4.33536321e-01 4.58389491e-01 -7.15881348e-01 -8.89674544e-01 1.02221584e+00 -2.80036986e-01 5.55637598e-01 1.76853672e-01 -2.21776903e-01 9.89670515e-01 -1.44077158e+00 3.40268463e-02 1.36839211e+00 5.78821957e-01 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-3.42720449e-01 5.40965736e-01 3.92793119e-01 -5.67110181e-01 -5.02478123e-01 -2.06758738e-01]	[14.354682922363281, 6.712670803070068]
ace1e298-9b84-4c18-9082-6d3dcdf126f6	machine-learning-for-detecting-data	2012.09344	null	https://arxiv.org/abs/2012.09344v2	https://arxiv.org/pdf/2012.09344v2.pdf	Machine Learning for Detecting Data Exfiltration: A Review	Context: Research at the intersection of cybersecurity, Machine Learning (ML), and Software Engineering (SE) has recently taken significant steps in proposing countermeasures for detecting sophisticated data exfiltration attacks. It is important to systematically review and synthesize the ML-based data exfiltration countermeasures for building a body of knowledge on this important topic. Objective: This paper aims at systematically reviewing ML-based data exfiltration countermeasures to identify and classify ML approaches, feature engineering techniques, evaluation datasets, and performance metrics used for these countermeasures. This review also aims at identifying gaps in research on ML-based data exfiltration countermeasures. Method: We used a Systematic Literature Review (SLR) method to select and review {92} papers. Results: The review has enabled us to (a) classify the ML approaches used in the countermeasures into data-driven, and behaviour-driven approaches, (b) categorize features into six types: behavioural, content-based, statistical, syntactical, spatial and temporal, (c) classify the evaluation datasets into simulated, synthesized, and real datasets and (d) identify 11 performance measures used by these studies. Conclusion: We conclude that: (i) the integration of data-driven and behaviour-driven approaches should be explored; (ii) There is a need of developing high quality and large size evaluation datasets; (iii) Incremental ML model training should be incorporated in countermeasures; (iv) resilience to adversarial learning should be considered and explored during the development of countermeasures to avoid poisoning attacks; and (v) the use of automated feature engineering should be encouraged for efficiently detecting data exfiltration attacks.	['Raj Gaire', 'M. Ali Babar', 'Faheem Ullah', 'Bushra Sabir']	2020-12-17	null	null	null	null	['automated-feature-engineering']	['methodology']	[ 1.90209895e-01 -5.23920298e-01 -4.83859986e-01 -1.70315821e-02 -4.08365071e-01 -1.00740111e+00 8.66837502e-01 7.95761526e-01 -3.33293647e-01 1.67419553e-01 2.36855119e-01 -1.05178976e+00 -6.62985623e-01 -8.54559600e-01 -4.71603245e-01 -4.06212449e-01 -4.36384618e-01 -3.34846675e-01 1.06840007e-01 -2.27669209e-01 1.15205157e+00 1.10111880e+00 -1.58123326e+00 3.62663269e-01 4.50546592e-01 8.47914457e-01 -4.82757241e-01 8.12475860e-01 1.53478846e-01 8.87649298e-01 -1.04550886e+00 -1.85015917e-01 3.95923436e-01 -4.50324714e-02 -9.27021563e-01 -4.83736135e-02 7.26891011e-02 -3.16484928e-01 -3.59283201e-02 1.03559899e+00 4.00046468e-01 -3.47153485e-01 3.17926943e-01 -1.65254176e+00 -5.75391650e-01 3.85061890e-01 -3.46849918e-01 5.25566101e-01 5.45553625e-01 5.95378697e-01 4.35757130e-01 -4.26699400e-01 4.13756788e-01 1.17061841e+00 4.77335960e-01 4.73014355e-01 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-3.86952907e-01 4.16840672e-01 1.22392051e-01 -7.56487072e-01 -8.57292414e-01 -4.66596127e-01]	[5.35377311706543, 7.217463970184326]
a10b6339-bd50-4555-9d9e-eccfda14be86	bump-hunting-through-density-curvature	2208.00174	null	https://arxiv.org/abs/2208.00174v2	https://arxiv.org/pdf/2208.00174v2.pdf	Bump hunting through density curvature features	Bump hunting deals with finding in sample spaces meaningful data subsets known as bumps. These have traditionally been conceived as modal or concave regions in the graph of the underlying density function. We define an abstract bump construct based on curvature functionals of the probability density. Then, we explore several alternative characterizations involving derivatives up to second order. In particular, a suitable implementation of Good and Gaskins' original concave bumps is proposed in the multivariate case. Moreover, we bring to exploratory data analysis concepts like the mean curvature and the Laplacian that have produced good results in applied domains. Our methodology addresses the approximation of the curvature functional with a plug-in kernel density estimator. We provide theoretical results that assure the asymptotic consistency of bump boundaries in the Hausdorff distance with affordable convergence rates. We also present asymptotically valid and consistent confidence regions bounding curvature bumps. The theory is illustrated through several use cases in sports analytics with datasets from the NBA, MLB and NFL. We conclude that the different curvature instances effectively combine to generate insightful visualizations.	['Javier Fernández Serrano', 'José E. Chacón']	2022-07-30	null	null	null	null	['sports-analytics']	['computer-vision']	[-5.16943812e-01 3.24833184e-01 -1.21145435e-01 -2.99878150e-01 -5.65993965e-01 -4.29548621e-01 3.55410993e-01 6.42649710e-01 -1.93462908e-01 7.43464828e-01 7.42523968e-02 -3.45706791e-01 -7.34373033e-01 -7.14486063e-01 -7.40420818e-01 -5.37396550e-01 -7.86200047e-01 5.22481024e-01 2.34567195e-01 -2.51709342e-01 6.31693065e-01 6.10477865e-01 -1.70671046e+00 -5.67147173e-02 1.31038487e+00 9.09160554e-01 -2.53593892e-01 5.97333431e-01 -2.23515350e-02 1.24382392e-01 -4.85032499e-01 -5.51023841e-01 1.94816053e-01 -2.29072608e-02 -7.13346481e-01 -1.37423828e-01 3.50430697e-01 -1.19057201e-01 6.87673926e-01 1.01142967e+00 1.74291387e-01 2.41134927e-01 1.12525392e+00 -1.66647220e+00 -5.42970598e-01 3.54598552e-01 -8.68063569e-01 -5.58614172e-02 4.22149241e-01 -2.42518544e-01 7.84419417e-01 -1.00708818e+00 8.74915779e-01 1.24051595e+00 1.02415264e+00 1.84877235e-02 -1.59676647e+00 -3.30655538e-02 -2.92288959e-01 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4115182c-89a3-44b1-9206-06efc41b2625	single-unit-status-in-deep-convolutional	2002.06274	null	https://arxiv.org/abs/2002.06274v2	https://arxiv.org/pdf/2002.06274v2.pdf	Single Unit Status in Deep Convolutional Neural Network Codes for Face Identification: Sparseness Redefined	Deep convolutional neural networks (DCNNs) trained for face identification develop representations that generalize over variable images, while retaining subject (e.g., gender) and image (e.g., viewpoint) information. Identity, gender, and viewpoint codes were studied at the "neural unit" and ensemble levels of a face-identification network. At the unit level, identification, gender classification, and viewpoint estimation were measured by deleting units to create variably-sized, randomly-sampled subspaces at the top network layer. Identification of 3,531 identities remained high (area under the ROC approximately 1.0) as dimensionality decreased from 512 units to 16 (0.95), 4 (0.80), and 2 (0.72) units. Individual identities separated statistically on every top-layer unit. Cross-unit responses were minimally correlated, indicating that units code non-redundant identity cues. This "distributed" code requires only a sparse, random sample of units to identify faces accurately. Gender classification declined gradually and viewpoint estimation fell steeply as dimensionality decreased. Individual units were weakly predictive of gender and viewpoint, but ensembles proved effective predictors. Therefore, distributed and sparse codes co-exist in the network units to represent different face attributes. At the ensemble level, principal component analysis of face representations showed that identity, gender, and viewpoint information separated into high-dimensional subspaces, ordered by explained variance. Identity, gender, and viewpoint information contributed to all individual unit responses, undercutting a neural tuning analogy for face attributes. Interpretation of neural-like codes from DCNNs, and by analogy, high-level visual codes, cannot be inferred from single unit responses. Instead, "meaning" is encoded by directions in the high-dimensional space.	["Alice J. O'Toole", 'Y. Ivette Colón', 'Matthew Q. Hill', 'Connor J. Parde', 'Carlos D. Castillo', 'Prithviraj Dhar']	2020-02-14	null	null	null	null	['viewpoint-estimation']	['computer-vision']	[ 1.76845178e-01 -1.26328632e-01 -1.10415593e-01 -7.89640069e-01 -2.48646706e-01 -1.06547749e+00 3.20808023e-01 -2.28529423e-01 -2.60487407e-01 4.62406307e-01 1.18633367e-01 1.38408151e-02 3.10826674e-02 -5.76396465e-01 -5.58811426e-01 -8.36359978e-01 -8.14884081e-02 3.48698139e-01 -6.26099467e-01 3.14416796e-01 1.23792291e-01 6.59363568e-01 -1.86407304e+00 7.33683288e-01 4.18970734e-01 1.27870560e+00 -1.79765448e-01 3.29977036e-01 4.18087188e-03 2.40520582e-01 -5.58272004e-01 -2.69454837e-01 2.15077937e-01 -7.90994048e-01 -1.45841792e-01 -7.73930773e-02 1.16890860e+00 -2.95694351e-01 -1.52383313e-01 9.83326256e-01 4.95839298e-01 -4.40218337e-02 1.20629716e+00 -1.16695893e+00 -1.20425141e+00 3.86786848e-01 -5.00256956e-01 -1.95963934e-01 9.18741822e-02 3.66662219e-02 1.09810817e+00 -1.18356681e+00 5.17732799e-01 1.44561088e+00 9.15947974e-01 8.63779008e-01 -1.84787107e+00 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-1.14681089e+00 -8.18803370e-01 3.87373239e-01 3.23984861e-01 -7.52071142e-01 -7.45597184e-01 6.89022690e-02]	[12.956074714660645, 1.2669785022735596]
5e66b86e-1e02-4c68-8328-e69a95e089e9	guiding-attention-in-sequence-to-sequence	2002.08801	null	https://arxiv.org/abs/2002.08801v2	https://arxiv.org/pdf/2002.08801v2.pdf	Guiding attention in Sequence-to-sequence models for Dialogue Act prediction	The task of predicting dialog acts (DA) based on conversational dialog is a key component in the development of conversational agents. Accurately predicting DAs requires a precise modeling of both the conversation and the global tag dependencies. We leverage seq2seq approaches widely adopted in Neural Machine Translation (NMT) to improve the modelling of tag sequentiality. Seq2seq models are known to learn complex global dependencies while currently proposed approaches using linear conditional random fields (CRF) only model local tag dependencies. In this work, we introduce a seq2seq model tailored for DA classification using: a hierarchical encoder, a novel guided attention mechanism and beam search applied to both training and inference. Compared to the state of the art our model does not require handcrafted features and is trained end-to-end. Furthermore, the proposed approach achieves an unmatched accuracy score of 85% on SwDA, and state-of-the-art accuracy score of 91.6% on MRDA.	['Pierre Colombo', 'Matteo Manica', 'Emmanuel Vignon', 'Emile Chapuis', 'Giovanna Varni', 'Chloe Clavel']	2020-02-20	null	null	null	null	['dialogue-act-classification']	['natural-language-processing']	[ 1.74348027e-01 5.25561631e-01 -8.11116770e-02 -8.10737550e-01 -7.23432541e-01 -5.64893842e-01 9.80029881e-01 -2.54953086e-01 -4.31799114e-01 1.00758684e+00 5.37925661e-01 -2.95170426e-01 2.93317735e-01 -4.80681121e-01 -3.06822270e-01 -3.65494162e-01 3.74623351e-02 1.28061545e+00 1.76326796e-01 -4.36101854e-01 7.54477531e-02 1.01684920e-01 -1.00776255e+00 6.17063284e-01 9.06223416e-01 7.85715640e-01 3.34549576e-01 1.01102555e+00 -4.58927214e-01 1.45586526e+00 -7.57700682e-01 -7.92877555e-01 -1.41642958e-01 -5.65398872e-01 -1.14172423e+00 -2.12481007e-01 3.76355976e-01 -4.78236169e-01 -1.46690488e-01 6.37830794e-01 3.98143053e-01 8.40550512e-02 6.06462836e-01 -7.83217072e-01 -3.64697844e-01 9.21959281e-01 1.69654220e-01 4.42095138e-02 4.63772684e-01 3.23698781e-02 1.25722098e+00 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2efcc01b-b5ce-4ffe-86f6-6b2638027522	text-and-style-conditioned-gan-for-generation	2009.00678	null	https://arxiv.org/abs/2009.00678v1	https://arxiv.org/pdf/2009.00678v1.pdf	Text and Style Conditioned GAN for Generation of Offline Handwriting Lines	This paper presents a GAN for generating images of handwritten lines conditioned on arbitrary text and latent style vectors. Unlike prior work, which produce stroke points or single-word images, this model generates entire lines of offline handwriting. The model produces variable-sized images by using style vectors to determine character widths. A generator network is trained with GAN and autoencoder techniques to learn style, and uses a pre-trained handwriting recognition network to induce legibility. A study using human evaluators demonstrates that the model produces images that appear to be written by a human. After training, the encoder network can extract a style vector from an image, allowing images in a similar style to be generated, but with arbitrary text.	['Bryan Morse', 'Curtis Wigington', 'Brian Price', 'Chris Tensmeyer', 'Brian Davis', 'Rajiv Jain']	2020-09-01	null	null	null	null	['handwriting-generation']	['computer-vision']	[ 7.17269957e-01 3.78504932e-01 -7.74844438e-02 -3.50860238e-01 -3.69558603e-01 -1.13297272e+00 5.82640350e-01 -7.30589092e-01 3.50843184e-02 8.00673306e-01 6.19225129e-02 -3.01642388e-01 6.01918638e-01 -1.02635026e+00 -7.59012938e-01 -5.66645503e-01 6.64647281e-01 6.98833108e-01 -3.00625712e-01 -8.79694670e-02 5.17198205e-01 8.98808241e-01 -8.36591959e-01 6.48737311e-01 5.04629314e-01 3.47901195e-01 1.22115813e-01 1.41540742e+00 3.65137458e-02 1.17806590e+00 -1.47957432e+00 -6.15034819e-01 5.57964683e-01 -1.00497806e+00 -5.46891332e-01 7.85656631e-01 6.74849749e-01 -1.02465773e+00 -6.58946514e-01 7.15449452e-01 3.81119579e-01 -2.27884591e-01 9.43513870e-01 -1.00824547e+00 -1.35936701e+00 5.95858872e-01 -2.49172032e-01 -5.33788145e-01 3.76096070e-01 5.62119901e-01 6.43359065e-01 -4.42227274e-01 8.57652009e-01 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5f0f2cd0-33a5-489f-ba66-06e3d23cc56a	modeling-inter-class-and-intra-class	2210.03591	null	https://arxiv.org/abs/2210.03591v3	https://arxiv.org/pdf/2210.03591v3.pdf	Modeling Inter-Class and Intra-Class Constraints in Novel Class Discovery	Novel class discovery (NCD) aims at learning a model that transfers the common knowledge from a class-disjoint labelled dataset to another unlabelled dataset and discovers new classes (clusters) within it. Many methods, as well as elaborate training pipelines and appropriate objectives, have been proposed and considerably boosted performance on NCD tasks. Despite all this, we find that the existing methods do not sufficiently take advantage of the essence of the NCD setting. To this end, in this paper, we propose to model both inter-class and intra-class constraints in NCD based on the symmetric Kullback-Leibler divergence (sKLD). Specifically, we propose an inter-class sKLD constraint to effectively exploit the disjoint relationship between labelled and unlabelled classes, enforcing the separability for different classes in the embedding space. In addition, we present an intra-class sKLD constraint to explicitly constrain the intra-relationship between a sample and its augmentations and ensure the stability of the training process at the same time. We conduct extensive experiments on the popular CIFAR10, CIFAR100 and ImageNet benchmarks and successfully demonstrate that our method can establish a new state of the art and can achieve significant performance improvements, e.g., 3.5%/3.7% clustering accuracy improvements on CIFAR100-50 dataset split under the task-aware/-agnostic evaluation protocol, over previous state-of-the-art methods. Code is available at https://github.com/FanZhichen/NCD-IIC.	['Yang Gao', 'Jing Huo', 'Zhichen Fan', 'Wenbin Li']	2022-10-07	null	http://openaccess.thecvf.com//content/CVPR2023/html/Li_Modeling_Inter-Class_and_Intra-Class_Constraints_in_Novel_Class_Discovery_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Modeling_Inter-Class_and_Intra-Class_Constraints_in_Novel_Class_Discovery_CVPR_2023_paper.pdf	cvpr-2023-1	['novel-class-discovery', 'novel-class-discovery']	['computer-vision', 'methodology']	[ 2.46836208e-02 -1.60390660e-01 -3.06359679e-01 -6.40526652e-01 -6.91072583e-01 -5.35375655e-01 7.98279464e-01 1.72868550e-01 -5.44748604e-01 4.76881355e-01 -8.94313902e-02 -1.80668697e-01 -3.49283606e-01 -4.97147322e-01 -4.52542394e-01 -7.58457303e-01 -8.81066024e-02 4.06512022e-01 1.94857910e-01 2.95913249e-01 9.13691744e-02 4.97668982e-01 -1.58852768e+00 3.17983001e-01 6.74718499e-01 1.17795348e+00 1.10469460e-01 3.13112348e-01 4.52773497e-02 5.31957746e-01 -4.33543712e-01 -2.03320920e-01 3.92016530e-01 -1.81381658e-01 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6c87fcdf-48c6-4c62-979f-b8ee80e9e1ca	anisotropic-multi-scale-graph-convolutional	2210.09466	null	https://arxiv.org/abs/2210.09466v2	https://arxiv.org/pdf/2210.09466v2.pdf	Anisotropic Multi-Scale Graph Convolutional Network for Dense Shape Correspondence	This paper studies 3D dense shape correspondence, a key shape analysis application in computer vision and graphics. We introduce a novel hybrid geometric deep learning-based model that learns geometrically meaningful and discretization-independent features with a U-Net model as the primary node feature extraction module, followed by a successive spectral-based graph convolutional network. To create a diverse set of filters, we use anisotropic wavelet basis filters, being sensitive to both different directions and band-passes. This filter set overcomes the over-smoothing behavior of conventional graph neural networks. To further improve the model's performance, we add a function that perturbs the feature maps in the last layer ahead of fully connected layers, forcing the network to learn more discriminative features overall. The resulting correspondence maps show state-of-the-art performance on the benchmark datasets based on average geodesic errors and superior robustness to discretization in 3D meshes. Our approach provides new insights and practical solutions to the dense shape correspondence research.	['Yalin Wang', 'Zhangsihao Yang', 'Wenhui Zhu', 'Mohammad Farazi']	2022-10-17	null	null	null	null	['3d-dense-shape-correspondence']	['computer-vision']	[-1.67999174e-02 1.70952901e-01 1.31827816e-01 -2.96530902e-01 -2.60992140e-01 -5.00801861e-01 7.35222280e-01 2.96055764e-01 -1.15295410e-01 3.01209152e-01 5.57571501e-02 -2.39104792e-01 -1.38868466e-01 -1.35801065e+00 -8.42351735e-01 -5.11205673e-01 -4.61522877e-01 4.92432058e-01 5.28402686e-01 -2.98934102e-01 2.53262103e-01 1.11576557e+00 -1.21137583e+00 1.77001968e-01 7.44199395e-01 1.17322850e+00 -2.28251547e-01 4.47578222e-01 -1.54124051e-01 2.25414038e-01 1.47128671e-01 -2.75011003e-01 5.48598707e-01 -1.15300730e-01 -7.42054880e-01 2.91142091e-02 7.72809625e-01 -3.03474665e-01 -4.80669945e-01 1.00450325e+00 5.73009491e-01 1.45771131e-01 7.61915803e-01 -9.00095165e-01 -9.33088481e-01 2.86739200e-01 -5.49675345e-01 -3.41935232e-02 1.35455817e-01 -5.64320805e-03 1.01961029e+00 -1.11269546e+00 7.86935508e-01 1.50756383e+00 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-1.35977781e+00 -3.45319390e-01 -6.99661016e-01 -3.52646649e-01 6.12031698e-01 3.52583379e-01 -7.75368035e-01 -2.35257387e-01 -2.77756602e-01]	[8.323112487792969, -3.6938765048980713]
4708ec23-211a-4848-bbc3-f7b4df906092	correction-of-out-of-focus-microscopic-images	null	null	https://www.sciencedirect.com/science/article/pii/S2001037022001192	https://doi.org/10.1016/j.csbj.2022.04.003	Correction of out-of-focus microscopic images by deep learning	Motivation Microscopic images are widely used in basic biomedical research, disease diagnosis and medical discovery. Obtaining high-quality in-focus microscopy images has been a cornerstone of the microscopy. However, images obtained by microscopes are often out-of-focus, resulting in poor performance in research and diagnosis. Results To solve the out-of-focus issue in microscopy, we developed a Cycle Generative Adversarial Network (CycleGAN) based model and a multi-component weighted loss function. We train and test our network in two self-collected datasets, namely Leishmania parasite dataset captured by a bright-field microscope, and bovine pulmonary artery endothelial cells (BPAEC) captured by a confocal fluorescence microscope. In comparison to other GAN-based deblurring methods, the proposed model reached state-of-the-art performance in correction. Another publicly available dataset, human cells dataset from the Broad Bioimage Benchmark Collection is used for evaluating the generalization abilities of the model. Our model showed excellent generalization capability, which could transfer to different types of microscopic image datasets. Availability and Implementation Code and dataset are publicly available at: https://github.com/jiangdat/COMI.	['Yang Zhang.', 'Mario Juhas', 'Shiming Tang', 'Junyi Li', 'Weihuang Liu', 'Hao Jiang', 'Chi Zhang']	2022-04-26	null	null	null	computational-and-structural-biotechnology	['medical-image-generation']	['medical']	[ 2.56776720e-01 -4.64030832e-01 3.68400991e-01 -2.66810544e-02 -7.20644057e-01 -5.25594413e-01 2.53222644e-01 -2.59751081e-01 -7.37397909e-01 1.07050347e+00 -5.12081087e-01 -2.35752881e-01 3.40342253e-01 -7.10687339e-01 -7.13660836e-01 -1.16592908e+00 1.29168183e-01 2.76987493e-01 7.47654215e-02 3.06875497e-01 2.01619267e-01 6.92328334e-01 -7.59428024e-01 5.91920316e-02 7.29134142e-01 5.03459990e-01 4.48261738e-01 8.88273478e-01 2.29366064e-01 8.20268810e-01 -6.59659922e-01 -2.42023945e-01 1.75476074e-02 -6.05452955e-01 -8.44775200e-01 -2.44468078e-02 8.05411637e-02 -3.65575045e-01 -4.03962642e-01 1.24980438e+00 8.62291157e-01 -2.13208959e-01 5.37057400e-01 -1.04748774e+00 -1.06356764e+00 -1.99094564e-02 -7.54970491e-01 7.39928186e-01 2.31842021e-03 4.66958135e-01 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86663ef6-489f-4fd7-8ede-4dd1f99c4eba	a-survey-of-federated-learning-for-connected	2303.10677	null	https://arxiv.org/abs/2303.10677v1	https://arxiv.org/pdf/2303.10677v1.pdf	A Survey of Federated Learning for Connected and Automated Vehicles	Connected and Automated Vehicles (CAVs) are one of the emerging technologies in the automotive domain that has the potential to alleviate the issues of accidents, traffic congestion, and pollutant emissions, leading to a safe, efficient, and sustainable transportation system. Machine learning-based methods are widely used in CAVs for crucial tasks like perception, motion planning, and motion control, where machine learning models in CAVs are solely trained using the local vehicle data, and the performance is not certain when exposed to new environments or unseen conditions. Federated learning (FL) is an effective solution for CAVs that enables a collaborative model development with multiple vehicles in a distributed learning framework. FL enables CAVs to learn from a wide range of driving environments and improve their overall performance while ensuring the privacy and security of local vehicle data. In this paper, we review the progress accomplished by researchers in applying FL to CAVs. A broader view of the various data modalities and algorithms that have been implemented on CAVs is provided. Specific applications of FL are reviewed in detail, and an analysis of the challenges and future scope of research are presented.	['Ziran Wang', 'Stanislaw H /. Zak', 'Liangqi Yuan', 'Vishnu Pandi Chellapandi']	2023-03-19	null	null	null	null	['motion-planning']	['robots']	[-1.88907042e-01 -3.85350771e-02 -4.51392084e-01 -4.05361891e-01 -6.32504046e-01 -2.92997986e-01 6.64555967e-01 -4.31288518e-02 -2.72356719e-01 5.75597227e-01 -2.40489885e-01 -3.70168835e-01 -3.42768840e-02 -9.74043787e-01 -6.29086375e-01 -7.83857226e-01 1.89067096e-01 5.57298139e-02 5.64367592e-01 -2.12270945e-01 -2.10066482e-01 8.10295761e-01 -2.25779533e+00 9.49420854e-02 5.32186210e-01 1.08213961e+00 2.68072546e-01 5.22217453e-01 -3.74797098e-02 7.53432810e-01 -2.93733120e-01 -3.48325729e-01 3.85930449e-01 1.49323821e-01 -4.18610960e-01 -2.18978509e-01 2.57462472e-01 -5.09034023e-02 -6.73987925e-01 8.90024543e-01 2.84728054e-02 4.43532407e-01 4.10427153e-01 -2.23155522e+00 -7.73657709e-02 -1.29559711e-01 -6.37949705e-02 -3.20588738e-01 -2.37396464e-01 4.03550893e-01 3.80316466e-01 -7.03819156e-01 6.07879817e-01 1.11832368e+00 5.10563254e-01 7.30216861e-01 -6.86593473e-01 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91347090-c95f-4b87-aeb4-f71b66b54c70	conditioned-u-net-introducing-a-control	1907.01277	null	https://arxiv.org/abs/1907.01277v3	https://arxiv.org/pdf/1907.01277v3.pdf	Conditioned-U-Net: Introducing a Control Mechanism in the U-Net for Multiple Source Separations	Data-driven models for audio source separation such as U-Net or Wave-U-Net are usually models dedicated to and specifically trained for a single task, e.g. a particular instrument isolation. Training them for various tasks at once commonly results in worse performances than training them for a single specialized task. In this work, we introduce the Conditioned-U-Net (C-U-Net) which adds a control mechanism to the standard U-Net. The control mechanism allows us to train a unique and generic U-Net to perform the separation of various instruments. The C-U-Net decides the instrument to isolate according to a one-hot-encoding input vector. The input vector is embedded to obtain the parameters that control Feature-wise Linear Modulation (FiLM) layers. FiLM layers modify the U-Net feature maps in order to separate the desired instrument via affine transformations. The C-U-Net performs different instrument separations, all with a single model achieving the same performances as the dedicated ones at a lower cost.	['Gabriel Meseguer-Brocal', 'Geoffroy Peeters']	2019-07-02	null	null	null	null	['audio-source-separation']	['audio']	[ 5.27446628e-01 -2.62277909e-02 -1.85102925e-01 -2.24472046e-01 -1.00242615e+00 -5.41933417e-01 3.59086692e-01 2.79578753e-03 -2.79324085e-01 2.81542331e-01 4.42215151e-06 -8.14945847e-02 -3.22379261e-01 -5.46979308e-01 -8.31480682e-01 -9.20700014e-01 4.10702527e-02 1.88194349e-01 1.61555573e-01 -2.61551619e-01 1.36333689e-01 4.76760924e-01 -1.78777921e+00 4.74178374e-01 5.64795554e-01 1.17404294e+00 4.06001776e-01 1.07932961e+00 5.87064028e-02 6.37757599e-01 -5.75901866e-01 1.83621719e-01 1.91379234e-01 -8.15768659e-01 -6.40090048e-01 -7.77486041e-02 4.40374106e-01 1.78874061e-02 -1.49393594e-03 1.02209663e+00 7.82254755e-01 2.33620763e-01 9.35883284e-01 -9.75035608e-01 -2.27497771e-01 1.09875715e+00 -8.90447646e-02 -4.88492437e-02 3.03070992e-01 -1.46497384e-01 1.36101389e+00 -7.18532145e-01 4.67199653e-01 9.89674568e-01 6.72750533e-01 6.27136350e-01 -1.58996868e+00 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-9.89422023e-01 7.73525760e-02 8.09361815e-01 -4.20009971e-01 -3.64164501e-01 1.31667495e-01]	[15.402838706970215, 5.578395366668701]
db3c3c11-295d-41ca-8034-16c182e103e8	efficient-chemical-space-exploration-using	2209.00514	null	https://arxiv.org/abs/2209.00514v1	https://arxiv.org/pdf/2209.00514v1.pdf	Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular Simulation	We introduce an explorative active learning (AL) algorithm based on Gaussian process regression and marginalized graph kernel (GPR-MGK) to explore chemical space with minimum cost. Using high-throughput molecular dynamics simulation to generate data and graph neural network (GNN) to predict, we constructed an active learning molecular simulation framework for thermodynamic property prediction. In specific, targeting 251,728 alkane molecules consisting of 4 to 19 carbon atoms and their liquid physical properties: densities, heat capacities, and vaporization enthalpies, we use the AL algorithm to select the most informative molecules to represent the chemical space. Validation of computational and experimental test sets shows that only 313 (0.124\% of the total) molecules were sufficient to train an accurate GNN model with $\rm R^2 > 0.99$ for computational test sets and $\rm R^2 > 0.94$ for experimental test sets. We highlight two advantages of the presented AL algorithm: compatibility with high-throughput data generation and reliable uncertainty quantification.	['Huai Sun', 'Guang Lin', 'Liang Wu', 'Hongyi Liu', 'Zheng Gong', 'Yu-Hang Tang', 'Yan Xiang']	2022-09-01	null	null	null	null	['gpr', 'gpr']	['computer-vision', 'miscellaneous']	[ 9.93016958e-02 3.01790684e-01 -3.24439526e-01 -1.65958956e-01 -6.99100316e-01 -4.06544864e-01 5.02440751e-01 9.00130808e-01 -6.45512700e-01 1.47897780e+00 -5.28900266e-01 -4.58312690e-01 -3.41886818e-01 -1.01256704e+00 -7.20490396e-01 -1.03568339e+00 -7.76757240e-01 6.92248762e-01 1.82814151e-02 2.47632802e-01 3.96957099e-01 9.29434061e-01 -1.28422439e+00 -2.84509271e-01 1.51194406e+00 1.04813921e+00 7.71809816e-02 3.97593498e-01 -1.17742538e-01 7.55724669e-01 -1.83673203e-01 -1.54883504e-01 9.11938697e-02 -3.92368704e-01 -5.55469692e-01 -5.64971387e-01 -3.39683890e-01 4.17010546e-01 5.52523434e-02 9.41724896e-01 5.17970741e-01 1.02623332e+00 1.23280180e+00 -9.19755340e-01 -3.70102108e-01 5.77668428e-01 -1.26184374e-01 -1.92821592e-01 2.69908100e-01 5.74535847e-01 8.25872242e-01 -1.12434173e+00 7.85888791e-01 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e6d6b835-8b87-4bfb-9220-2ce9bf625fc4	multi-projection-fusion-for-real-time	2011.01974	null	https://arxiv.org/abs/2011.01974v2	https://arxiv.org/pdf/2011.01974v2.pdf	Multi Projection Fusion for Real-time Semantic Segmentation of 3D LiDAR Point Clouds	Semantic segmentation of 3D point cloud data is essential for enhanced high-level perception in autonomous platforms. Furthermore, given the increasing deployment of LiDAR sensors onboard of cars and drones, a special emphasis is also placed on non-computationally intensive algorithms that operate on mobile GPUs. Previous efficient state-of-the-art methods relied on 2D spherical projection of point clouds as input for 2D fully convolutional neural networks to balance the accuracy-speed trade-off. This paper introduces a novel approach for 3D point cloud semantic segmentation that exploits multiple projections of the point cloud to mitigate the loss of information inherent in single projection methods. Our Multi-Projection Fusion (MPF) framework analyzes spherical and bird's-eye view projections using two separate highly-efficient 2D fully convolutional models then combines the segmentation results of both views. The proposed framework is validated on the SemanticKITTI dataset where it achieved a mIoU of 55.5 which is higher than state-of-the-art projection-based methods RangeNet++ and PolarNet while being 1.6x faster than the former and 3.1x faster than the latter.	['Yara Ali Alnaggar', 'Mohamed ElHelw', 'Karim Amer', 'Mohamed Afifi']	2020-11-03	multi-projection-fusion-for-real-time-1	https://openaccess.thecvf.com/content/WACV2021/html/Alnaggar_Multi_Projection_Fusion_for_Real-Time_Semantic_Segmentation_of_3D_LiDAR_WACV_2021_paper.html	https://openaccess.thecvf.com/content/WACV2021/papers/Alnaggar_Multi_Projection_Fusion_for_Real-Time_Semantic_Segmentation_of_3D_LiDAR_WACV_2021_paper.pdf	null	['lidar-semantic-segmentation']	['computer-vision']	[-4.99738473e-03 -5.15466966e-02 3.63683611e-01 -5.34085095e-01 -4.84083176e-01 -5.23471534e-01 6.18933439e-01 -7.91986883e-02 -8.30399096e-01 1.14649966e-01 -6.38829947e-01 -6.29670739e-01 7.54434317e-02 -1.00950897e+00 -9.13198531e-01 -2.99215049e-01 2.35809907e-01 1.01112151e+00 7.87571132e-01 -4.74447370e-01 2.65705019e-01 1.03205061e+00 -2.02209115e+00 6.48712590e-02 7.64784336e-01 1.27431238e+00 3.24991494e-01 7.03271925e-01 -3.85061055e-01 -2.72393614e-01 -1.86586991e-01 -2.99739450e-01 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20cbf827-3a13-49d9-8d7e-6e1f83bcbd13	gradskip-communication-accelerated-local	2210.16402	null	https://arxiv.org/abs/2210.16402v2	https://arxiv.org/pdf/2210.16402v2.pdf	GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity	We study a class of distributed optimization algorithms that aim to alleviate high communication costs by allowing the clients to perform multiple local gradient-type training steps prior to communication. While methods of this type have been studied for about a decade, the empirically observed acceleration properties of local training eluded all attempts at theoretical understanding. In a recent breakthrough, Mishchenko et al. (ICML 2022) proved that local training, when properly executed, leads to provable communication acceleration, and this holds in the strongly convex regime without relying on any data similarity assumptions. However, their method ProxSkip requires all clients to take the same number of local training steps in each communication round. Inspired by a common sense intuition, we start our investigation by conjecturing that clients with ``less important'' data should be able to get away with fewer local training steps without this impacting the overall communication complexity of the method. It turns out that this intuition is correct: we managed to redesign the original ProxSkip method to achieve this. In particular, we prove that our modified method, for which coin the name GradSkip, converges linearly under the same assumptions, has the same accelerated communication complexity, while the number of local gradient steps can be reduced relative to a local condition number. We further generalize our method by extending the randomness of probabilistic alternations to arbitrary unbiased compression operators and considering a generic proximable regularizer. This generalization, which we call GradSkip+, recovers several related methods in the literature as special cases. Finally, we present an empirical study on carefully designed toy problems that confirm our theoretical claims.	['Peter Richtárik', 'Mher Safaryan', 'Artavazd Maranjyan']	2022-10-28	null	null	null	null	['distributed-optimization', 'common-sense-reasoning']	['methodology', 'reasoning']	[ 2.08132818e-01 2.51215070e-01 -2.27541625e-01 -2.14428291e-01 -1.09329557e+00 -7.66141295e-01 3.74029160e-01 3.73793125e-01 -7.71106064e-01 8.85670006e-01 2.41057828e-01 -4.29870337e-01 -5.06989002e-01 -9.64603007e-01 -1.14509273e+00 -1.17934704e+00 -2.32657284e-01 6.95239663e-01 6.18137084e-02 -1.50668100e-02 4.51984674e-01 4.02860433e-01 -1.12050211e+00 -5.78507334e-02 6.22674584e-01 8.61476660e-01 -2.67920271e-03 8.67334723e-01 1.78790182e-01 5.99652588e-01 -5.71216822e-01 -5.24852931e-01 5.43146551e-01 -4.82320607e-01 -1.22657979e+00 -4.41632187e-03 1.04378663e-01 -3.25061351e-01 -2.34287366e-01 1.19357455e+00 7.01931059e-01 1.90892056e-01 2.92234182e-01 -1.01990294e+00 -2.69378483e-01 1.26843357e+00 -5.26239693e-01 1.76863801e-02 1.62220508e-01 -2.21054554e-01 1.35363483e+00 -4.62519825e-01 5.04478216e-01 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85019e63-870e-4166-9e17-b29f392d0e0e	a-computational-efficient-pumped-storage	2304.03821	null	https://arxiv.org/abs/2304.03821v1	https://arxiv.org/pdf/2304.03821v1.pdf	A Computational Efficient Pumped Storage Hydro Optimization in the Look-ahead Unit Commitment and Real-time Market Dispatch Under Uncertainty	Pumped storage hydro units (PSHU) are great sources of flexibility in power systems. This is especially valuable in modern systems with increasing shares of intermittent renewable resources. However, the flexibility from PSHUs, particularly in the real-time market, has not been thoroughly studied. The storage optimization in a real-time market hasn't been well addressed. To enhance the use of PSH resources and leverage their flexibility, it is important to incorporate the uncertainties, properly address the risks and avoid increasing too much computational burdens in the real-time market operation. To provide a practical solution to the daily operation of a PSHU in a single day look-ahead commitment (LAC) and real-time market, this paper proposes two pumped storage hydro (PSH) models that only use probabilistic price forecast to incorporate uncertainties and manage risks in the LAC and real-time market operation. The price forecast scenarios are formulated only on PSHUs that minimizes the computational challenges to the Security Constrained Unit Commitment (SCUC) problem. Numerical studies in Mid-continent Independent System Operator (MISO) demonstrate that the proposed models improves market efficiency. Compared to traditional stochastic and robust unit commitment, the proposed methods only moderately increase the solving time from current practice of deterministic LAC. Probabilistic forecast for Real Time Locational Marginal Price (RT-LMP) on PSH locations is created and embedded into the proposed stochastic optimization model, an statistical robust approach is used to generate scenarios for reflecting the temporal inter-dependence of the LMP forecast uncertainties.	['Ross Baldick', 'Yonghong Chen', 'Arezou Ghesmati', 'Bing Huang']	2023-04-07	null	null	null	null	['stochastic-optimization']	['methodology']	[-6.63867474e-01 -1.21201307e-01 1.02386646e-01 2.65853822e-01 -3.80835444e-01 -8.65210235e-01 4.71229136e-01 1.23868920e-01 1.84297532e-01 1.39081001e+00 -8.90443400e-02 -5.34597576e-01 -5.20754993e-01 -1.23206007e+00 -3.57227772e-01 -1.04038584e+00 -4.17163074e-01 3.92674387e-01 -1.07299484e-01 -5.21400750e-01 1.86186731e-01 4.70100433e-01 -1.29430294e+00 -4.52940702e-01 1.22697866e+00 1.14897633e+00 5.31048894e-01 -2.41471231e-02 2.21170753e-01 4.22496647e-02 -5.83708882e-01 2.37217456e-01 6.67708397e-01 -1.63647115e-01 8.28389376e-02 -2.89533347e-01 -1.18086648e+00 -4.21244472e-01 2.38033861e-01 1.20849788e+00 7.14116096e-01 6.14384651e-01 4.96123612e-01 -1.32876480e+00 -5.11887729e-01 1.03540277e+00 -6.43071711e-01 4.63103414e-01 -7.76846558e-02 8.76659080e-02 6.57792985e-01 -8.71681809e-01 3.21598724e-02 1.03864336e+00 2.07790524e-01 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5501e785-dd48-45a6-8521-d2a3d89989ed	forensicability-assessment-of-questioned	2209.01935	null	https://arxiv.org/abs/2209.01935v1	https://arxiv.org/pdf/2209.01935v1.pdf	Forensicability Assessment of Questioned Images in Recapturing Detection	Recapture detection of face and document images is an important forensic task. With deep learning, the performances of face anti-spoofing (FAS) and recaptured document detection have been improved significantly. However, the performances are not yet satisfactory on samples with weak forensic cues. The amount of forensic cues can be quantified to allow a reliable forensic result. In this work, we propose a forensicability assessment network to quantify the forensicability of the questioned samples. The low-forensicability samples are rejected before the actual recapturing detection process to improve the efficiency of recapturing detection systems. We first extract forensicability features related to both image quality assessment and forensic tasks. By exploiting domain knowledge of the forensic application in image quality and forensic features, we define three task-specific forensicability classes and the initialized locations in the feature space. Based on the extracted features and the defined centers, we train the proposed forensic assessment network (FANet) with cross-entropy loss and update the centers with a momentum-based update method. We integrate the trained FANet with practical recapturing detection schemes in face anti-spoofing and recaptured document detection tasks. Experimental results show that, for a generic CNN-based FAS scheme, FANet reduces the EERs from 33.75% to 19.23% under ROSE to IDIAP protocol by rejecting samples with the lowest 30% forensicability scores. The performance of FAS schemes is poor in the rejected samples, with EER as high as 56.48%. Similar performances in rejecting low-forensicability samples have been observed for the state-of-the-art approaches in FAS and recaptured document detection tasks. To the best of our knowledge, this is the first work that assesses the forensicability of recaptured document images and improves the system efficiency.	['Alex C. Kot', 'Jiwu Huang', 'Zitong Yu', 'Rizhao Cai', 'Lin Zhao', 'Changsheng chen']	2022-09-05	null	null	null	null	['face-anti-spoofing']	['computer-vision']	[ 1.21606894e-01 -3.21362644e-01 1.36330768e-01 -1.15500286e-01 -7.27419615e-01 -4.65732425e-01 5.19153416e-01 1.56990588e-02 -4.98022825e-01 4.68223393e-01 -3.24673295e-01 8.76095146e-02 -2.92857975e-01 -6.12309158e-01 -4.10794586e-01 -9.21909988e-01 -1.14399649e-01 3.07736725e-01 4.06469144e-02 9.84539613e-02 3.44974667e-01 1.01848257e+00 -1.64805984e+00 7.04595447e-01 8.11948001e-01 1.09566629e+00 -1.40574977e-01 5.02678812e-01 -6.77617192e-02 6.07438087e-01 -1.00824630e+00 -9.35137093e-01 3.02292347e-01 -2.56873548e-01 -2.21919984e-01 -1.98201135e-01 7.75882006e-01 -6.57385707e-01 -4.21227902e-01 1.28347218e+00 7.45304167e-01 -1.27629519e-01 6.98663056e-01 -1.31184185e+00 -7.65446842e-01 5.40049195e-01 -8.72845948e-01 4.16938007e-01 1.21287949e-01 2.98508644e-01 4.46730852e-01 -1.07339978e+00 4.95040506e-01 1.39880693e+00 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21b27a05-42b6-428a-b09e-ea4a8ac4706e	estimation-of-mitral-valve-hinge-point	2301.08782	null	https://arxiv.org/abs/2301.08782v1	https://arxiv.org/pdf/2301.08782v1.pdf	Estimation of mitral valve hinge point coordinates -- deep neural net for echocardiogram segmentation	Cardiac image segmentation is a powerful tool in regard to diagnostics and treatment of cardiovascular diseases. Purely feature-based detection of anatomical structures like the mitral valve is a laborious task due to specifically required feature engineering and is especially challenging in echocardiograms, because of their inherently low contrast and blurry boundaries between some anatomical structures. With the publication of further annotated medical datasets and the increase in GPU processing power, deep learning-based methods in medical image segmentation became more feasible in the past years. We propose a fully automatic detection method for mitral valve hinge points, which uses a U-Net based deep neural net to segment cardiac chambers in echocardiograms in a first step, and subsequently extracts the mitral valve hinge points from the resulting segmentations in a second step. Results measured with this automatic detection method were compared to reference coordinate values, which with median absolute hinge point coordinate errors of 1.35 mm for the x- (15-85 percentile range: [0.3 mm; 3.15 mm]) and 0.75 mm for the y- coordinate (15-85 percentile range: [0.15 mm; 1.88 mm]).	['Heinrich Martin Overhoff', 'Christian Schmidt']	2023-01-20	null	null	null	null	['feature-engineering']	['methodology']	[ 5.63201234e-02 2.55149752e-01 3.21041167e-01 -3.53311300e-02 -5.24381757e-01 -7.89634287e-01 6.53841812e-03 4.57445174e-01 -4.86335576e-01 4.96711493e-01 -2.16487572e-01 -4.65789199e-01 -2.22357973e-01 -5.28640330e-01 -2.08905399e-01 -5.49569488e-01 -4.34084743e-01 6.43356502e-01 2.84184247e-01 1.76104888e-01 2.06011415e-01 7.99599946e-01 -8.06555986e-01 -3.12985808e-01 9.05210555e-01 1.05082297e+00 -5.70300668e-02 9.86747801e-01 9.10088196e-02 -5.41215055e-02 -7.14846015e-01 -4.10455838e-02 4.50704277e-01 -6.60460353e-01 -4.98830885e-01 2.97720224e-01 4.39146906e-01 -4.30618078e-01 1.12890095e-01 9.72931564e-01 7.13385284e-01 -1.38614804e-01 7.20815659e-01 -5.12995660e-01 -1.92260444e-01 2.16308549e-01 -5.41374922e-01 2.45138586e-01 -2.39669368e-01 -8.28213841e-02 6.47587657e-01 -7.94902802e-01 7.73659647e-01 4.56387341e-01 9.43406463e-01 4.27074164e-01 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7cdf21f7-6382-41a3-be8a-21529ca16383	a-capsule-network-for-hierarchical-multi	2209.05723	null	https://arxiv.org/abs/2209.05723v1	https://arxiv.org/pdf/2209.05723v1.pdf	A Capsule Network for Hierarchical Multi-Label Image Classification	Image classification is one of the most important areas in computer vision. Hierarchical multi-label classification applies when a multi-class image classification problem is arranged into smaller ones based upon a hierarchy or taxonomy. Thus, hierarchical classification modes generally provide multiple class predictions on each instance, whereby these are expected to reflect the structure of image classes as related to one another. In this paper, we propose a multi-label capsule network (ML-CapsNet) for hierarchical classification. Our ML-CapsNet predicts multiple image classes based on a hierarchical class-label tree structure. To this end, we present a loss function that takes into account the multi-label predictions of the network. As a result, the training approach for our ML-CapsNet uses a coarse to fine paradigm while maintaining consistency with the structure in the classification levels in the label-hierarchy. We also perform experiments using widely available datasets and compare the model with alternatives elsewhere in the literature. In our experiments, our ML-CapsNet yields a margin of improvement with respect to these alternative methods.	['Brano Kusy', 'Antonio Robles-Kelly', 'Khondaker Tasrif Noor']	2022-09-13	null	null	null	null	['multi-label-image-classification']	['computer-vision']	[ 4.62701559e-01 2.24052742e-01 -4.00116116e-01 -6.40149236e-01 -5.20323992e-01 -4.54789817e-01 5.41226983e-01 4.24465060e-01 -2.92384356e-01 4.18975025e-01 -2.58773416e-02 -3.61193158e-02 -1.76266551e-01 -7.05134392e-01 -3.43926638e-01 -6.45785987e-01 2.40906447e-01 4.98038232e-01 4.65742648e-01 2.89431363e-01 2.85516441e-01 4.53256667e-01 -1.98309255e+00 9.56968606e-01 5.05033970e-01 1.42248869e+00 9.78721082e-02 4.36973274e-01 9.50305611e-02 1.17407584e+00 -3.33558679e-01 -4.99888390e-01 1.37432501e-01 -1.92852795e-01 -1.15270090e+00 3.81349832e-01 1.02825379e+00 7.90966377e-02 3.56794149e-01 1.13839829e+00 -5.25985882e-02 -2.73453027e-01 1.12489271e+00 -1.44353294e+00 -2.75723308e-01 3.27498257e-01 -5.97968698e-01 -1.36736423e-01 -2.98139080e-02 -4.88057017e-01 1.48918593e+00 -7.80090988e-01 5.07799029e-01 1.42598426e+00 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8c335215-f568-4f52-aad3-cad45e5b492b	identification-of-substation-configurations	2207.05603	null	https://arxiv.org/abs/2207.05603v2	https://arxiv.org/pdf/2207.05603v2.pdf	Identification of Substation Configurations in Modern Power Systems using Artificial Intelligence	Power system transmission network topology is utilized in energy management system applications. Substation configurations are fundamental to transmission network topology processing. Modern power systems consisting of renewable energy sources require reliable and fast network topology processing due to the variable nature of wind and solar power plants. Currently used transmission network topology processing, which is based on the relay signals communicated through SCADA is not highly reliable or highly accurate. Substation configuration identification (SCI) for different substation arrangements including main and transfer bus arrangement (MTBA), ring bus arrangement (RBA), and single bus arrangement (SBA) is investigated. Synchrophasor measurement based SCI for functional arrangements (FA) using artificial intelligence (AI) approaches is proposed in this paper. This method improves monitoring FA. Typical results for MTBA, RBA and SBA substation configuration identification is presented. A modified two-area four-machine power system model with two grid connected solar PV plants consisting of MTBA, RBA and SBA is simulated on real-time digital simulator. AI based SCI is shown to accurately identify all possible FAs for the three substation arrangements under any operating condition.	['Ganesh K. Venayagamoorthy', 'Dulip Madurasinghe']	2022-07-12	null	null	null	null	['energy-management']	['time-series']	[-2.00929940e-01 -6.97606921e-01 2.01545849e-01 1.20002970e-01 2.64979661e-01 -1.22345746e+00 5.76144934e-01 4.76031780e-01 4.96997893e-01 1.20487106e+00 -4.83757108e-01 -4.71987784e-01 -7.80479312e-01 -9.02395427e-01 1.92312956e-01 -8.50002646e-01 -2.41875917e-01 5.27651787e-01 -2.30060980e-01 -4.73354429e-01 1.67037025e-01 1.05051494e+00 -1.18175006e+00 -2.86157101e-01 1.06485212e+00 7.84042120e-01 1.14980958e-01 7.39720285e-01 2.64715165e-01 3.04448277e-01 -1.45251274e+00 5.43064892e-01 3.70392948e-01 -5.52467406e-01 -4.78410870e-01 -9.26038250e-02 -6.62990391e-01 -2.28847727e-01 1.44297600e-01 1.32518411e+00 5.35972416e-01 1.24233700e-01 9.99848187e-01 -2.08749533e+00 -8.89372006e-02 9.55461383e-01 -9.58778322e-01 7.38995910e-01 4.18499082e-01 5.66995107e-02 7.32849836e-01 -3.40675175e-01 6.37091622e-02 7.31830120e-01 4.72776920e-01 -5.78965724e-01 -1.04868996e+00 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-4.92891446e-02 1.37237504e-01 -5.02943695e-01 -6.38168395e-01 1.86015107e-02]	[5.787928104400635, 2.5691099166870117]
b356c617-1e61-4f9b-9897-9fbc7ccfeca8	modern-talking-in-key-point-analysis-key	null	null	https://aclanthology.org/2021.argmining-1.18	https://aclanthology.org/2021.argmining-1.18.pdf	Modern Talking in Key Point Analysis: Key Point Matching using Pretrained Encoders	We contribute to the ArgMining 2021 shared task on Quantitative Summarization and Key Point Analysis with two approaches for argument key point matching. For key point matching the task is to decide if a short key point matches the content of an argument with the same topic and stance towards the topic. We approach this task in two ways: First, we develop a simple rule-based baseline matcher by computing token overlap after removing stop words, stemming, and adding synonyms/antonyms. Second, we fine-tune pretrained BERT and RoBERTalanguage models as aregression classifier for only a single epoch. We manually examine errors of our proposed matcher models and find that long arguments are harder to classify. Our fine-tuned RoBERTa-Base model achieves a mean average precision score of 0.913, the best score for strict labels of all participating teams.	['Yamen Ajjour', 'Max Henze', 'Thi Kim Hanh Luu', 'Jan Heinrich Reimer']	null	null	null	null	emnlp-argmining-2021-11	['key-point-matching']	['natural-language-processing']	[ 2.00828552e-01 4.31355387e-01 -7.83870935e-01 -2.30928779e-01 -1.69538653e+00 -1.07655156e+00 1.18004644e+00 1.19347858e+00 -7.25809693e-01 6.52530849e-01 8.91334414e-01 -3.79902512e-01 -2.44328573e-01 -4.94617045e-01 -7.69325316e-01 -1.59435809e-01 2.75564730e-01 6.00898623e-01 3.43215108e-01 -3.51812184e-01 9.01688397e-01 4.55022193e-02 -8.41562986e-01 9.60696757e-01 9.80382502e-01 8.25488389e-01 -4.51150149e-01 7.93784261e-01 -1.86005831e-01 7.14972794e-01 -1.01684177e+00 -1.09656942e+00 2.94216629e-02 -1.59967005e-01 -1.37874687e+00 -5.40232062e-01 8.64840865e-01 1.60788551e-01 -4.36360016e-02 7.76664853e-01 4.50986028e-01 -8.31026435e-02 8.83318007e-01 -8.95875573e-01 -3.12270850e-01 1.38825178e+00 -6.19691670e-01 5.52860260e-01 5.17307520e-01 -2.77618915e-01 1.69574654e+00 -7.82614112e-01 7.30818927e-01 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e72ed504-2763-4761-9776-6bcb3ed50d66	large-language-models-as-annotators-enhancing	2306.15766	null	https://arxiv.org/abs/2306.15766v1	https://arxiv.org/pdf/2306.15766v1.pdf	Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Cost	State-of-the-art supervised NLP models achieve high accuracy but are also susceptible to failures on inputs from low-data regimes, such as domains that are not represented in training data. As an approximation to collecting ground-truth labels for the specific domain, we study the use of large language models (LLMs) for annotating inputs and improving the generalization of NLP models. Specifically, given a budget for LLM annotations, we present an algorithm for sampling the most informative inputs to annotate and retrain the NLP model. We find that popular active learning strategies such as uncertainty-based sampling do not work well. Instead, we propose a sampling strategy based on the difference in prediction scores between the base model and the finetuned NLP model, utilizing the fact that most NLP models are finetuned from a base model. Experiments with classification (semantic similarity) and ranking (semantic search) tasks show that our sampling strategy leads to significant gains in accuracy for both the training and target domains.	['Amit Sharma', 'Parikshit Bansal']	2023-06-27	null	null	null	null	['active-learning', 'active-learning', 'semantic-textual-similarity', 'semantic-similarity']	['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[ 3.64971280e-01 7.30083287e-01 -1.00908530e+00 -6.80281997e-01 -1.27479672e+00 -8.00199628e-01 6.39208019e-01 5.01024246e-01 -5.88163674e-01 1.01333666e+00 9.16927308e-02 -1.72448754e-01 -3.89584392e-01 -6.66689098e-01 -1.06960809e+00 -2.78704911e-01 3.33937347e-01 1.33301222e+00 5.24229705e-01 1.98496029e-01 2.03014195e-01 2.96481282e-01 -1.43955684e+00 6.26353383e-01 1.20131981e+00 9.98984158e-01 6.06631525e-02 2.96902120e-01 -5.99775434e-01 7.62904048e-01 -5.42373300e-01 -4.73149210e-01 1.29373565e-01 -1.98412240e-01 -1.14061606e+00 -2.09417626e-01 2.44030938e-01 -6.60801455e-02 1.95848525e-01 8.48450243e-01 7.61358961e-02 4.17553872e-01 9.71596718e-01 -1.43040276e+00 -6.09587908e-01 9.03426826e-01 4.08019265e-03 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36edad20-afd4-49d2-9de7-486f3c29ccd8	dinasti-dialogues-with-a-negotiating	null	null	https://aclanthology.org/L14-1466	https://aclanthology.org/L14-1466.pdf	DINASTI: Dialogues with a Negotiating Appointment Setting Interface	This paper describes the DINASTI (DIalogues with a Negotiating Appointment SeTting Interface) corpus, which is composed of 1734 dialogues with the French spoken dialogue system NASTIA (Negotiating Appointment SeTting InterfAce). NASTIA is a reinforcement learning-based system. The DINASTI corpus was collected while the system was following a uniform policy. Each entry of the corpus is a system-user exchange annotated with 120 automatically computable features.The corpus contains a total of 21587 entries, with 385 testers. Each tester performed at most five scenario-based interactions with NASTIA. The dialogues last an average of 10.82 dialogue turns, with 4.45 reinforcement learning decisions. The testers filled an evaluation questionnaire after each dialogue. The questionnaire includes three questions to measure task completion. In addition, it comprises 7 Likert-scaled items evaluating several aspects of the interaction, a numerical overall evaluation on a scale of 1 to 10, and a free text entry. Answers to this questionnaire are provided with DINASTI. This corpus is meant for research on reinforcement learning modelling for dialogue management.	['Romain Laroche', 'Layla El Asri', 'Olivier Pietquin']	2014-05-01	null	null	null	lrec-2014-5	['dialogue-management']	['natural-language-processing']	[-1.33115515e-01 8.14872205e-01 1.68330908e-01 -8.03213298e-01 -9.55154061e-01 -9.04589951e-01 6.02514863e-01 3.56906861e-01 -6.31029963e-01 1.21686161e+00 4.46326107e-01 -4.28392500e-01 -2.44983226e-01 -4.31437731e-01 2.43327003e-02 -1.92341194e-01 5.45315556e-02 1.21147346e+00 -1.83970481e-02 -9.44013655e-01 6.31954730e-01 8.29600617e-02 -9.62007225e-01 4.56244558e-01 8.64445150e-01 5.82610130e-01 2.80116886e-01 1.22923958e+00 -2.43816286e-01 9.61650789e-01 -1.32381046e+00 -3.39444041e-01 -2.77631193e-01 -5.92914760e-01 -1.45975149e+00 1.65805638e-01 -7.21198842e-02 -4.86939669e-01 -9.61133614e-02 4.16687638e-01 6.90418303e-01 4.91634846e-01 5.37384570e-01 -1.30944324e+00 1.14945762e-01 5.99896908e-01 2.94417262e-01 1.30396366e-01 1.24704170e+00 2.24322647e-01 9.61950839e-01 -5.53981006e-01 7.38656938e-01 1.54763627e+00 3.17769140e-01 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08caf8aa-401c-4450-9f61-95e45419d185	fusevis-interpreting-neural-networks-for	2012.08932	null	https://arxiv.org/abs/2012.08932v1	https://arxiv.org/pdf/2012.08932v1.pdf	FuseVis: Interpreting neural networks for image fusion using per-pixel saliency visualization	Image fusion helps in merging two or more images to construct a more informative single fused image. Recently, unsupervised learning based convolutional neural networks (CNN) have been utilized for different types of image fusion tasks such as medical image fusion, infrared-visible image fusion for autonomous driving as well as multi-focus and multi-exposure image fusion for satellite imagery. However, it is challenging to analyze the reliability of these CNNs for the image fusion tasks since no groundtruth is available. This led to the use of a wide variety of model architectures and optimization functions yielding quite different fusion results. Additionally, due to the highly opaque nature of such neural networks, it is difficult to explain the internal mechanics behind its fusion results. To overcome these challenges, we present a novel real-time visualization tool, named FuseVis, with which the end-user can compute per-pixel saliency maps that examine the influence of the input image pixels on each pixel of the fused image. We trained several image fusion based CNNs on medical image pairs and then using our FuseVis tool, we performed case studies on a specific clinical application by interpreting the saliency maps from each of the fusion methods. We specifically visualized the relative influence of each input image on the predictions of the fused image and showed that some of the evaluated image fusion methods are better suited for the specific clinical application. To the best of our knowledge, currently, there is no approach for visual analysis of neural networks for image fusion. Therefore, this work opens up a new research direction to improve the interpretability of deep fusion networks. The FuseVis tool can also be adapted in other deep neural network based image processing applications to make them interpretable.	['Stefan Gumhold', 'Nishant Kumar']	2020-12-06	null	null	null	null	['multi-exposure-image-fusion']	['computer-vision']	[ 4.46012765e-01 3.09462249e-01 4.03326541e-01 -3.18087846e-01 -3.51697147e-01 -2.15762779e-01 3.45373869e-01 4.03534621e-01 -2.99825847e-01 6.06082380e-01 -5.67986406e-02 -5.41759133e-01 -1.31430417e-01 -6.80700839e-01 -6.58806205e-01 -7.75432229e-01 1.32707611e-01 7.85934925e-02 2.34198704e-01 -4.11476851e-01 3.90170305e-03 6.66368008e-01 -1.89370251e+00 5.00328839e-01 1.02404690e+00 8.78946960e-01 6.64076090e-01 7.34810770e-01 -1.13702074e-01 4.35199648e-01 -8.37635338e-01 -1.50902748e-01 8.05017054e-02 -4.44183618e-01 -6.39872789e-01 1.56886186e-02 3.72627586e-01 -2.12622024e-02 -1.34424435e-03 1.21813071e+00 5.07763445e-01 4.18969132e-02 4.59535778e-01 -1.25479627e+00 -4.63872105e-01 4.76056516e-01 -4.66047287e-01 5.03813148e-01 2.77900428e-01 2.28032023e-01 2.67486870e-01 -5.22768676e-01 6.62965119e-01 1.10935915e+00 4.11145538e-01 1.74265936e-01 -1.02785802e+00 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a790e311-4552-46ae-90f5-4c2d35a71b35	synthetic-data-generation-for-a-longitudinal	2305.07685	null	https://arxiv.org/abs/2305.07685v1	https://arxiv.org/pdf/2305.07685v1.pdf	Synthetic data generation for a longitudinal cohort study -- Evaluation, method extension and reproduction of published data analysis results	Access to individual-level health data is essential for gaining new insights and advancing science. In particular, modern methods based on artificial intelligence rely on the availability of and access to large datasets. In the health sector, access to individual-level data is often challenging due to privacy concerns. A promising alternative is the generation of fully synthetic data, i.e. data generated through a randomised process that have similar statistical properties as the original data, but do not have a one-to-one correspondence with the original individual-level records. In this study, we use a state-of-the-art synthetic data generation method and perform in-depth quality analyses of the generated data for a specific use case in the field of nutrition. We demonstrate the need for careful analyses of synthetic data that go beyond descriptive statistics and provide valuable insights into how to realise the full potential of synthetic datasets. By extending the methods, but also by thoroughly analysing the effects of sampling from a trained model, we are able to largely reproduce significant real-world analysis results in the chosen use case.	['Juliane Fluck', 'Holger Fröhlich', 'Ute Nöthlings', 'Fabian Prasser', 'Tim Adams', 'Ines Perrar', 'Julian Schneider', 'Lisa Kühnel']	2023-05-12	null	null	null	null	['synthetic-data-generation', 'synthetic-data-generation']	['medical', 'miscellaneous']	[ 5.68574429e-01 5.62194288e-01 -8.32519680e-02 -3.45668793e-01 -9.30544138e-01 -4.30658281e-01 5.17673910e-01 1.00649786e+00 -4.95876253e-01 9.68096495e-01 4.98268187e-01 -3.08727652e-01 -4.38833654e-01 -1.14468944e+00 -8.68601203e-01 -4.42411929e-01 -2.38594916e-02 6.71343744e-01 -3.32029581e-01 -4.09916602e-02 -1.17585294e-01 3.36644560e-01 -1.69178104e+00 5.05389035e-01 9.33881402e-01 5.73145926e-01 -1.68391332e-01 3.69280607e-01 9.22318399e-02 2.32674375e-01 -5.59951305e-01 -5.21848977e-01 3.77309352e-01 -4.99133140e-01 -5.45824707e-01 -2.36789603e-02 3.10180476e-03 -2.38315552e-01 3.26578945e-01 7.86251187e-01 5.70861518e-01 -1.18861884e-01 5.07584810e-01 -1.14075053e+00 -4.20200557e-01 4.47059989e-01 -9.20813531e-02 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ab978b24-7246-4460-880c-676fd2f89c65	target-confusion-in-end-to-end-speaker	2204.01355	null	https://arxiv.org/abs/2204.01355v1	https://arxiv.org/pdf/2204.01355v1.pdf	Target Confusion in End-to-end Speaker Extraction: Analysis and Approaches	Recently, end-to-end speaker extraction has attracted increasing attention and shown promising results. However, its performance is often inferior to that of a blind source separation (BSS) counterpart with a similar network architecture, due to the auxiliary speaker encoder may sometimes generate ambiguous speaker embeddings. Such ambiguous guidance information may confuse the separation network and hence lead to wrong extraction results, which deteriorates the overall performance. We refer to this as the target confusion problem. In this paper, we conduct an analysis of such an issue and solve it in two stages. In the training phase, we propose to integrate metric learning methods to improve the distinguishability of embeddings produced by the speaker encoder. While for inference, a novel post-filtering strategy is designed to revise the wrong results. Specifically, we first identify these confusion samples by measuring the similarities between output estimates and enrollment utterances, after which the true target sources are recovered by a subtraction operation. Experiments show that performance improvement of more than 1dB SI-SDRi can be brought, which validates the effectiveness of our methods and emphasizes the impact of the target confusion problem.	['Yuexian Zou', 'Haoran Zhang', 'Rongzhi Gu', 'Dongchao Yang', 'Zifeng Zhao']	2022-04-04	null	null	null	null	['speech-separation', 'speaker-separation']	['speech', 'speech']	[ 3.65701169e-01 -2.95106899e-02 1.66879967e-01 -3.25354069e-01 -9.47997451e-01 -4.51974839e-01 4.18012977e-01 -1.92161754e-01 -2.04403758e-01 5.97671211e-01 4.23335701e-01 -2.82721609e-01 -1.52415320e-01 -5.36397584e-02 -3.39507043e-01 -1.04963195e+00 2.15348333e-01 -2.06372254e-02 8.44101682e-02 1.20276242e-01 1.23699702e-01 1.88992694e-01 -1.48740399e+00 -3.13026570e-02 1.11227775e+00 9.52625096e-01 1.28759235e-01 2.46530056e-01 -3.17956269e-01 5.48838437e-01 -1.06364989e+00 -4.90382403e-01 1.05747089e-01 -6.18334413e-01 -2.60580778e-01 1.11801654e-01 1.36375397e-01 -4.54086691e-01 -1.94907352e-01 1.47216606e+00 8.82372677e-01 -1.73538715e-01 6.55891478e-01 -1.30336797e+00 -4.38623279e-01 7.80533791e-01 -5.83156645e-01 1.54484823e-01 1.22982010e-01 -2.33940110e-01 9.43518102e-01 -1.11704719e+00 -2.00487208e-02 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950b0a90-f11f-4fd5-8c2a-56955b90ce9b	improving-deep-embedded-clustering-via	null	null	https://aclanthology.org/2022.coling-1.195	https://aclanthology.org/2022.coling-1.195.pdf	Improving Deep Embedded Clustering via Learning Cluster-level Representations	Driven by recent advances in neural networks, various Deep Embedding Clustering (DEC) based short text clustering models are being developed. In these works, latent representation learning and text clustering are performed simultaneously. Although these methods are becoming increasingly popular, they use pure cluster-oriented objectives, which can produce meaningless representations. To alleviate this problem, several improvements have been developed to introduce additional learning objectives in the clustering process, such as models based on contrastive learning. However, existing efforts rely heavily on learning meaningful representations at the instance level. They have limited focus on learning global representations, which are necessary to capture the overall data structure at the cluster level. In this paper, we propose a novel DEC model, which we named the deep embedded clustering model with cluster-level representation learning (DECCRL) to jointly learn cluster and instance level representations. Here, we extend the embedded topic modelling approach to introduce reconstruction constraints to help learn cluster-level representations. Experimental results on real-world short text datasets demonstrate that our model produces meaningful clusters.	['Xian Yang', 'Yike Guo', 'Liang Bai', 'Shuai Niu', 'Yida Xu', 'Yunya Song', 'Zhihua Wang', 'Qing Yin']	null	null	null	null	coling-2022-10	['text-clustering', 'short-text-clustering']	['natural-language-processing', 'natural-language-processing']	[-1.68562889e-01 6.03708159e-03 -1.84246987e-01 -4.26401556e-01 -5.85425198e-01 -6.76366538e-02 7.12924957e-01 4.19860363e-01 -1.32764250e-01 -8.21772069e-02 4.23450708e-01 1.11599334e-01 -3.78076360e-02 -6.23865962e-01 -3.24566245e-01 -8.30784976e-01 2.73971111e-01 4.66625422e-01 -5.92011586e-02 2.57955492e-01 2.19459832e-01 -2.33283937e-01 -1.47276688e+00 3.25898200e-01 1.20280945e+00 4.68063563e-01 2.70579308e-01 2.80243725e-01 -5.65139711e-01 9.99699891e-01 -5.33181965e-01 -6.08586296e-02 -1.85787380e-01 -5.17076671e-01 -5.13562202e-01 3.85852635e-01 -2.29584888e-01 -1.73495859e-01 -4.46597964e-01 9.83443975e-01 3.93994451e-01 4.49370772e-01 8.63529682e-01 -1.39089036e+00 -8.23594809e-01 1.14405370e+00 -8.80278230e-01 -3.66103381e-01 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2dcffe9e-71b9-4411-84aa-d55549dff751	read-like-humans-autonomous-bidirectional-and	2103.06495	null	https://arxiv.org/abs/2103.06495v1	https://arxiv.org/pdf/2103.06495v1.pdf	Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition	Linguistic knowledge is of great benefit to scene text recognition. However, how to effectively model linguistic rules in end-to-end deep networks remains a research challenge. In this paper, we argue that the limited capacity of language models comes from: 1) implicitly language modeling; 2) unidirectional feature representation; and 3) language model with noise input. Correspondingly, we propose an autonomous, bidirectional and iterative ABINet for scene text recognition. Firstly, the autonomous suggests to block gradient flow between vision and language models to enforce explicitly language modeling. Secondly, a novel bidirectional cloze network (BCN) as the language model is proposed based on bidirectional feature representation. Thirdly, we propose an execution manner of iterative correction for language model which can effectively alleviate the impact of noise input. Additionally, based on the ensemble of iterative predictions, we propose a self-training method which can learn from unlabeled images effectively. Extensive experiments indicate that ABINet has superiority on low-quality images and achieves state-of-the-art results on several mainstream benchmarks. Besides, the ABINet trained with ensemble self-training shows promising improvement in realizing human-level recognition. Code is available at https://github.com/FangShancheng/ABINet.	['Yongdong Zhang', 'Zhendong Mao', 'Yuxin Wang', 'Hongtao Xie', 'Shancheng Fang']	2021-03-11	null	http://openaccess.thecvf.com//content/CVPR2021/html/Fang_Read_Like_Humans_Autonomous_Bidirectional_and_Iterative_Language_Modeling_for_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Fang_Read_Like_Humans_Autonomous_Bidirectional_and_Iterative_Language_Modeling_for_CVPR_2021_paper.pdf	cvpr-2021-1	['scene-text-recognition']	['computer-vision']	[ 6.28198907e-02 -4.74976867e-01 -3.70285004e-01 -5.37253380e-01 -2.37626866e-01 -1.10982344e-01 7.88682818e-01 -5.66359580e-01 -4.92668480e-01 3.65609616e-01 3.91799837e-01 -5.37305295e-01 2.59310097e-01 -7.42907643e-01 -8.15944612e-01 -5.03011823e-01 7.51190662e-01 1.35891706e-01 1.42961428e-01 -1.88498393e-01 2.35712633e-01 2.26245984e-01 -1.10850465e+00 6.35433137e-01 9.76908743e-01 9.62554693e-01 3.48636389e-01 5.86575925e-01 -6.14545047e-01 1.43942034e+00 -3.08210216e-02 -2.73698837e-01 8.51105750e-02 -5.08238375e-01 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e20af336-2dfd-4f7d-96b2-ccc27bd00098	multilingual-name-entity-recognition-and	2211.02415	null	https://arxiv.org/abs/2211.02415v1	https://arxiv.org/pdf/2211.02415v1.pdf	Multilingual Name Entity Recognition and Intent Classification Employing Deep Learning Architectures	Named Entity Recognition and Intent Classification are among the most important subfields of the field of Natural Language Processing. Recent research has lead to the development of faster, more sophisticated and efficient models to tackle the problems posed by those two tasks. In this work we explore the effectiveness of two separate families of Deep Learning networks for those tasks: Bidirectional Long Short-Term networks and Transformer-based networks. The models were trained and tested on the ATIS benchmark dataset for both English and Greek languages. The purpose of this paper is to present a comparative study of the two groups of networks for both languages and showcase the results of our experiments. The models, being the current state-of-the-art, yielded impressive results and achieved high performance.	['Konstantinos Ch. Chatzisavvas', 'George Sarigiannidis', 'Athena Vakali', 'Angelos Theofilatos', 'Antonia Paflioti', 'Sofia Rizou']	2022-11-04	null	null	null	null	['intent-classification']	['natural-language-processing']	[-2.39341781e-01 -1.44923985e-01 -2.29194969e-01 -6.13918602e-01 -5.88488162e-01 -4.14684981e-01 9.71898854e-01 5.06970137e-02 -1.01353025e+00 8.31531942e-01 5.69757700e-01 -6.73553884e-01 -2.87146345e-02 -7.27556288e-01 -1.52799338e-01 -1.33747697e-01 -2.49231711e-01 5.72792232e-01 2.27035135e-01 -4.35496181e-01 1.70951128e-01 7.70356059e-01 -1.12873590e+00 5.78071654e-01 5.50216675e-01 1.08714390e+00 -2.34038487e-01 5.26362777e-01 -5.42274773e-01 1.15815997e+00 -3.91405791e-01 -7.15934813e-01 5.23630902e-02 8.94116610e-02 -1.23711526e+00 -4.56206769e-01 1.96329981e-01 -2.36954451e-01 -4.77071226e-01 5.35531700e-01 6.48090422e-01 1.82310835e-01 5.29624045e-01 -1.02717340e+00 -5.37991822e-01 7.09222674e-01 -1.65997088e-01 5.93003452e-01 3.17973256e-01 -3.63450348e-01 1.18985403e+00 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adbbc7b9-b546-48bd-ab95-f2eda19c4aaa	global-and-local-consistent-wavelet-domain	1809.07764	null	http://arxiv.org/abs/1809.07764v2	http://arxiv.org/pdf/1809.07764v2.pdf	Global and Local Consistent Wavelet-domain Age Synthesis	Age synthesis is a challenging task due to the complicated and non-linear transformation in human aging process. Aging information is usually reflected in local facial parts, such as wrinkles at the eye corners. However, these local facial parts contribute less in previous GAN based methods for age synthesis. To address this issue, we propose a Wavelet-domain Global and Local Consistent Age Generative Adversarial Network (WaveletGLCA-GAN), in which one global specific network and three local specific networks are integrated together to capture both global topology information and local texture details of human faces. Different from the most existing methods that modeling age synthesis in image-domain, we adopt wavelet transform to depict the textual information in frequency-domain. %Moreover, to achieve accurate age generation under the premise of preserving the identity information, age estimation network and face verification network are employed. Moreover, five types of losses are adopted: 1) adversarial loss aims to generate realistic wavelets; 2) identity preserving loss aims to better preserve identity information; 3) age preserving loss aims to enhance the accuracy of age synthesis; 4) pixel-wise loss aims to preserve the background information of the input face; 5) the total variation regularization aims to remove ghosting artifacts. Our method is evaluated on three face aging datasets, including CACD2000, Morph and FG-NET. Qualitative and quantitative experiments show the superiority of the proposed method over other state-of-the-arts.	['Pei-Pei Li', 'Yibo Hu', 'Zhenan Sun', 'Ran He']	2018-09-20	null	null	null	null	['human-aging']	['miscellaneous']	[ 7.26509243e-02 3.57231237e-02 1.32994667e-01 -2.27144897e-01 -2.19854176e-01 3.22382748e-02 2.70604193e-01 -5.01614451e-01 3.25607695e-02 9.67998803e-01 2.41345733e-01 4.01576906e-01 2.01200485e-01 -9.94209349e-01 -5.79286993e-01 -1.07779443e+00 5.66996820e-02 -1.06646508e-01 -2.46937916e-01 -2.30882898e-01 -8.77377465e-02 4.36155498e-01 -1.48657548e+00 -1.28651205e-02 1.12338293e+00 1.41109419e+00 -4.45862979e-01 1.53753683e-01 6.36959728e-03 6.85294151e-01 -5.86572528e-01 -6.37244105e-01 3.55428576e-01 -6.70951843e-01 -2.73361117e-01 1.32667854e-01 5.57341397e-01 -6.05452895e-01 -3.58518332e-01 1.17311287e+00 8.97047102e-01 -1.87984541e-01 7.77561605e-01 -1.56790161e+00 -7.78224826e-01 1.33353636e-01 -9.85794008e-01 -1.30011246e-01 6.56151548e-02 3.73542488e-01 4.23355430e-01 -5.91127813e-01 5.02836108e-01 1.62311780e+00 8.06778014e-01 7.43389308e-01 -9.25730586e-01 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5.19908011e-01 3.07419032e-01 -7.55246699e-01 -5.65754712e-01 3.85104448e-01]	[13.033914566040039, 0.29294002056121826]
e6bc2f77-77c3-4779-b20c-ba69d94d2e17	explainable-artificial-intelligence-for	2009.02098	null	https://arxiv.org/abs/2009.02098v2	https://arxiv.org/pdf/2009.02098v2.pdf	Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring	The contemporary process-aware information systems possess the capabilities to record the activities generated during the process execution. To leverage these process specific fine-granular data, process mining has recently emerged as a promising research discipline. As an important branch of process mining, predictive business process management, pursues the objective to generate forward-looking, predictive insights to shape business processes. In this study, we propose a conceptual framework sought to establish and promote understanding of decision-making environment, underlying business processes and nature of the user characteristics for developing explainable business process prediction solutions. Consequently, with regard to the theoretical and practical implications of the framework, this study proposes a novel local post-hoc explanation approach for a deep learning classifier that is expected to facilitate the domain experts in justifying the model decisions. In contrary to alternative popular perturbation-based local explanation approaches, this study defines the local regions from the validation dataset by using the intermediate latent space representations learned by the deep neural networks. To validate the applicability of the proposed explanation method, the real-life process log data delivered by the Volvo IT Belgium's incident management system are used.The adopted deep learning classifier achieves a good performance with the Area Under the ROC Curve of 0.94. The generated local explanations are also visualized and presented with relevant evaluation measures that are expected to increase the users' trust in the black-box-model.	['Nijat Mehdiyev', 'Peter Fettke']	2020-09-04	null	null	null	null	['predictive-process-monitoring']	['time-series']	[ 4.86044616e-01 6.44634247e-01 -2.12154817e-02 -1.51180074e-01 -2.55693253e-02 5.12719620e-03 9.31110322e-01 7.93859720e-01 4.52846894e-03 5.76972842e-01 3.43332916e-01 -4.67645615e-01 -1.06489635e+00 -7.80378044e-01 -3.04918647e-01 -7.45661438e-01 -2.49854326e-01 7.36417592e-01 -6.21548057e-01 1.31920755e-01 7.40070343e-01 6.48849189e-01 -1.70851207e+00 5.61484873e-01 8.63366187e-01 1.14656377e+00 -1.00789793e-01 4.02380884e-01 -3.71679336e-01 9.28817868e-01 -2.42209315e-01 -3.29437286e-01 2.48789877e-01 -2.10501924e-01 -5.37397504e-01 1.04077071e-01 -4.14381176e-01 4.45938818e-02 1.42247736e-01 7.64238656e-01 1.61624745e-01 2.71695852e-01 8.00505698e-01 -1.35830200e+00 -6.69526517e-01 6.08078122e-01 -2.06864044e-01 3.84306818e-01 3.40461850e-01 5.58361053e-01 9.75134492e-01 -8.21740746e-01 5.17208934e-01 1.02782631e+00 5.09505391e-01 3.05031389e-01 -1.09760249e+00 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4c19f561-bf02-4657-86b7-2401c91334c6	a-robust-and-efficient-video-representation	1504.05524	null	http://arxiv.org/abs/1504.05524v1	http://arxiv.org/pdf/1504.05524v1.pdf	A robust and efficient video representation for action recognition	This paper introduces a state-of-the-art video representation and applies it to efficient action recognition and detection. We first propose to improve the popular dense trajectory features by explicit camera motion estimation. More specifically, we extract feature point matches between frames using SURF descriptors and dense optical flow. The matches are used to estimate a homography with RANSAC. To improve the robustness of homography estimation, a human detector is employed to remove outlier matches from the human body as human motion is not constrained by the camera. Trajectories consistent with the homography are considered as due to camera motion, and thus removed. We also use the homography to cancel out camera motion from the optical flow. This results in significant improvement on motion-based HOF and MBH descriptors. We further explore the recent Fisher vector as an alternative feature encoding approach to the standard bag-of-words histogram, and consider different ways to include spatial layout information in these encodings. We present a large and varied set of evaluations, considering (i) classification of short basic actions on six datasets, (ii) localization of such actions in feature-length movies, and (iii) large-scale recognition of complex events. We find that our improved trajectory features significantly outperform previous dense trajectories, and that Fisher vectors are superior to bag-of-words encodings for video recognition tasks. In all three tasks, we show substantial improvements over the state-of-the-art results.	['Cordelia Schmid', 'Heng Wang', 'Jakob Verbeek', 'Dan Oneata']	2015-04-21	null	null	null	null	['homography-estimation']	['computer-vision']	[ 2.26183981e-01 -7.09889174e-01 -3.14095765e-01 -5.30112348e-02 -8.17554533e-01 -5.70466518e-01 6.60364687e-01 -1.88642275e-02 -4.98293638e-01 4.83688146e-01 6.65718138e-01 3.27410638e-01 -5.44671603e-02 -4.50819254e-01 -6.65969729e-01 -6.43294096e-01 -3.05320531e-01 1.98548228e-01 6.23451829e-01 -4.41880934e-02 6.31229460e-01 7.71435976e-01 -1.71343362e+00 3.98203045e-01 1.58203304e-01 9.67768073e-01 -2.40244791e-01 8.79710734e-01 3.09751719e-01 9.61784601e-01 -4.56914216e-01 -2.00492740e-01 3.39871854e-01 -5.13232827e-01 -6.67338312e-01 5.59186220e-01 1.07039392e+00 -7.69151092e-01 -8.96636128e-01 7.55275846e-01 2.77923077e-01 5.11554718e-01 6.08512342e-01 -1.22098505e+00 -3.58900458e-01 -1.92916736e-01 -6.03521645e-01 4.62666541e-01 1.09776652e+00 1.09727815e-01 9.38661337e-01 -1.04412591e+00 9.81512010e-01 1.28639984e+00 7.20584929e-01 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-6.67546570e-01 9.22388315e-01 4.61615056e-01 -1.14214075e+00 -5.29899716e-01 4.39166844e-01]	[8.115589141845703, 0.3066141605377197]
69a107ef-b706-447c-aba1-d10a8cd1e754	arcnn-a-semantic-enhanced-relation-detection	null	null	https://openreview.net/forum?id=_TLu-3ucBI	https://openreview.net/pdf?id=_TLu-3ucBI	ARCNN: A Semantic Enhanced Relation Detection Model for Knowledge Base Question Answering	Relation detection plays an important role in knowledge base question answering (KBQA), and it is critical for the final performance of KBQA systems. The previous works mainly focused on enriching the information representations of questions and relations, and neglected the interaction information of questions and relations and different tokens within the relation. In this paper, we propose a semantic enhanced relation detection model called ARCNN, which is carefully designed by combining BiGRU, multi-scale semantic extracted CNN, and different attention mechanisms in a seamless way. Moreover, we combine four levels of relation abstractions to ensure the integrity of relation information and hence to enrich the relation representation. The experimental results on two benchmarks show that our ARCNN model achieves new state-of-the-art accuracies of 96.42% for SimpleQuestions and 90.4% for WebQuestions. Moreover, it helps our KBQA system to yield the accuracy of 81.5% and the F1 score of 72.0% on two benchmarks, respectively.	['Anonymous']	2021-11-16	null	null	null	acl-arr-november-2021-11	['knowledge-base-question-answering']	['natural-language-processing']	[-3.36833775e-01 3.36173922e-01 -1.65572971e-01 -2.86200911e-01 -7.80703843e-01 -4.22617704e-01 3.60241383e-01 2.28315189e-01 -3.91066611e-01 7.19260871e-01 2.63630927e-01 -4.35203105e-01 -1.68504015e-01 -1.28302097e+00 -6.72829568e-01 -1.99705899e-01 4.48596776e-01 5.58280468e-01 9.69918728e-01 -8.28882933e-01 -2.48123392e-01 4.09784727e-02 -1.34171951e+00 5.90197027e-01 1.35840738e+00 1.39563620e+00 -2.61331946e-01 4.94207263e-01 -5.73406279e-01 1.28064585e+00 -7.00621367e-01 -1.06179690e+00 -3.49775255e-01 -2.50448674e-01 -1.34726191e+00 -5.88232577e-01 2.44705483e-01 -3.70700568e-01 -5.39128006e-01 1.03361130e+00 3.42380971e-01 1.65098593e-01 2.32139021e-01 -1.06808996e+00 -1.15979779e+00 9.14773643e-01 -1.55168593e-01 3.72832954e-01 6.24001980e-01 -1.13249354e-01 1.47519958e+00 -5.61538637e-01 3.52696717e-01 1.37174773e+00 3.57542664e-01 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f17790e5-5b80-4121-ac53-ee3becfe50e8	cose-co-text-conditioned-generative-1	2206.05706	null	https://arxiv.org/abs/2206.05706v2	https://arxiv.org/pdf/2206.05706v2.pdf	CoSe-Co: Text Conditioned Generative CommonSense Contextualizer	Pre-trained Language Models (PTLMs) have been shown to perform well on natural language tasks. Many prior works have leveraged structured commonsense present in the form of entities linked through labeled relations in Knowledge Graphs (KGs) to assist PTLMs. Retrieval approaches use KG as a separate static module which limits coverage since KGs contain finite knowledge. Generative methods train PTLMs on KG triples to improve the scale at which knowledge can be obtained. However, training on symbolic KG entities limits their applicability in tasks involving natural language text where they ignore overall context. To mitigate this, we propose a CommonSense Contextualizer (CoSe-Co) conditioned on sentences as input to make it generically usable in tasks for generating knowledge relevant to the overall context of input text. To train CoSe-Co, we propose a novel dataset comprising of sentence and commonsense knowledge pairs. The knowledge inferred by CoSe-Co is diverse and contain novel entities not present in the underlying KG. We augment generated knowledge in Multi-Choice QA and Open-ended CommonSense Reasoning tasks leading to improvements over current best methods on CSQA, ARC, QASC and OBQA datasets. We also demonstrate its applicability in improving performance of a baseline model for paraphrase generation task.	['Balaji Krishnamurthy', 'Jivat Neet Kaur', 'Sumit Bhatia', 'Milan Aggarwal', 'Rachit Bansal']	2022-06-12	cose-co-text-conditioned-generative	https://aclanthology.org/2022.naacl-main.83	https://aclanthology.org/2022.naacl-main.83.pdf	naacl-2022-7	['paraphrase-generation', 'paraphrase-generation']	['computer-code', 'natural-language-processing']	[ 2.76390731e-01 8.50263476e-01 -9.57444683e-02 -2.39312589e-01 -1.08371186e+00 -8.37775826e-01 9.49135423e-01 2.54313290e-01 -2.70136625e-01 1.18619418e+00 7.22282946e-01 -4.96355653e-01 -2.22278722e-02 -1.25322580e+00 -1.17237663e+00 -8.14241469e-02 4.95521337e-01 7.52776444e-01 2.23073006e-01 -7.67984927e-01 -1.48123223e-02 8.25586841e-02 -1.04269242e+00 7.75123954e-01 1.42719638e+00 4.19136733e-01 1.43154353e-01 3.60326409e-01 -3.59385669e-01 1.23524284e+00 -8.42517912e-01 -1.08224440e+00 1.72766764e-03 -6.46426082e-01 -1.29727173e+00 -4.22387004e-01 6.26149952e-01 2.95071900e-02 -3.39158535e-01 8.41259241e-01 5.40692806e-01 2.50125766e-01 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23fb64c9-d12a-496e-ab99-0ee01883a943	towards-generalized-and-distributed-privacy	2010.01792	null	https://arxiv.org/abs/2010.01792v5	https://arxiv.org/pdf/2010.01792v5.pdf	Can we Generalize and Distribute Private Representation Learning?	We study the problem of learning representations that are private yet informative, i.e., provide information about intended "ally" targets while hiding sensitive "adversary" attributes. We propose Exclusion-Inclusion Generative Adversarial Network (EIGAN), a generalized private representation learning (PRL) architecture that accounts for multiple ally and adversary attributes unlike existing PRL solutions. While centrally-aggregated dataset is a prerequisite for most PRL techniques, data in real-world is often siloed across multiple distributed nodes unwilling to share the raw data because of privacy concerns. We address this practical constraint by developing D-EIGAN, the first distributed PRL method that learns representations at each node without transmitting the source data. We theoretically analyze the behavior of adversaries under the optimal EIGAN and D-EIGAN encoders and the impact of dependencies among ally and adversary tasks on the optimization objective. Our experiments on various datasets demonstrate the advantages of EIGAN in terms of performance, robustness, and scalability. In particular, EIGAN outperforms the previous state-of-the-art by a significant accuracy margin (47% improvement), and D-EIGAN's performance is consistently on par with EIGAN under different network settings.	['Carlee Joe-Wong', 'Saurabh Bagchi', 'Christopher Brinton', 'Seyyedali Hosseinalipour', 'Taejin Kim', 'Sheikh Shams Azam']	2020-10-05	null	null	null	null	['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning']	['methodology', 'natural-language-processing']	[ 8.67679119e-02 5.25362551e-01 -2.63203055e-01 -2.74594128e-01 -1.20126951e+00 -1.03768563e+00 6.79614246e-01 -6.03513187e-03 -1.36719570e-01 9.18289959e-01 3.05309445e-01 -2.94920474e-01 -1.95779726e-01 -9.73194718e-01 -1.05150485e+00 -1.00697351e+00 -3.53887498e-01 5.55759370e-01 -2.11154997e-01 -2.07027346e-01 -4.25424188e-01 5.99102855e-01 -8.51987422e-01 1.01670131e-01 3.45872134e-01 8.30588818e-01 -6.39177978e-01 2.91060656e-01 5.19836664e-01 8.29952657e-01 -7.29094207e-01 -9.14448977e-01 8.38949442e-01 -3.67071331e-01 -7.31044173e-01 -3.32440853e-01 1.63285688e-01 -5.79987824e-01 -9.07546878e-01 1.04944992e+00 5.63617468e-01 -4.76979911e-02 6.57306790e-01 -1.90116358e+00 -1.04341900e+00 1.25839710e+00 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22f6c05a-c412-4b48-b245-f997e03c51fd	hesitate-in-portuguese	null	null	https://aclanthology.org/L14-1473	https://aclanthology.org/L14-1473.pdf	HESITA(te) in Portuguese	Hesitations, so-called disfluencies, are a characteristic of spontaneous speech, playing a primary role in its structure, reflecting aspects of the language production and the management of inter-communication. In this paper we intend to present a database of hesitations in European Portuguese speech - HESITA - as a relevant base of work to study a variety of speech phenomena. Patterns of hesitations, hesitation distribution according to speaking style, and phonetic properties of the fillers are some of the characteristics we extrapolated from the HESITA database. This database also represents an important resource for improvement in synthetic speech naturalness as well as in robust acoustic modelling for automatic speech recognition. The HESITA database is the output of a project in the speech-processing field for European Portuguese held by an interdisciplinary group in intimate articulation between engineering tools and experience and the linguistic approach.	['o', 'Fern Perdig{\\~a}o', 'Carla Lopes', 'Jorge Proen{\\c{c}}a', 'C', 'Dirce Celorico', 'Sara eias', 'Arlindo Veiga']	2014-05-01	null	null	null	lrec-2014-5	['acoustic-modelling']	['speech']	[-1.08755767e-01 2.25126669e-01 3.27743083e-01 -1.99944258e-01 -4.64687169e-01 -3.84410590e-01 6.58600807e-01 1.93628088e-01 -3.42648208e-01 6.21948779e-01 5.64535081e-01 -9.63682383e-02 -8.19784403e-02 -5.94426036e-01 -3.37824821e-01 -5.56993783e-01 2.45987430e-01 4.96160775e-01 3.60608339e-01 -6.05244815e-01 1.52800575e-01 7.84596980e-01 -1.78826129e+00 4.56591487e-01 4.61667001e-01 3.37567717e-01 8.29254866e-01 4.96311724e-01 -3.61842304e-01 5.79458535e-01 -9.79898334e-01 4.97095957e-02 -3.26758325e-01 -6.31389022e-01 -1.06528068e+00 3.56596112e-01 -6.44074261e-01 4.66827691e-01 9.37220752e-02 8.59108210e-01 5.73538125e-01 1.60012931e-01 5.88532388e-01 -6.61420882e-01 4.98037273e-03 1.03742886e+00 3.66757274e-01 4.97010082e-01 5.55720150e-01 -1.39743999e-01 7.07675576e-01 -9.15938020e-01 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d9d0e1aa-504a-499a-9340-371a846d5e41	fine-grained-visual-categorization-using-meta	1807.10916	null	http://arxiv.org/abs/1807.10916v1	http://arxiv.org/pdf/1807.10916v1.pdf	Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data	Fine-grained visual categorization (FGVC) is challenging due in part to the fact that it is often difficult to acquire an enough number of training samples. To employ large models for FGVC without suffering from overfitting, existing methods usually adopt a strategy of pre-training the models using a rich set of auxiliary data, followed by fine-tuning on the target FGVC task. However, the objective of pre-training does not take the target task into account, and consequently such obtained models are suboptimal for fine-tuning. To address this issue, we propose in this paper a new deep FGVC model termed MetaFGNet. Training of MetaFGNet is based on a novel regularized meta-learning objective, which aims to guide the learning of network parameters so that they are optimal for adapting to the target FGVC task. Based on MetaFGNet, we also propose a simple yet effective scheme for selecting more useful samples from the auxiliary data. Experiments on benchmark FGVC datasets show the efficacy of our proposed method.	['Kui Jia', 'Yabin Zhang', 'Hui Tang']	2018-07-28	fine-grained-visual-categorization-using-meta-1	http://openaccess.thecvf.com/content_ECCV_2018/html/Yabin_Zhang_Fine-Grained_Visual_Categorization_ECCV_2018_paper.html	http://openaccess.thecvf.com/content_ECCV_2018/papers/Yabin_Zhang_Fine-Grained_Visual_Categorization_ECCV_2018_paper.pdf	eccv-2018-9	['fine-grained-visual-categorization']	['computer-vision']	[ 2.07875311e-01 -2.06320137e-01 5.89610264e-02 -5.14333427e-01 -5.00256896e-01 -3.24990511e-01 5.28517723e-01 1.52143657e-01 -5.03832221e-01 7.47401416e-01 3.17530520e-02 2.21611559e-03 -1.88643739e-01 -7.78867364e-01 -5.27103961e-01 -8.21536720e-01 4.14940298e-01 3.26702178e-01 2.66013205e-01 5.00080362e-02 4.39890862e-01 4.92087036e-01 -1.55850446e+00 1.78836733e-01 1.22345352e+00 1.16558099e+00 7.16615081e-01 4.15865242e-01 -2.48981476e-01 5.91155648e-01 -7.64037549e-01 -1.69426560e-01 1.06872506e-01 -5.35369098e-01 -8.26687574e-01 3.37079942e-01 1.30081192e-01 -4.90313880e-02 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bed71632-30fd-45a0-b89e-d6a3fd49373e	egotaskqa-understanding-human-tasks-in	2210.03929	null	https://arxiv.org/abs/2210.03929v1	https://arxiv.org/pdf/2210.03929v1.pdf	EgoTaskQA: Understanding Human Tasks in Egocentric Videos	Understanding human tasks through video observations is an essential capability of intelligent agents. The challenges of such capability lie in the difficulty of generating a detailed understanding of situated actions, their effects on object states (i.e., state changes), and their causal dependencies. These challenges are further aggravated by the natural parallelism from multi-tasking and partial observations in multi-agent collaboration. Most prior works leverage action localization or future prediction as an indirect metric for evaluating such task understanding from videos. To make a direct evaluation, we introduce the EgoTaskQA benchmark that provides a single home for the crucial dimensions of task understanding through question-answering on real-world egocentric videos. We meticulously design questions that target the understanding of (1) action dependencies and effects, (2) intents and goals, and (3) agents' beliefs about others. These questions are divided into four types, including descriptive (what status?), predictive (what will?), explanatory (what caused?), and counterfactual (what if?) to provide diagnostic analyses on spatial, temporal, and causal understandings of goal-oriented tasks. We evaluate state-of-the-art video reasoning models on our benchmark and show their significant gaps between humans in understanding complex goal-oriented egocentric videos. We hope this effort will drive the vision community to move onward with goal-oriented video understanding and reasoning.	['Siyuan Huang', 'Song-Chun Zhu', 'Ting Lei', 'Baoxiong Jia']	2022-10-08	null	null	null	null	['action-localization']	['computer-vision']	[ 5.78093007e-02 1.32711917e-01 -3.04197431e-01 -4.15781170e-01 -1.17093615e-01 -6.39632940e-01 1.16911423e+00 -1.10854596e-01 -2.48883590e-01 7.44462907e-01 1.03434753e+00 -1.12797171e-01 -3.78602505e-01 -4.15498674e-01 -6.76868558e-01 -4.77361619e-01 -4.72128361e-01 4.19424653e-01 2.34631032e-01 -4.31497455e-01 4.20986563e-01 6.02099486e-02 -1.73988378e+00 6.83452785e-01 5.39452910e-01 5.65061867e-01 2.51602113e-01 8.45166862e-01 2.99050212e-01 1.77711391e+00 -3.66934627e-01 -2.56435156e-01 -6.55046552e-02 -4.99576926e-01 -1.30378115e+00 2.98190027e-01 2.13898689e-01 -8.26460004e-01 -6.35908723e-01 9.28083897e-01 -1.56027317e-01 4.99988437e-01 6.36552811e-01 -1.90982926e+00 -7.02078700e-01 5.08296609e-01 -2.02422917e-01 6.35792136e-01 9.22046006e-01 7.78084338e-01 1.04174745e+00 -2.81810999e-01 8.91444921e-01 1.61322665e+00 1.98617369e-01 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3012ff17-d447-4627-a4e8-081a802f66d6	undercover-deepfakes-detecting-fake-segments	2305.06564	null	https://arxiv.org/abs/2305.06564v2	https://arxiv.org/pdf/2305.06564v2.pdf	Undercover Deepfakes: Detecting Fake Segments in Videos	The recent renaissance in generative models, driven primarily by the advent of diffusion models and iterative improvement in GAN methods, has enabled many creative applications. However, each advancement is also accompanied by a rise in the potential for misuse. In the arena of deepfake generation this is a key societal issue. In particular, the ability to modify segments of videos using such generative techniques creates a new paradigm of deepfakes which are mostly real videos altered slightly to distort the truth. Current deepfake detection methods in the academic literature are not evaluated on this paradigm. In this paper, we present a deepfake detection method able to address this issue by performing both frame and video level deepfake prediction. To facilitate testing our method we create a new benchmark dataset where videos have both real and fake frame sequences. Our method utilizes the Vision Transformer, Scaling and Shifting pretraining and Timeseries Transformer to temporally segment videos to help facilitate the interpretation of possible deepfakes. Extensive experiments on a variety of deepfake generation methods show excellent results on temporal segmentation and classical video level predictions as well. In particular, the paradigm we introduce will form a powerful tool for the moderation of deepfakes, where human oversight can be better targeted to the parts of videos suspected of being deepfakes. All experiments can be reproduced at: https://github.com/sanjaysaha1311/temporal-deepfake-segmentation.	['Saman Halgamuge', 'Terence Sim', 'Deshani Geethika', 'Sanka Rasnayaka', 'Tamasha Malepathirana', 'Sachith Seneviratne', 'Rashindrie Perera', 'Sanjay Saha']	2023-05-11	null	null	null	null	['deepfake-detection', 'face-swapping']	['computer-vision', 'computer-vision']	[ 1.50313377e-01 4.70200069e-02 3.09895948e-02 1.43001154e-02 -4.76907432e-01 -8.08496118e-01 9.10216451e-01 -7.23581731e-01 7.88874775e-02 7.31478631e-01 2.29769871e-01 -1.92938030e-01 1.54638112e-01 -8.15171480e-01 -9.84403372e-01 -7.68724561e-01 1.80510655e-01 1.99017331e-01 1.99611232e-01 -2.77657807e-01 2.63691187e-01 4.08892363e-01 -1.34409404e+00 4.91235197e-01 6.74556613e-01 7.07708299e-01 3.80238816e-02 8.43274117e-01 2.07716763e-01 7.23482788e-01 -1.04381704e+00 -7.55035579e-01 5.70228875e-01 -8.07011247e-01 -6.12698972e-01 1.84363753e-01 6.02654159e-01 -6.95619702e-01 -5.42964220e-01 1.08978379e+00 6.14702761e-01 -7.50527531e-02 3.74547839e-01 -1.50580776e+00 -7.27291405e-01 6.68802738e-01 -5.04439771e-01 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2d7a2d64-cc73-46fd-b49d-f5cbec4bb8ec	visfis-visual-feature-importance-supervision	2206.11212	null	https://arxiv.org/abs/2206.11212v2	https://arxiv.org/pdf/2206.11212v2.pdf	VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives	Many past works aim to improve visual reasoning in models by supervising feature importance (estimated by model explanation techniques) with human annotations such as highlights of important image regions. However, recent work has shown that performance gains from feature importance (FI) supervision for Visual Question Answering (VQA) tasks persist even with random supervision, suggesting that these methods do not meaningfully align model FI with human FI. In this paper, we show that model FI supervision can meaningfully improve VQA model accuracy as well as performance on several Right-for-the-Right-Reason (RRR) metrics by optimizing for four key model objectives: (1) accurate predictions given limited but sufficient information (Sufficiency); (2) max-entropy predictions given no important information (Uncertainty); (3) invariance of predictions to changes in unimportant features (Invariance); and (4) alignment between model FI explanations and human FI explanations (Plausibility). Our best performing method, Visual Feature Importance Supervision (VisFIS), outperforms strong baselines on benchmark VQA datasets in terms of both in-distribution and out-of-distribution accuracy. While past work suggests that the mechanism for improved accuracy is through improved explanation plausibility, we show that this relationship depends crucially on explanation faithfulness (whether explanations truly represent the model's internal reasoning). Predictions are more accurate when explanations are plausible and faithful, and not when they are plausible but not faithful. Lastly, we show that, surprisingly, RRR metrics are not predictive of out-of-distribution model accuracy when controlling for a model's in-distribution accuracy, which calls into question the value of these metrics for evaluating model reasoning. All supporting code is available at https://github.com/zfying/visfis	['Mohit Bansal', 'Peter Hase', 'Zhuofan Ying']	2022-06-22	null	null	null	null	['visual-reasoning', 'visual-reasoning']	['computer-vision', 'reasoning']	[ 2.72433609e-01 5.38817525e-01 -4.82616276e-01 -5.88583171e-01 -6.01292074e-01 -6.30163074e-01 1.00103343e+00 2.28113815e-01 -1.90630093e-01 6.01679623e-01 5.63775659e-01 -4.84162718e-01 -2.58718878e-01 -4.85930353e-01 -1.06431293e+00 -2.39974886e-01 3.24179709e-01 5.64805925e-01 3.13967377e-01 -5.75249083e-02 3.78626704e-01 4.85946685e-01 -1.80531871e+00 7.21952021e-01 8.46757770e-01 6.95560396e-01 -1.06753811e-01 8.39287043e-01 -6.05799956e-04 1.30854166e+00 -4.40337092e-01 -5.52904427e-01 1.29005685e-01 -7.98489928e-01 -9.82135713e-01 -8.74109864e-02 1.03582871e+00 -3.93244624e-01 -1.72270074e-01 8.78058970e-01 -1.96298599e-01 1.24151260e-01 1.11403573e+00 -1.64694309e+00 -1.15310192e+00 2.38803536e-01 -4.32432652e-01 3.58798355e-01 3.45352203e-01 6.23949349e-01 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cf622fff-db6b-4b24-b0d2-f624335b89fa	global-adaptation-meets-local-generalization	2303.16456	null	https://arxiv.org/abs/2303.16456v1	https://arxiv.org/pdf/2303.16456v1.pdf	Global Adaptation meets Local Generalization: Unsupervised Domain Adaptation for 3D Human Pose Estimation	When applying a pre-trained 2D-to-3D human pose lifting model to a target unseen dataset, large performance degradation is commonly encountered due to domain shift issues. We observe that the degradation is caused by two factors: 1) the large distribution gap over global positions of poses between the source and target datasets due to variant camera parameters and settings, and 2) the deficient diversity of local structures of poses in training. To this end, we combine \textbf{global adaptation} and \textbf{local generalization} in \textit{PoseDA}, a simple yet effective framework of unsupervised domain adaptation for 3D human pose estimation. Specifically, global adaptation aims to align global positions of poses from the source domain to the target domain with a proposed global position alignment (GPA) module. And local generalization is designed to enhance the diversity of 2D-3D pose mapping with a local pose augmentation (LPA) module. These modules bring significant performance improvement without introducing additional learnable parameters. In addition, we propose local pose augmentation (LPA) to enhance the diversity of 3D poses following an adversarial training scheme consisting of 1) a augmentation generator that generates the parameters of pre-defined pose transformations and 2) an anchor discriminator to ensure the reality and quality of the augmented data. Our approach can be applicable to almost all 2D-3D lifting models. \textit{PoseDA} achieves 61.3 mm of MPJPE on MPI-INF-3DHP under a cross-dataset evaluation setup, improving upon the previous state-of-the-art method by 10.2\%.	['Gaoang Wang', 'Jenq-Neng Hwang', 'Zhongyu Jiang', 'Wenhao Chai']	2023-03-29	null	null	null	null	['3d-human-pose-estimation']	['computer-vision']	[ 2.62999266e-01 1.39199838e-01 1.35661960e-01 -2.29032084e-01 -1.22210634e+00 -6.78437293e-01 3.85207981e-01 -3.79787952e-01 -4.05120492e-01 7.10334599e-01 2.52621233e-01 2.53289104e-01 5.49650230e-02 -6.09073699e-01 -1.09224927e+00 -6.81894779e-01 -6.88460981e-03 8.54710639e-01 2.90615380e-01 -5.61785281e-01 -1.90779135e-01 6.06214285e-01 -1.23337448e+00 1.00087039e-01 6.62503839e-01 8.96656275e-01 -1.54122533e-02 5.01407743e-01 3.48367274e-01 2.55359560e-02 -8.63791406e-01 -4.31009054e-01 7.37731636e-01 -3.31038296e-01 -5.26796520e-01 1.48405552e-01 7.87709475e-01 -4.62967753e-01 -3.99543017e-01 7.78430641e-01 1.08745444e+00 1.02211297e-01 5.94859421e-01 -1.26928890e+00 -9.97853577e-02 -8.19593444e-02 -7.40785301e-01 -1.35992214e-01 5.88311017e-01 2.80180186e-01 4.35318679e-01 -9.62811172e-01 8.97686541e-01 1.16828549e+00 9.45536852e-01 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d2df0614-3fa3-47da-8fef-c80c740103c4	mammograms-classification-a-review	2203.03618	null	https://arxiv.org/abs/2203.03618v1	https://arxiv.org/pdf/2203.03618v1.pdf	Mammograms Classification: A Review	An advanced reliable low-cost form of screening method, Digital mammography has been used as an effective imaging method for breast cancer detection. With an increased focus on technologies to aid healthcare, Mammogram images have been utilized in developing computer-aided diagnosis systems that will potentially help in clinical diagnosis. Researchers have proved that artificial intelligence with its emerging technologies can be used in the early detection of the disease and improve radiologists' performance in assessing breast cancer. In this paper, we review the methods developed for mammogram mass classification in two categories. The first one is classifying manually provided cropped region of interests (ROI) as either malignant or benign, and the second one is the classification of automatically segmented ROIs as either malignant or benign. We also provide an overview of datasets and evaluation metrics used in the classification task. Finally, we compare and discuss the deep learning approach to classical image processing and learning approach in this domain.	['Marawan Elbatel']	2022-03-04	null	null	null	null	['breast-cancer-detection', 'breast-cancer-detection']	['knowledge-base', 'medical']	[ 6.74266338e-01 4.37600315e-01 -4.00185466e-01 -5.50314963e-01 -7.42869198e-01 5.22203445e-02 4.18057412e-01 5.12836397e-01 -5.35459995e-01 3.99500579e-01 -7.63534531e-02 -6.63656116e-01 2.92783733e-02 -9.23895419e-01 -4.20465738e-01 -7.89669514e-01 -2.93338865e-01 5.47790289e-01 3.30782115e-01 2.14459896e-01 8.43991190e-02 5.49341559e-01 -1.37587833e+00 7.38410056e-01 4.37814236e-01 1.26822877e+00 3.83578151e-01 9.15636361e-01 -6.63919821e-02 1.05187738e+00 -2.07057789e-01 -4.07092839e-01 1.72399580e-01 -6.66237295e-01 -8.91851723e-01 2.04668716e-01 9.01873261e-02 -4.58832920e-01 -2.33643472e-01 1.11478436e+00 4.22318637e-01 -5.22520602e-01 8.60947013e-01 -4.61981624e-01 -1.52484000e-01 5.84109008e-01 -8.55742216e-01 6.58327818e-01 1.69963501e-02 -2.81944960e-01 1.15063131e-01 -7.03704894e-01 4.27362382e-01 1.04849541e+00 6.84704483e-01 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-5.49302518e-01 1.04214162e-01 1.55821741e-01 -4.53719079e-01 5.99469095e-02 4.81528610e-01]	[15.21530818939209, -2.50240421295166]
fea4006f-b2e7-4088-9556-84333d4a06d8	top1-solution-of-qq-browser-2021-ai-algorithm	2111.01677	null	https://arxiv.org/abs/2111.01677v1	https://arxiv.org/pdf/2111.01677v1.pdf	Top1 Solution of QQ Browser 2021 Ai Algorithm Competition Track 1 : Multimodal Video Similarity	In this paper, we describe the solution to the QQ Browser 2021 Ai Algorithm Competition (AIAC) Track 1. We use the multi-modal transformer model for the video embedding extraction. In the pretrain phase, we train the model with three tasks, (1) Video Tag Classification (VTC), (2) Mask Language Modeling (MLM) and (3) Mask Frame Modeling (MFM). In the finetune phase, we train the model with video similarity based on rank normalized human labels. Our full pipeline, after ensembling several models, scores 0.852 on the leaderboard, which we achieved the 1st place in the competition. The source codes have been released at Github.	['Xuan Ouyang', 'Majing Lou', 'Zhuoran Ma']	2021-10-30	null	null	null	null	['video-similarity']	['computer-vision']	[ 3.38320374e-01 1.17443070e-01 -1.99559808e-01 -1.63119495e-01 -1.16400576e+00 -5.94826639e-01 8.60388100e-01 -2.65495718e-01 -8.00526083e-01 2.85858482e-01 2.94168711e-01 -3.40071440e-01 3.07002872e-01 -1.04622178e-01 -8.81634235e-01 -3.88678342e-01 -3.70838344e-02 5.74051082e-01 5.01913786e-01 8.89893696e-02 -1.20245822e-01 -1.34911492e-01 -1.50081599e+00 1.05858648e+00 3.58540177e-01 1.37586808e+00 1.06566109e-01 1.15089905e+00 3.07956692e-02 1.18921995e+00 -1.67725950e-01 -9.05900061e-01 1.76504731e-01 -3.40443969e-01 -9.97299612e-01 -2.94613749e-01 6.35207117e-01 -4.42701429e-01 -5.31487644e-01 1.05968153e+00 3.01351964e-01 6.92829639e-02 5.10626853e-01 -1.66524553e+00 -1.42843589e-01 7.97040224e-01 -3.48022133e-01 9.26874056e-02 3.76541406e-01 2.97350734e-01 1.16152894e+00 -1.38346756e+00 7.70963848e-01 1.24881065e+00 4.91600603e-01 7.44426548e-01 -1.07010174e+00 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3.32433917e-02 -5.79988658e-01 -2.06684187e-01 4.28156525e-01]	[10.180978775024414, 0.9148262739181519]
92c7f85b-6976-46f6-bad5-80b62b2e1e4d	impact-of-channel-variation-on-one-class	2109.14900	null	https://arxiv.org/abs/2109.14900v3	https://arxiv.org/pdf/2109.14900v3.pdf	Impact of Channel Variation on One-Class Learning for Spoof Detection	Margin-based losses, especially one-class classification loss, have improved the generalization capabilities of countermeasure systems (CMs), but their reliability is not tested with spoofing attacks degraded with channel variation. Our experiments aim to tackle this in two ways: first, by investigating the impact of various codec simulations and their corresponding parameters, namely bit-rate, discontinuous transmission (DTX), and loss, on the performance of the one-class classification-based CM system; second, by testing the efficacy of the various settings of margin-based losses for training and evaluating our CM system on codec simulated data. Multi-conditional training (MCT) along with various data-feeding and custom mini-batching strategies were also explored to handle the added variability in the new data setting and to find an optimal setting to carry out the above experiments. Our experimental results reveal that a strict restrain over the embedding space degrades the performance of the one-class classification model. MCT relatively improves performance by 35.55\%, and custom mini-batching captures more generalized features for the new data setting. Whereas varying the codec parameters made a significant impact on the performance of the countermeasure system.	['Rohit Singh Rathore', 'Anmol Arora', 'Rohit Arora']	2021-09-30	null	null	null	null	['one-class-classification']	['miscellaneous']	[ 6.36585951e-02 -3.07414293e-01 -2.64934838e-01 -1.86261442e-02 -6.70937300e-01 -5.67621052e-01 5.24814367e-01 3.12341928e-01 -6.64059281e-01 3.85510981e-01 -1.61695600e-01 -9.79521453e-01 -1.64533764e-01 -6.86228395e-01 -5.15884340e-01 -9.51354206e-01 -7.68368065e-01 -3.58143181e-01 5.85340381e-01 -3.07813585e-01 3.57594699e-01 6.21515930e-01 -1.16638744e+00 1.31065875e-01 8.42969656e-01 1.00438452e+00 -9.66083109e-02 7.51896262e-01 1.73148170e-01 2.98540354e-01 -1.15509927e+00 -6.53482616e-01 4.31976497e-01 -1.41735584e-01 -3.18220079e-01 -2.66324729e-01 -4.05394547e-02 -3.84637713e-01 -4.18695360e-01 1.24303293e+00 5.18541157e-01 -3.56316566e-01 3.74926537e-01 -1.24444103e+00 -1.14675965e-02 8.05270314e-01 -3.47642690e-01 3.88311267e-01 1.50384322e-01 2.66402096e-01 5.60052276e-01 -3.66962850e-02 1.94986165e-01 1.24254262e+00 8.37520182e-01 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e94c4696-aa5f-432c-91af-60481630b386	mudiff-unified-diffusion-for-complete	2304.14621	null	https://arxiv.org/abs/2304.14621v2	https://arxiv.org/pdf/2304.14621v2.pdf	MUDiff: Unified Diffusion for Complete Molecule Generation	Molecule generation is a very important practical problem, with uses in drug discovery and material design, and AI methods promise to provide useful solutions. However, existing methods for molecule generation focus either on 2D graph structure or on 3D geometric structure, which is not sufficient to represent a complete molecule as 2D graph captures mainly topology while 3D geometry captures mainly spatial atom arrangements. Combining these representations is essential to better represent a molecule. In this paper, we present a new model for generating a comprehensive representation of molecules, including atom features, 2D discrete molecule structures, and 3D continuous molecule coordinates, by combining discrete and continuous diffusion processes. The use of diffusion processes allows for capturing the probabilistic nature of molecular processes and exploring the effect of different factors on molecular structures. Additionally, we propose a novel graph transformer architecture to denoise the diffusion process. The transformer adheres to 3D roto-translation equivariance constraints, allowing it to learn invariant atom and edge representations while preserving the equivariance of atom coordinates. This transformer can be used to learn molecular representations robust to geometric transformations. We evaluate the performance of our model through experiments and comparisons with existing methods, showing its ability to generate more stable and valid molecules. Our model is a promising approach for designing stable and diverse molecules and can be applied to a wide range of tasks in molecular modeling.	['Doina Precup', 'Stefano Ermon', 'Jie Fu', 'Rex Ying', 'Minkai Xu', 'Sitao Luan', 'Chenqing Hua']	2023-04-28	null	null	null	null	['drug-discovery']	['medical']	[ 3.46792072e-01 -1.05074376e-01 -4.84355301e-01 -4.42904346e-02 -2.05581218e-01 -7.20611632e-01 8.34464490e-01 4.77455407e-01 -1.11597283e-02 8.42181087e-01 1.41236395e-01 -3.58624399e-01 -1.35463670e-01 -1.26976979e+00 -8.40862215e-01 -9.51202869e-01 -1.28640682e-01 6.55524552e-01 -1.48244640e-02 -3.50823164e-01 2.96450973e-01 1.14299142e+00 -1.08422053e+00 -7.07253302e-03 1.00133216e+00 4.11582947e-01 1.05464101e-01 3.44305664e-01 -1.83808178e-01 3.90284449e-01 -3.71274143e-01 -2.82664955e-01 1.37377411e-01 -7.35812664e-01 -6.02582097e-01 -1.13389440e-01 2.12960199e-01 2.27400020e-01 -3.18271250e-01 9.00582314e-01 6.20984495e-01 3.87465775e-01 1.15151489e+00 -6.63480520e-01 -9.63337362e-01 3.21718961e-01 -4.23099875e-01 -2.39864498e-01 6.38089478e-01 2.40121290e-01 1.07721519e+00 -8.01222444e-01 9.61524785e-01 1.47655618e+00 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3b7b9d05-ca88-4804-9b90-8fa3a1228a2d	1st-place-solution-to-eccv-2022-challenge-on-1	2209.00224	null	https://arxiv.org/abs/2209.00224v1	https://arxiv.org/pdf/2209.00224v1.pdf	1st Place Solution to ECCV 2022 Challenge on Out of Vocabulary Scene Text Understanding: End-to-End Recognition of Out of Vocabulary Words	Scene text recognition has attracted increasing interest in recent years due to its wide range of applications in multilingual translation, autonomous driving, etc. In this report, we describe our solution to the Out of Vocabulary Scene Text Understanding (OOV-ST) Challenge, which aims to extract out-of-vocabulary (OOV) words from natural scene images. Our oCLIP-based model achieves 28.59\% in h-mean which ranks 1st in end-to-end OOV word recognition track of OOV Challenge in ECCV2022 TiE Workshop.	['Song Bai', 'Wenqing Zhang', 'Yu Hao', 'Chuhui Xue', 'Zhangzi Zhu']	2022-09-01	null	null	null	null	['scene-text-recognition']	['computer-vision']	[ 4.50928390e-01 -8.20691884e-02 -3.96631390e-01 -5.83799422e-01 -9.31312561e-01 -6.38974905e-01 1.15634084e+00 -8.20285976e-02 -4.34433907e-01 2.98890442e-01 6.56925917e-01 -3.72185707e-01 5.66576838e-01 -1.41023248e-01 -7.86131501e-01 -1.36286467e-01 4.82983440e-01 6.10591829e-01 2.92676479e-01 -3.11168253e-01 3.88579458e-01 1.71728544e-02 -1.38731647e+00 8.01274955e-01 8.32965910e-01 8.15649688e-01 4.85691845e-01 8.73793483e-01 -7.20411777e-01 7.82707930e-01 -4.83665794e-01 -4.32263613e-01 8.21567774e-02 -2.17712209e-01 -1.01656187e+00 2.41221413e-01 1.28405166e+00 1.59920678e-02 -6.30662560e-01 1.00307524e+00 5.42915761e-01 -2.29196265e-01 9.29422736e-01 -1.10368979e+00 -7.81314492e-01 6.36138260e-01 -2.79560387e-01 3.34833026e-01 3.38259608e-01 -1.90413073e-01 1.09803975e+00 -1.47836113e+00 1.27132761e+00 1.30334556e+00 1.10308710e-03 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e4db41b2-9ddf-451c-bef2-183430c4e10c	knowledge-graph-transfer-network-for-few-shot	1911.09579	null	https://arxiv.org/abs/1911.09579v2	https://arxiv.org/pdf/1911.09579v2.pdf	Knowledge Graph Transfer Network for Few-Shot Recognition	Few-shot learning aims to learn novel categories from very few samples given some base categories with sufficient training samples. The main challenge of this task is the novel categories are prone to dominated by color, texture, shape of the object or background context (namely specificity), which are distinct for the given few training samples but not common for the corresponding categories (see Figure 1). Fortunately, we find that transferring information of the correlated based categories can help learn the novel concepts and thus avoid the novel concept being dominated by the specificity. Besides, incorporating semantic correlations among different categories can effectively regularize this information transfer. In this work, we represent the semantic correlations in the form of structured knowledge graph and integrate this graph into deep neural networks to promote few-shot learning by a novel Knowledge Graph Transfer Network (KGTN). Specifically, by initializing each node with the classifier weight of the corresponding category, a propagation mechanism is learned to adaptively propagate node message through the graph to explore node interaction and transfer classifier information of the base categories to those of the novel ones. Extensive experiments on the ImageNet dataset show significant performance improvement compared with current leading competitors. Furthermore, we construct an ImageNet-6K dataset that covers larger scale categories, i.e, 6,000 categories, and experiments on this dataset further demonstrate the effectiveness of our proposed model. Our codes and models are available at https://github.com/MyChocer/KGTN .	['Riquan Chen', 'Liang Lin', 'Tianshui Chen', 'Hefeng Wu', 'Guanbin Li', 'Xiaolu Hui']	2019-11-21	null	null	null	null	['novel-concepts']	['reasoning']	[ 1.37280226e-01 1.81305185e-01 -2.41077706e-01 -4.00929362e-01 -1.09002106e-02 -3.18181187e-01 4.41917926e-01 1.78895056e-01 -2.27165371e-01 6.71447575e-01 1.35463700e-02 1.73213154e-01 -1.38683885e-01 -1.07850778e+00 -7.17109680e-01 -8.37652981e-01 -1.89771116e-01 1.64717123e-01 6.41231954e-01 -3.03750426e-01 5.77976853e-02 -3.20322905e-03 -1.53844643e+00 2.22180575e-01 7.74140894e-01 1.13324428e+00 3.67140234e-01 2.29693413e-01 -2.20549390e-01 7.82798946e-01 -3.13987315e-01 -1.84269950e-01 3.99179645e-02 -5.56426406e-01 -6.87954068e-01 1.49402231e-01 3.75413805e-01 -5.37741929e-02 -5.35131335e-01 1.30472171e+00 3.27444017e-01 5.69433391e-01 6.43796146e-01 -1.29013860e+00 -9.71733034e-01 7.77183235e-01 -6.00807250e-01 3.44923049e-01 -1.32841691e-01 3.34440619e-01 9.31999445e-01 -1.01574624e+00 7.46537685e-01 1.11783314e+00 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7732df62-386a-4151-af58-07396f0ef4d1	lip-to-speech-synthesis-in-the-wild-with	2302.08841	null	https://arxiv.org/abs/2302.08841v1	https://arxiv.org/pdf/2302.08841v1.pdf	Lip-to-Speech Synthesis in the Wild with Multi-task Learning	Recent studies have shown impressive performance in Lip-to-speech synthesis that aims to reconstruct speech from visual information alone. However, they have been suffering from synthesizing accurate speech in the wild, due to insufficient supervision for guiding the model to infer the correct content. Distinct from the previous methods, in this paper, we develop a powerful Lip2Speech method that can reconstruct speech with correct contents from the input lip movements, even in a wild environment. To this end, we design multi-task learning that guides the model using multimodal supervision, i.e., text and audio, to complement the insufficient word representations of acoustic feature reconstruction loss. Thus, the proposed framework brings the advantage of synthesizing speech containing the right content of multiple speakers with unconstrained sentences. We verify the effectiveness of the proposed method using LRS2, LRS3, and LRW datasets.	['Yong Man Ro', 'Joanna Hong', 'Minsu Kim']	2023-02-17	null	null	null	null	['lip-to-speech-synthesis', 'speech-synthesis']	['computer-vision', 'speech']	[ 2.43885234e-01 2.95389891e-01 -1.85866222e-01 -1.53528482e-01 -1.20666385e+00 -1.57809407e-01 5.15951753e-01 -5.92030108e-01 1.51308263e-02 7.58565128e-01 6.45963550e-01 -1.95690393e-01 3.58897865e-01 -1.82177663e-01 -7.99198687e-01 -7.87612140e-01 8.91911626e-01 1.11144297e-01 -4.02255431e-02 -9.82540697e-02 7.10237920e-02 6.94738254e-02 -1.87971640e+00 4.09543633e-01 8.31601024e-01 9.87148166e-01 6.99046016e-01 6.26024723e-01 -4.83234763e-01 5.53434610e-01 -4.82548863e-01 -4.07692969e-01 1.39212534e-01 -6.50167644e-01 -2.17873886e-01 2.85568118e-01 4.76781189e-01 -6.69030845e-01 -2.43142113e-01 9.27928209e-01 9.39989030e-01 1.10294148e-02 7.12569833e-01 -1.27267289e+00 -4.25875455e-01 4.53405231e-01 -5.01356006e-01 -5.01562238e-01 5.19221783e-01 2.31636375e-01 1.08825958e+00 -1.14715981e+00 5.11021614e-01 1.51359117e+00 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77b8665d-c0d4-41e9-91d0-957d31d9a16d	taking-a-closer-look-at-visual-relation	2303.13209	null	https://arxiv.org/abs/2303.13209v1	https://arxiv.org/pdf/2303.13209v1.pdf	Taking A Closer Look at Visual Relation: Unbiased Video Scene Graph Generation with Decoupled Label Learning	Current video-based scene graph generation (VidSGG) methods have been found to perform poorly on predicting predicates that are less represented due to the inherent biased distribution in the training data. In this paper, we take a closer look at the predicates and identify that most visual relations (e.g. sit_above) involve both actional pattern (sit) and spatial pattern (above), while the distribution bias is much less severe at the pattern level. Based on this insight, we propose a decoupled label learning (DLL) paradigm to address the intractable visual relation prediction from the pattern-level perspective. Specifically, DLL decouples the predicate labels and adopts separate classifiers to learn actional and spatial patterns respectively. The patterns are then combined and mapped back to the predicate. Moreover, we propose a knowledge-level label decoupling method to transfer non-target knowledge from head predicates to tail predicates within the same pattern to calibrate the distribution of tail classes. We validate the effectiveness of DLL on the commonly used VidSGG benchmark, i.e. VidVRD. Extensive experiments demonstrate that the DLL offers a remarkably simple but highly effective solution to the long-tailed problem, achieving the state-of-the-art VidSGG performance.	['Jun Xiao', 'Yi Yang', 'Lei Chen', 'Tao Jiang', 'Zhiqing Chen', 'Yawei Luo', 'Wenqing Wang']	2023-03-23	null	null	null	null	['scene-graph-generation']	['computer-vision']	[ 4.20044363e-01 2.17438072e-01 -5.09167790e-01 -3.48602802e-01 -4.88288015e-01 -6.73348427e-01 7.99877167e-01 9.14128721e-02 2.46362910e-01 6.00544095e-01 1.08406290e-01 -3.37200135e-01 -1.90447554e-01 -7.56653070e-01 -9.95422661e-01 -7.64560223e-01 3.32514085e-02 6.66157901e-01 6.22127295e-01 -1.53593663e-02 -1.07729785e-01 4.36327934e-01 -1.76423478e+00 5.90748250e-01 7.53845513e-01 1.02463746e+00 -4.36588898e-02 2.95604497e-01 -5.37795462e-02 1.33769715e+00 -4.57748950e-01 -4.81196970e-01 1.76528245e-01 -4.92966712e-01 -7.87799656e-01 2.14600146e-01 8.25326443e-01 1.84534565e-01 -3.75148714e-01 9.78887916e-01 2.56445378e-01 1.87156603e-01 8.81825507e-01 -1.74249315e+00 -6.29716933e-01 3.27441603e-01 -7.97380626e-01 7.08675459e-02 4.36883956e-01 5.80003578e-03 1.50354528e+00 -8.60438764e-01 9.52445507e-01 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c39a40f8-291a-4894-8bfc-3d21e93ec735	mvkt-ecg-efficient-single-lead-ecg	2301.12178	null	https://arxiv.org/abs/2301.12178v1	https://arxiv.org/pdf/2301.12178v1.pdf	MVKT-ECG: Efficient Single-lead ECG Classification on Multi-Label Arrhythmia by Multi-View Knowledge Transferring	The widespread emergence of smart devices for ECG has sparked demand for intelligent single-lead ECG-based diagnostic systems. However, it is challenging to develop a single-lead-based ECG interpretation model for multiple diseases diagnosis due to the lack of some key disease information. In this work, we propose inter-lead Multi-View Knowledge Transferring of ECG (MVKT-ECG) to boost single-lead ECG's ability for multi-label disease diagnosis. This training strategy can transfer superior disease knowledge from multiple different views of ECG (e.g. 12-lead ECG) to single-lead-based ECG interpretation model to mine details in single-lead ECG signals that are easily overlooked by neural networks. MVKT-ECG allows this lead variety as a supervision signal within a teacher-student paradigm, where the teacher observes multi-lead ECG educates a student who observes only single-lead ECG. Since the mutual disease information between the single-lead ECG and muli-lead ECG plays a key role in knowledge transferring, we present a new disease-aware Contrastive Lead-information Transferring(CLT) to improve the mutual disease information between the single-lead ECG and muli-lead ECG. Moreover, We modify traditional Knowledge Distillation to multi-label disease Knowledge Distillation (MKD) to make it applicable for multi-label disease diagnosis. The comprehensive experiments verify that MVKT-ECG has an excellent performance in improving the diagnostic effect of single-lead ECG.	['Guijin Wang', 'Jintao Fei', 'Wenming Yang', 'Wei-Qiang Zhang', 'Hui Chen', 'Li Sun', 'Yuzhen Qin']	2023-01-28	null	null	null	null	['ecg-classification']	['medical']	[ 4.61262763e-01 4.78620790e-02 -8.63810629e-02 -4.41973865e-01 -9.29256499e-01 -6.12041652e-01 -3.59299183e-01 -9.20737311e-02 8.40325058e-02 7.46788085e-01 -2.25827359e-02 -4.70322788e-01 -6.57261133e-01 -7.55646646e-01 -4.13934439e-01 -8.49600434e-01 2.72136927e-01 6.36166811e-01 -2.30245128e-01 1.74292356e-01 -4.51357335e-01 2.60599881e-01 -9.89137173e-01 4.49152470e-01 1.17286885e+00 7.59611309e-01 6.79095313e-02 7.30721056e-01 3.36523265e-01 9.02088702e-01 -9.15995657e-01 -3.15315634e-01 -1.01557024e-01 -1.06246710e+00 -8.98277164e-01 -4.87019345e-02 6.47116750e-02 -2.32556075e-01 -1.28712267e-01 9.59048867e-01 1.32582724e+00 -4.44100946e-01 5.17195284e-01 -1.15026832e+00 -5.48695087e-01 5.77681184e-01 -5.63049316e-01 3.63139659e-01 3.13262731e-01 -1.39879838e-01 3.64781797e-01 -3.97964627e-01 5.47655284e-01 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4.40263480e-01 -7.07129017e-02 -3.24818254e-01 -8.90905380e-01 -8.80418897e-01 4.60677296e-01 3.49734187e-01 -2.90768623e-01 1.39147371e-01 5.57240009e-01]	[14.230962753295898, 3.174302577972412]
77412d42-db9b-4cf3-bd56-485b7f4a257a	deep-bingham-networks-dealing-with	2012.11002	null	https://arxiv.org/abs/2012.11002v1	https://arxiv.org/pdf/2012.11002v1.pdf	Deep Bingham Networks: Dealing with Uncertainty and Ambiguity in Pose Estimation	In this work, we introduce Deep Bingham Networks (DBN), a generic framework that can naturally handle pose-related uncertainties and ambiguities arising in almost all real life applications concerning 3D data. While existing works strive to find a single solution to the pose estimation problem, we make peace with the ambiguities causing high uncertainty around which solutions to identify as the best. Instead, we report a family of poses which capture the nature of the solution space. DBN extends the state of the art direct pose regression networks by (i) a multi-hypotheses prediction head which can yield different distribution modes; and (ii) novel loss functions that benefit from Bingham distributions on rotations. This way, DBN can work both in unambiguous cases providing uncertainty information, and in ambiguous scenes where an uncertainty per mode is desired. On a technical front, our network regresses continuous Bingham mixture models and is applicable to both 2D data such as images and to 3D data such as point clouds. We proposed new training strategies so as to avoid mode or posterior collapse during training and to improve numerical stability. Our methods are thoroughly tested on two different applications exploiting two different modalities: (i) 6D camera relocalization from images; and (ii) object pose estimation from 3D point clouds, demonstrating decent advantages over the state of the art. For the former we contributed our own dataset composed of five indoor scenes where it is unavoidable to capture images corresponding to views that are hard to uniquely identify. For the latter we achieve the top results especially for symmetric objects of ModelNet dataset.	['Tolga Birdal', 'Slobodan Ilic', 'Leonidas Guibas', 'Nassir Navab', 'Mai Bui', 'Haowen Deng']	2020-12-20	null	null	null	null	['camera-relocalization']	['computer-vision']	[ 1.02843247e-01 1.40564442e-01 -5.85828349e-02 -2.69922853e-01 -8.38517666e-01 -6.28032446e-01 6.80438221e-01 -2.89351076e-01 -4.61028308e-01 7.00791240e-01 -2.02165574e-01 -2.73388624e-01 -5.53567410e-01 -4.50000018e-01 -1.01289785e+00 -7.70124853e-01 3.13119479e-02 1.08949447e+00 1.03587210e-01 -1.71530366e-01 1.94846123e-01 9.12255585e-01 -1.53455043e+00 -2.09424332e-01 6.78363681e-01 1.15041375e+00 2.27514878e-01 3.80711555e-01 8.63279328e-02 1.31441087e-01 -3.36777240e-01 -3.36864740e-01 5.90514481e-01 2.53313571e-01 -4.64589059e-01 1.75875157e-01 7.42916822e-01 -3.17881167e-01 2.15603914e-02 1.03283894e+00 5.33161998e-01 -8.99537206e-02 9.22225416e-01 -1.32755125e+00 -1.40668020e-01 3.26809764e-01 -6.10092998e-01 -2.53317833e-01 1.84770212e-01 -4.49181907e-02 8.07694137e-01 -9.94259298e-01 6.39967918e-01 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6ffe0125-3345-408b-adc1-5b66c35576e7	scope-safe-exploration-for-dynamic-computer	2204.10451	null	https://arxiv.org/abs/2204.10451v1	https://arxiv.org/pdf/2204.10451v1.pdf	SCOPE: Safe Exploration for Dynamic Computer Systems Optimization	Modern computer systems need to execute under strict safety constraints (e.g., a power limit), but doing so often conflicts with their ability to deliver high performance (i.e. minimal latency). Prior work uses machine learning to automatically tune hardware resources such that the system execution meets safety constraints optimally. Such solutions monitor past system executions to learn the system's behavior under different hardware resource allocations before dynamically tuning resources to optimize the application execution. However, system behavior can change significantly between different applications and even different inputs of the same applications. Hence, the models learned using data collected a priori are often suboptimal and violate safety constraints when used with new applications and inputs. To address this limitation, we introduce the concept of an execution space, which is the cross product of hardware resources, input features, and applications. To dynamically and safely allocate hardware resources from the execution space, we present SCOPE, a resource manager that leverages a novel safe exploration framework. We evaluate SCOPE's ability to deliver improved latency while minimizing power constraint violations by dynamically configuring hardware while running a variety of Apache Spark applications. Compared to prior approaches that minimize power constraint violations, SCOPE consumes comparable power while improving latency by up to 9.5X. Compared to prior approaches that minimize latency, SCOPE achieves similar latency but reduces power constraint violation rates by up to 45.88X, achieving almost zero safety constraint violations across all applications.	['Yi Ding', 'Michael Carbin', 'Henry Hoffmann', 'Ahsan Pervaiz', 'Hyunji Kim']	2022-04-22	null	null	null	null	['safe-exploration']	['robots']	[-6.68620691e-02 -3.47051710e-01 -7.78233707e-01 -3.37218523e-01 -5.81489146e-01 -6.41962349e-01 2.23532349e-01 3.36427301e-01 -2.25185752e-01 2.09622443e-01 -2.98723076e-02 -7.46729970e-01 1.87829018e-01 -6.79798007e-01 -6.56678200e-01 -4.84874338e-01 -3.23582202e-01 1.27960473e-01 3.76067191e-01 6.84976131e-02 4.61439431e-01 3.43439728e-01 -1.74211717e+00 3.74927282e-01 5.35659432e-01 7.52976537e-01 -2.46522129e-01 7.51336217e-01 1.22600041e-01 6.12336516e-01 -7.95899630e-01 6.20627820e-01 4.01511282e-01 -1.95584111e-02 -8.29227865e-01 -3.63463312e-01 1.13982357e-01 -6.15353346e-01 -2.79795140e-01 7.57067382e-01 2.03368217e-01 -1.12649545e-01 -1.88250214e-01 -1.79394662e+00 2.94031370e-02 9.54470158e-01 -8.61607313e-01 3.59326541e-01 9.96131524e-02 5.36578417e-01 6.45386815e-01 3.27996686e-02 5.90255782e-02 6.64114296e-01 3.48819643e-01 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2f26f82c-6085-4f16-88fd-49af5e85ef64	global-inference-to-chinese-temporal-relation	null	null	https://aclanthology.org/C16-1137	https://aclanthology.org/C16-1137.pdf	Global Inference to Chinese Temporal Relation Extraction	Previous studies on temporal relation extraction focus on mining sentence-level information or enforcing coherence on different temporal relation types among various event mentions in the same sentence or neighboring sentences, largely ignoring those discourse-level temporal relations in nonadjacent sentences. In this paper, we propose a discourse-level global inference model to mine those temporal relations between event mentions in document-level, especially in nonadjacent sentences. Moreover, we provide various kinds of discourse-level constraints, which derived from event semantics, to further improve our global inference model. Evaluation on a Chinese corpus justifies the effectiveness of our discourse-level global inference model over two strong baselines.	['Qiaoming Zhu', 'Peifeng Li', 'Guodong Zhou', 'Hongling Wang']	2016-12-01	global-inference-to-chinese-temporal-relation-1	https://aclanthology.org/C16-1137	https://aclanthology.org/C16-1137.pdf	coling-2016-12	['temporal-relation-extraction']	['natural-language-processing']	[ 8.46641138e-02 3.29854935e-01 -7.68030226e-01 -4.40949172e-01 -6.53968573e-01 -5.83735108e-01 9.47902143e-01 6.20379210e-01 -4.44216251e-01 1.15045202e+00 1.04616296e+00 -5.16148925e-01 -2.99393505e-01 -1.09813631e+00 -3.98764312e-01 -2.84027636e-01 -5.53207636e-01 5.38795209e-03 8.28891575e-01 -5.01485705e-01 2.51925349e-01 1.67670306e-02 -9.49471116e-01 6.83489203e-01 6.21798098e-01 4.88138109e-01 1.37133807e-01 4.86762077e-01 -7.09833354e-02 1.64088702e+00 -9.78091538e-01 -3.63291144e-01 -3.82764310e-01 -8.22413504e-01 -1.30074835e+00 -1.77258179e-01 -1.95653796e-01 -1.98309109e-01 -4.65117931e-01 7.80828118e-01 5.57580143e-02 4.40323591e-01 6.25531018e-01 -8.19343269e-01 -3.81237656e-01 1.31325924e+00 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10cd9a61-8f8d-4c76-ba3e-6c74b5a89988	implicit-warping-for-animation-with-image	2210.01794	null	https://arxiv.org/abs/2210.01794v1	https://arxiv.org/pdf/2210.01794v1.pdf	Implicit Warping for Animation with Image Sets	We present a new implicit warping framework for image animation using sets of source images through the transfer of the motion of a driving video. A single cross- modal attention layer is used to find correspondences between the source images and the driving image, choose the most appropriate features from different source images, and warp the selected features. This is in contrast to the existing methods that use explicit flow-based warping, which is designed for animation using a single source and does not extend well to multiple sources. The pick-and-choose capability of our framework helps it achieve state-of-the-art results on multiple datasets for image animation using both single and multiple source images. The project website is available at https://deepimagination.cc/implicit warping/	['Ming-Yu Liu', 'Ting-Chun Wang', 'Arun Mallya']	2022-10-04	null	null	null	null	['image-animation']	['computer-vision']	[ 1.07100524e-01 -3.21612746e-01 -2.79204398e-01 -1.58130631e-01 -5.79126596e-01 -6.34478629e-01 9.62771714e-01 -6.16973221e-01 -5.39770663e-01 3.02247941e-01 2.86095619e-01 5.38303778e-02 5.69227450e-02 -6.15270615e-01 -7.73501694e-01 -6.37128055e-01 -8.85479301e-02 2.98944801e-01 3.85108560e-01 -5.73418319e-01 2.65853703e-01 3.91781777e-01 -1.59348989e+00 4.29692388e-01 3.89919102e-01 5.18229127e-01 3.42187285e-01 9.32349503e-01 1.47839144e-01 8.08438659e-01 -2.62209892e-01 -1.47495940e-01 5.57044148e-01 -7.89733946e-01 -9.42188919e-01 1.06617818e-02 7.12561429e-01 -6.71818674e-01 -6.63592398e-01 9.75255191e-01 3.37519914e-01 3.42607677e-01 2.80449629e-01 -1.64193726e+00 -8.28892112e-01 4.50118929e-01 -5.57874262e-01 6.15486801e-01 5.29841065e-01 7.55615413e-01 9.06861603e-01 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4a9aeb87-7c15-40d5-b206-0c69fcf8e32f	bayesian-neural-networks-via-mcmc-a-python	2304.02595	null	https://arxiv.org/abs/2304.02595v1	https://arxiv.org/pdf/2304.02595v1.pdf	Bayesian neural networks via MCMC: a Python-based tutorial	Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain Monte-Carlo (MCMC) sampling techniques are used to implement Bayesian inference. In the past three decades, MCMC methods have faced a number of challenges in being adapted to larger models (such as in deep learning) and big data problems. Advanced proposals that incorporate gradients, such as a Langevin proposal distribution, provide a means to address some of the limitations of MCMC sampling for Bayesian neural networks. Furthermore, MCMC methods have typically been constrained to use by statisticians and are still not prominent among deep learning researchers. We present a tutorial for MCMC methods that covers simple Bayesian linear and logistic models, and Bayesian neural networks. The aim of this tutorial is to bridge the gap between theory and implementation via coding, given a general sparsity of libraries and tutorials to this end. This tutorial provides code in Python with data and instructions that enable their use and extension. We provide results for some benchmark problems showing the strengths and weaknesses of implementing the respective Bayesian models via MCMC. We highlight the challenges in sampling multi-modal posterior distributions in particular for the case of Bayesian neural networks, and the need for further improvement of convergence diagnosis.	['Joshua Simmons', 'Royce Chen', 'Rohitash Chandra']	2023-04-02	null	null	null	null	['bayesian-inference']	['methodology']	[-1.41639426e-01 -3.34829465e-02 -3.62848490e-02 -6.23036623e-01 -7.90548027e-01 -3.07457775e-01 8.09284747e-01 -3.88229609e-01 -6.54157281e-01 9.75984931e-01 -1.73257664e-02 -4.18839633e-01 -2.01259717e-01 -7.81755388e-01 -6.78087533e-01 -1.09207141e+00 -3.76428366e-02 7.07973182e-01 1.48861647e-01 4.12947297e-01 2.30908662e-01 5.57350278e-01 -1.66695142e+00 9.23327077e-03 4.01608795e-01 5.81872404e-01 1.76517248e-01 6.28864408e-01 -2.35550791e-01 4.58935380e-01 -3.63775462e-01 -4.12757248e-01 -3.58031809e-01 -8.17590952e-02 -4.56297755e-01 -5.92066467e-01 4.16853786e-01 -6.82915032e-01 -2.33464867e-01 9.72369611e-01 7.74533331e-01 2.72664964e-01 1.10140562e+00 -1.15345931e+00 -1.40475646e-01 8.37727904e-01 -5.14777362e-01 2.09326699e-01 -5.22817075e-02 1.03510797e-01 6.21359468e-01 -9.91014063e-01 2.68801987e-01 1.75876522e+00 1.00898612e+00 8.48905683e-01 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98dc42bf-601c-4f74-817b-a1fbc8905b11	probing-cross-modal-representations-in-multi	null	null	https://aclanthology.org/2021.repl4nlp-1.16	https://aclanthology.org/2021.repl4nlp-1.16.pdf	Probing Cross-Modal Representations in Multi-Step Relational Reasoning	We investigate the representations learned by vision and language models in tasks that require relational reasoning. Focusing on the problem of assessing the relative size of objects in abstract visual contexts, we analyse both one-step and two-step reasoning. For the latter, we construct a new dataset of three-image scenes and define a task that requires reasoning at the level of the individual images and across images in a scene. We probe the learned model representations using diagnostic classifiers. Our experiments show that pretrained multimodal transformer-based architectures can perform higher-level relational reasoning, and are able to learn representations for novel tasks and data that are very different from what was seen in pretraining.	['Sandro Pezzelle', 'Raquel Fernández', 'Desmond Elliott', 'Iuliia Parfenova']	null	null	null	null	acl-repl4nlp-2021-8	['relational-reasoning']	['natural-language-processing']	[ 4.74733263e-01 5.27679026e-01 3.36256534e-01 -4.89973217e-01 -4.18583870e-01 -7.27362692e-01 1.08242273e+00 4.46354061e-01 -3.82339537e-01 1.80534735e-01 3.40593725e-01 -2.77077317e-01 -3.69679511e-01 -8.51198912e-01 -8.79434168e-01 -5.66009820e-01 1.32117420e-01 9.39882517e-01 4.66171890e-01 -2.19129995e-01 4.30584610e-01 7.87166893e-01 -1.67530215e+00 1.18845880e+00 2.51575172e-01 8.32570553e-01 1.79656416e-01 8.67438674e-01 -2.97512323e-01 1.41014099e+00 -4.57186908e-01 -5.44775069e-01 -2.95615476e-02 -4.65858042e-01 -1.26076949e+00 3.84644687e-01 8.54360878e-01 -2.85243034e-01 -4.18061852e-01 9.67301309e-01 -5.18468358e-02 1.20830618e-01 9.08632994e-01 -1.08717847e+00 -1.04647458e+00 8.16476047e-01 -3.09214115e-01 5.45941532e-01 6.55939817e-01 7.08406270e-02 1.01780772e+00 -9.52173233e-01 6.76520586e-01 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ef2375e8-2f41-489b-af71-358ba3096188	a-semi-supervised-learning-approach-for-b	2211.14050	null	https://arxiv.org/abs/2211.14050v3	https://arxiv.org/pdf/2211.14050v3.pdf	A Semi-supervised Learning Approach for B-line Detection in Lung Ultrasound Images	Studies have proved that the number of B-lines in lung ultrasound images has a strong statistical link to the amount of extravascular lung water, which is significant for hemodialysis treatment. Manual inspection of B-lines requires experts and is time-consuming, whilst modelling automation methods is currently problematic because of a lack of ground truth. Therefore, in this paper, we propose a novel semi-supervised learning method for the B-line detection task based on contrastive learning. Through multi-level unsupervised learning on unlabelled lung ultrasound images, the features of the artefacts are learnt. In the downstream task, we introduce a fine-tuning process on a small number of labelled images using the EIoU-based loss function. Apart from reducing the data labelling workload, the proposed method shows a superior performance to model-based algorithm with the recall of 91.43%, the accuracy of 84.21% and the F1 score of 91.43%.	['Alin Achim', 'Marco Allinovi', 'Oktay Karakuş', 'Nantheera Anantrasirichai', 'Tianqi Yang']	2022-11-25	null	null	null	null	['line-detection']	['computer-vision']	[ 3.63948166e-01 2.89369702e-01 5.27572148e-02 -4.22492117e-01 -8.72394979e-01 -2.18175352e-01 3.76358658e-01 4.00482178e-01 -4.85070199e-01 7.99988449e-01 -1.89353511e-01 -3.06281507e-01 -3.17635626e-01 -6.14390910e-01 -5.02538860e-01 -8.36760461e-01 7.03349411e-02 4.32074487e-01 6.69691980e-01 5.05450487e-01 2.45856926e-01 5.33968151e-01 -1.30099857e+00 3.08252037e-01 9.08441246e-01 9.73363340e-01 4.97203916e-01 8.64697576e-01 -1.19400620e-01 9.49993372e-01 -4.05099839e-01 -2.47223093e-03 1.09751031e-01 -7.55885959e-01 -6.98050559e-01 3.12458634e-01 4.47254449e-01 -2.30057567e-01 2.94745862e-01 6.40429497e-01 7.88752675e-01 -2.59100586e-01 1.00481856e+00 -7.42493868e-01 4.97011513e-01 2.18966424e-01 -3.64326835e-01 3.28831285e-01 1.05926394e-01 1.14337981e-01 6.01066470e-01 -7.12817132e-01 3.27850610e-01 6.83527768e-01 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-6.87055111e-01 -1.14699388e+00 -6.31080568e-01 4.67691153e-01 1.24052219e-01 -6.62318468e-01 -4.87790734e-01 2.77898788e-01]	[14.844454765319824, -2.207166910171509]
1c52a4fe-e7f0-47a4-b8a8-8729a635df6a	prediction-of-cellular-burden-with-host	2004.00995	null	https://arxiv.org/abs/2004.00995v2	https://arxiv.org/pdf/2004.00995v2.pdf	Prediction of cellular burden with host-circuit models	Heterologous gene expression draws resources from host cells. These resources include vital components to sustain growth and replication, and the resulting cellular burden is a widely recognised bottleneck in the design of robust circuits. In this tutorial we discuss the use of computational models that integrate gene circuits and the physiology of host cells. Through various use cases, we illustrate the power of host-circuit models to predict the impact of design parameters on both burden and circuit functionality. Our approach relies on a new generation of computational models for microbial growth that can flexibly accommodate resource bottlenecks encountered in gene circuit design. Adoption of this modelling paradigm can facilitate fast and robust design cycles in synthetic biology.	['Diego A. Oyarzún', 'Andrea Y. Weiße', 'Evangelos-Marios Nikolados']	2020-04-02	null	null	null	null	['robust-design']	['miscellaneous']	[ 4.09916937e-01 -2.13650763e-01 1.73609808e-01 4.70185548e-01 1.36375189e-01 -1.03396666e+00 3.08015615e-01 3.48946452e-01 1.00208689e-02 1.00953853e+00 -1.25126868e-01 -5.19930840e-01 -1.80741817e-01 -7.94475973e-01 -7.46436417e-01 -7.71556616e-01 -6.19324781e-02 1.51004180e-01 -4.26942036e-02 -5.12757063e-01 3.09983224e-01 7.58587360e-01 -1.36367035e+00 1.04112715e-01 3.66194397e-01 5.87213993e-01 4.71430928e-01 1.02730024e+00 -1.00671038e-01 5.41420400e-01 -6.67410016e-01 4.01350409e-01 -8.42510238e-02 -8.31034899e-01 -3.11505556e-01 -1.31918207e-01 -7.93720186e-01 4.08154190e-01 -3.96925062e-02 2.41197068e-02 9.73091424e-01 -1.10222682e-01 8.57147217e-01 -9.93062258e-01 -3.15511882e-01 -5.12309037e-02 2.32622698e-01 -1.00926623e-01 4.26537603e-01 5.29580712e-01 3.07615042e-01 -6.71785176e-01 9.33931470e-01 9.58464265e-01 8.26267719e-01 4.45823103e-01 -1.65533125e+00 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-4.13611621e-01 -3.43294382e-01 -8.38313475e-02 -5.08177757e-01 6.47282302e-02 6.41653717e-01]	[5.716117858886719, 4.332364082336426]
aff490c0-4246-4669-8ff9-58d15905dbdb	toward-safe-and-accelerated-deep	2209.13532	null	https://arxiv.org/abs/2209.13532v1	https://arxiv.org/pdf/2209.13532v1.pdf	Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless Networks	Deep reinforcement learning (DRL) algorithms have recently gained wide attention in the wireless networks domain. They are considered promising approaches for solving dynamic radio resource management (RRM) problems in next-generation networks. Given their capabilities to build an approximate and continuously updated model of the wireless network environments, DRL algorithms can deal with the multifaceted complexity of such environments. Nevertheless, several challenges hinder the practical adoption of DRL in commercial networks. In this article, we first discuss two key practical challenges that are faced but rarely tackled when developing DRL-based RRM solutions. We argue that it is inevitable to address these DRL-related challenges for DRL to find its way to RRM commercial solutions. In particular, we discuss the need to have safe and accelerated DRL-based RRM solutions that mitigate the slow convergence and performance instability exhibited by DRL algorithms. We then review and categorize the main approaches used in the RRM domain to develop safe and accelerated DRL-based solutions. Finally, a case study is conducted to demonstrate the importance of having safe and accelerated DRL-based RRM solutions. We employ multiple variants of transfer learning (TL) techniques to accelerate the convergence of intelligent radio access network (RAN) slicing DRL-based controllers. We also propose a hybrid TL-based approach and sigmoid function-based rewards as examples of safe exploration in DRL-based RAN slicing.	['Hossam S. Hassanein', 'Hatem Abou-zeid', 'Ahmad M. Nagib']	2022-09-16	null	null	null	null	['safe-exploration']	['robots']	[-1.60783410e-01 3.10478685e-03 -6.79540873e-01 -3.79629806e-02 -5.57901621e-01 -3.09170663e-01 1.53292730e-01 -5.74848413e-01 -3.35599422e-01 1.50079703e+00 -4.06711429e-01 -8.50972652e-01 -9.93048012e-01 -8.60180020e-01 -4.01839405e-01 -5.62267184e-01 -7.17791855e-01 4.12457108e-01 -1.73254982e-02 -6.12093210e-01 -2.72313748e-02 7.42934346e-01 -1.02958286e+00 -3.08337450e-01 6.42448843e-01 1.22745609e+00 2.86272198e-01 6.65540040e-01 1.05896108e-01 8.22094917e-01 -9.55572784e-01 2.07982376e-01 4.23478246e-01 -4.16772723e-01 -8.00221562e-01 -2.19959572e-01 -4.28210855e-01 -3.87178063e-01 -1.60885721e-01 4.64313865e-01 9.41274881e-01 2.44529650e-01 3.27751994e-01 -1.63683116e+00 7.71632567e-02 9.27445710e-01 -6.62581444e-01 3.57978135e-01 1.37433171e-01 2.70783603e-02 7.19081581e-01 -3.28833938e-01 3.32678258e-01 1.31399727e+00 8.38604271e-01 5.83069444e-01 -1.13242090e+00 -8.97432864e-01 3.80500734e-01 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4ee4a1eb-4506-42c8-b250-581d2a9a50e5	overfit-neural-networks-as-a-compact-shape	2009.09808	null	https://arxiv.org/abs/2009.09808v3	https://arxiv.org/pdf/2009.09808v3.pdf	On the Effectiveness of Weight-Encoded Neural Implicit 3D Shapes	A neural implicit outputs a number indicating whether the given query point in space is inside, outside, or on a surface. Many prior works have focused on _latent-encoded_ neural implicits, where a latent vector encoding of a specific shape is also fed as input. While affording latent-space interpolation, this comes at the cost of reconstruction accuracy for any _single_ shape. Training a specific network for each 3D shape, a _weight-encoded_ neural implicit may forgo the latent vector and focus reconstruction accuracy on the details of a single shape. While previously considered as an intermediary representation for 3D scanning tasks or as a toy-problem leading up to latent-encoding tasks, weight-encoded neural implicits have not yet been taken seriously as a 3D shape representation. In this paper, we establish that weight-encoded neural implicits meet the criteria of a first-class 3D shape representation. We introduce a suite of technical contributions to improve reconstruction accuracy, convergence, and robustness when learning the signed distance field induced by a polygonal mesh -- the _de facto_ standard representation. Viewed as a lossy compression, our conversion outperforms standard techniques from geometry processing. Compared to previous latent- and weight-encoded neural implicits we demonstrate superior robustness, scalability, and performance.	['Derek Nowrouzezahrai', 'Alec Jacobson', 'Thomas Davies']	2020-09-17	on-the-effectiveness-of-weight-encoded-neural	https://openreview.net/forum?id=_QnwcbR-GG	https://openreview.net/pdf?id=_QnwcbR-GG	null	['3d-shape-representation']	['computer-vision']	[ 5.47859490e-01 2.88384974e-01 -1.16545409e-01 -3.77009243e-01 -7.89505064e-01 -2.07672849e-01 4.00437444e-01 1.57404676e-01 -1.66016400e-01 3.60864222e-01 1.75916225e-01 -4.28054303e-01 1.75484475e-02 -1.22975624e+00 -1.08301222e+00 -5.38454473e-01 -1.92938060e-01 6.75144792e-01 9.38349366e-02 -6.10615052e-02 4.88029987e-01 1.16128767e+00 -1.28327477e+00 3.72779578e-01 3.25831413e-01 1.30112922e+00 -1.77247196e-01 4.19618845e-01 -3.70328188e-01 3.29993844e-01 -2.85372585e-01 -2.96512961e-01 5.60570061e-01 7.38190487e-02 -7.48956740e-01 -1.79509401e-01 9.69232082e-01 -6.55464172e-01 -4.89990592e-01 7.08583295e-01 5.40383458e-01 -3.11107133e-02 8.91337097e-01 -8.78617704e-01 -7.88863599e-01 1.87029153e-01 -2.86902905e-01 -2.00296879e-01 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a8e39d75-11e2-4c66-a7af-584dd5cc9031	the-professional-go-annotation-dataset-page	2211.01559	null	https://arxiv.org/abs/2211.01559v1	https://arxiv.org/pdf/2211.01559v1.pdf	The ProfessionAl Go annotation datasEt (PAGE)	The game of Go has been highly under-researched due to the lack of game records and analysis tools. In recent years, the increasing number of professional competitions and the advent of AlphaZero-based algorithms provide an excellent opportunity for analyzing human Go games on a large scale. In this paper, we present the ProfessionAl Go annotation datasEt (PAGE), containing 98,525 games played by 2,007 professional players and spans over 70 years. The dataset includes rich AI analysis results for each move. Moreover, PAGE provides detailed metadata for every player and game after manual cleaning and labeling. Beyond the preliminary analysis of the dataset, we provide sample tasks that benefit from our dataset to demonstrate the potential application of PAGE in multiple research directions. To the best of our knowledge, PAGE is the first dataset with extensive annotation in the game of Go. This work is an extended version of [1] where we perform a more detailed description, analysis, and application.	['Haoyue Li', 'Danni Zhang', 'Yifan Gao']	2022-11-03	null	null	null	null	['game-of-go']	['playing-games']	[ 1.12209721e-02 4.26672995e-02 -5.07733412e-02 9.95561779e-02 -8.04465473e-01 -6.73013031e-01 2.78362453e-01 2.39488527e-01 -6.69686496e-01 6.17570341e-01 2.67908573e-01 7.68556073e-02 -3.95398825e-01 -7.28505313e-01 -4.41062003e-01 -2.24714443e-01 -1.34653136e-01 5.49123704e-01 4.99643713e-01 -6.06924117e-01 4.39722300e-01 -1.17614739e-01 -1.93353140e+00 4.03741866e-01 6.37052000e-01 8.42585802e-01 4.13294554e-01 6.70642793e-01 1.81440040e-01 1.07644665e+00 -7.22068131e-01 -7.83860862e-01 3.64771992e-01 -5.64932704e-01 -9.78461742e-01 -1.67522952e-01 2.48648360e-01 -7.63362572e-02 -3.91137213e-01 9.26645756e-01 7.28211701e-01 5.04041910e-01 4.36404534e-02 -1.40205240e+00 -2.56982088e-01 9.54426348e-01 -4.33693737e-01 4.29067731e-01 6.56393945e-01 1.72257021e-01 1.42493737e+00 -2.86091119e-01 9.98000145e-01 4.63825077e-01 8.81083310e-01 2.69402087e-01 -6.50852442e-01 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-1.24461865e+00 6.20365620e-01 -8.05137455e-01 -1.01836932e+00 -7.56284222e-02 -2.63553616e-02]	[6.517930030822754, 0.3476470410823822]
31a23f74-a38d-409a-85b7-c6ef50b5e513	alphablock-embodied-finetuning-for-vision	2305.18898	null	https://arxiv.org/abs/2305.18898v1	https://arxiv.org/pdf/2305.18898v1.pdf	AlphaBlock: Embodied Finetuning for Vision-Language Reasoning in Robot Manipulation	We propose a novel framework for learning high-level cognitive capabilities in robot manipulation tasks, such as making a smiley face using building blocks. These tasks often involve complex multi-step reasoning, presenting significant challenges due to the limited paired data connecting human instructions (e.g., making a smiley face) and robot actions (e.g., end-effector movement). Existing approaches relieve this challenge by adopting an open-loop paradigm decomposing high-level instructions into simple sub-task plans, and executing them step-by-step using low-level control models. However, these approaches are short of instant observations in multi-step reasoning, leading to sub-optimal results. To address this issue, we propose to automatically collect a cognitive robot dataset by Large Language Models (LLMs). The resulting dataset AlphaBlock consists of 35 comprehensive high-level tasks of multi-step text plans and paired observation sequences. To enable efficient data acquisition, we employ elaborated multi-round prompt designs that effectively reduce the burden of extensive human involvement. We further propose a closed-loop multi-modal embodied planning model that autoregressively generates plans by taking image observations as input. To facilitate effective learning, we leverage MiniGPT-4 with a frozen visual encoder and LLM, and finetune additional vision adapter and Q-former to enable fine-grained spatial perception for manipulation tasks. We conduct experiments to verify the superiority over existing open and closed-loop methods, and achieve a significant increase in success rate by 21.4% and 14.5% over ChatGPT and GPT-4 based robot tasks. Real-world demos are shown in https://www.youtube.com/watch?v=ayAzID1_qQk .	['Jianlong Fu', 'LiMin Wang', 'Ruihua Song', 'Bei Liu', 'Jiange Yang', 'Wenhui Tan', 'Chuhao Jin']	2023-05-30	null	null	null	null	['robot-manipulation']	['robots']	[ 1.78392753e-01 3.27653795e-01 -7.11939633e-02 -2.15690196e-01 -7.01341987e-01 -4.84779775e-01 6.40844345e-01 -2.14914620e-01 -3.77800107e-01 5.78061759e-01 3.00502360e-01 -4.79847878e-01 -2.40445286e-01 -5.16776621e-01 -1.09112227e+00 -2.82851398e-01 -6.94524869e-02 5.94367921e-01 1.32708520e-01 -5.54274499e-01 4.83666897e-01 1.57181352e-01 -1.59904671e+00 3.82605225e-01 1.04371631e+00 8.19622099e-01 1.12025642e+00 5.35867989e-01 6.75857067e-02 1.16628349e+00 -3.51718813e-01 2.11748376e-01 3.25996161e-01 -1.58751145e-01 -9.29536283e-01 1.80503637e-01 -5.02201589e-03 -6.28588855e-01 -2.38642633e-01 8.39940369e-01 3.31489354e-01 3.02755564e-01 5.46779513e-01 -1.40282595e+00 -7.72610486e-01 5.33927441e-01 -3.58907700e-01 -4.31675971e-01 9.43075240e-01 5.85748911e-01 6.62904859e-01 -8.63549888e-01 4.39269245e-01 1.52548003e+00 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f6d3fbf8-931f-4344-b7c8-17a3fcce2052	on-faking-a-nash-equilibrium	2306.08041	null	https://arxiv.org/abs/2306.08041v1	https://arxiv.org/pdf/2306.08041v1.pdf	On Faking a Nash Equilibrium	We characterize offline data poisoning attacks on Multi-Agent Reinforcement Learning (MARL), where an attacker may change a data set in an attempt to install a (potentially fictitious) unique Markov-perfect Nash equilibrium. We propose the unique Nash set, namely the set of games, specified by their Q functions, with a specific joint policy being the unique Nash equilibrium. The unique Nash set is central to poisoning attacks because the attack is successful if and only if data poisoning pushes all plausible games inside it. The unique Nash set generalizes the reward polytope commonly used in inverse reinforcement learning to MARL. For zero-sum Markov games, both the inverse Nash set and the set of plausible games induced by data are polytopes in the Q function space. We exhibit a linear program to efficiently compute the optimal poisoning attack. Our work sheds light on the structure of data poisoning attacks on offline MARL, a necessary step before one can design more robust MARL algorithms.	['Qiaomin Xie', 'Xiaojin Zhu', 'Jeremy McMahan', 'Young Wu']	2023-06-13	null	null	null	null	['data-poisoning', 'multi-agent-reinforcement-learning']	['adversarial', 'methodology']	[-3.29853833e-01 2.21303955e-01 -2.45894998e-01 5.47638297e-01 -9.39418912e-01 -1.38371420e+00 4.58202809e-01 1.30841762e-01 -9.95785832e-01 7.94964194e-01 1.21057719e-01 -4.62760359e-01 -4.56300884e-01 -1.10275233e+00 -1.04176736e+00 -1.00884736e+00 -7.07246006e-01 8.21027458e-01 1.23348951e-01 -3.79490465e-01 2.54087001e-01 4.64492500e-01 -9.04281676e-01 -1.67196527e-01 5.51817417e-01 3.03734869e-01 -1.57998756e-01 8.68396342e-01 2.54655719e-01 9.37225521e-01 -9.52935219e-01 -3.76577377e-01 7.69716322e-01 -3.95339340e-01 -8.49257112e-01 -4.14371304e-02 -3.32157195e-01 -8.66941392e-01 -5.58521390e-01 1.40746379e+00 3.59201312e-01 -5.61808497e-02 5.45499802e-01 -2.09884620e+00 -3.64427030e-01 1.19340920e+00 -6.84751630e-01 4.21330146e-02 2.87316591e-01 7.01088130e-01 1.26754868e+00 5.87703325e-02 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2a1335f0-5e7d-4e58-ab5b-1d16170de05f	machine-learning-aided-precise-indoor	2204.03990	null	https://arxiv.org/abs/2204.03990v1	https://arxiv.org/pdf/2204.03990v1.pdf	Machine Learning aided Precise Indoor Positioning	This study describes a UWB and Machine Learning (ML)-based indoor positioning system. We propose a simple mathematical strategy to create data to reduce the job of measurements for fingerprint-based indoor localization systems. A considerable number of measurements can be avoided this way. The paper compares and contrasts the performance of four distinct models. Most test locations' average error may be reduced to less than 150 mm using the best model.	['Zihuai Lin', 'Anqi Yin']	2022-04-08	null	null	null	null	['indoor-localization']	['computer-vision']	[ 1.45190164e-01 9.85705480e-02 -8.18271488e-02 -7.69207895e-01 -7.53972888e-01 -3.34910929e-01 4.42384183e-01 1.09244343e-02 -7.04138041e-01 1.40891182e+00 -3.50470126e-01 -7.07727969e-01 -6.95778728e-01 -9.55701530e-01 -3.48487645e-01 -6.71102226e-01 -2.42232800e-01 2.31155798e-01 8.24642777e-02 9.90620330e-02 3.68032008e-01 7.32009768e-01 -1.05194438e+00 -2.45388433e-01 7.67436087e-01 1.11511028e+00 2.43911102e-01 8.99809539e-01 -1.25362098e-01 3.73652041e-01 -1.10093737e+00 2.86726691e-02 2.34061792e-01 -1.68232828e-01 -3.83694232e-01 -6.69521213e-01 2.64708281e-01 -6.04890287e-02 -1.30291805e-01 5.27865410e-01 9.32775617e-01 7.45226741e-02 5.34927130e-01 -1.16220224e+00 -3.86363938e-02 6.03999376e-01 -2.11253986e-02 -8.69182572e-02 9.21919942e-01 -9.35695112e-01 -4.29615192e-02 -6.78785205e-01 1.08677708e-01 5.86037219e-01 1.53336549e+00 1.81271672e-01 -9.93191779e-01 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127582eb-c9df-438e-a91f-8dbd7563df5f	a-similarity-measure-for-material-appearance	1905.01562	null	https://arxiv.org/abs/1905.01562v1	https://arxiv.org/pdf/1905.01562v1.pdf	A Similarity Measure for Material Appearance	We present a model to measure the similarity in appearance between different materials, which correlates with human similarity judgments. We first create a database of 9,000 rendered images depicting objects with varying materials, shape and illumination. We then gather data on perceived similarity from crowdsourced experiments; our analysis of over 114,840 answers suggests that indeed a shared perception of appearance similarity exists. We feed this data to a deep learning architecture with a novel loss function, which learns a feature space for materials that correlates with such perceived appearance similarity. Our evaluation shows that our model outperforms existing metrics. Last, we demonstrate several applications enabled by our metric, including appearance-based search for material suggestions, database visualization, clustering and summarization, and gamut mapping.	['Belen Masia', 'Elena Garces', 'Manuel Lagunas', 'Ana Serrano', 'Sandra Malpica', 'Diego Gutierrez']	2019-05-04	null	null	null	null	['image-similarity-search']	['computer-vision']	[ 7.84781650e-02 -3.11271846e-01 3.66994351e-01 -6.75102472e-01 -5.88351130e-01 -8.34606349e-01 8.01436365e-01 5.61013103e-01 -1.26044929e-01 8.76503587e-02 5.13242900e-01 1.16337530e-01 -1.45316288e-01 -4.87931013e-01 -6.63853645e-01 -2.28087261e-01 1.17118977e-01 3.63762826e-01 2.37033833e-02 -1.46082461e-01 8.76373291e-01 6.30197942e-01 -1.84143877e+00 6.72552347e-01 6.22902870e-01 1.41047001e+00 6.75927103e-02 7.41885245e-01 -2.54705787e-01 7.53340244e-01 -9.45222378e-01 -6.81475282e-01 5.52321672e-01 -1.78667098e-01 -7.91501462e-01 2.61321694e-01 1.55484986e+00 -3.97067189e-01 -2.19086424e-01 8.57474446e-01 6.18862748e-01 4.30358320e-01 1.01787913e+00 -1.32975733e+00 -1.50179303e+00 1.06027082e-01 -5.91631055e-01 3.52024287e-01 1.06134558e+00 6.00220025e-01 1.22364891e+00 -1.05762160e+00 8.20874929e-01 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782ca496-8f30-44e9-a7a8-edc1d5b127b8	privacy-preserving-record-linkage-for	2301.04000	null	https://arxiv.org/abs/2301.04000v1	https://arxiv.org/pdf/2301.04000v1.pdf	Privacy-Preserving Record Linkage for Cardinality Counting	Several applications require counting the number of distinct items in the data, which is known as the cardinality counting problem. Example applications include health applications such as rare disease patients counting for adequate awareness and funding, and counting the number of cases of a new disease for outbreak detection, marketing applications such as counting the visibility reached for a new product, and cybersecurity applications such as tracking the number of unique views of social media posts. The data needed for the counting is however often personal and sensitive, and need to be processed using privacy-preserving techniques. The quality of data in different databases, for example typos, errors and variations, poses additional challenges for accurate cardinality estimation. While privacy-preserving cardinality counting has gained much attention in the recent times and a few privacy-preserving algorithms have been developed for cardinality estimation, no work has so far been done on privacy-preserving cardinality counting using record linkage techniques with fuzzy matching and provable privacy guarantees. We propose a novel privacy-preserving record linkage algorithm using unsupervised clustering techniques to link and count the cardinality of individuals in multiple datasets without compromising their privacy or identity. In addition, existing Elbow methods to find the optimal number of clusters as the cardinality are far from accurate as they do not take into account the purity and completeness of generated clusters. We propose a novel method to find the optimal number of clusters in unsupervised learning. Our experimental results on real and synthetic datasets are highly promising in terms of significantly smaller error rate of less than 0.1 with a privacy budget {\epsilon} = 1.0 compared to the state-of-the-art fuzzy matching and clustering method.	['Sanath Kumar Ramesh', 'Mohamed Ali Kaafar', 'Dinusha Vatsalan', 'Nan Wu']	2023-01-09	null	null	null	null	['marketing']	['miscellaneous']	[ 4.39138226e-02 -1.32207781e-01 -2.31046334e-01 -5.51908791e-01 -3.91860247e-01 -6.05909169e-01 1.20449208e-01 1.10016549e+00 -6.15136147e-01 8.21128070e-01 -2.87217766e-01 5.28558269e-02 -4.48714316e-01 -1.17994738e+00 -5.59374452e-01 -6.22137070e-01 -1.69873431e-01 7.34699488e-01 1.53297618e-01 2.66190559e-01 2.93065816e-01 4.22647208e-01 -1.77084708e+00 9.65143070e-02 1.06138265e+00 9.87894058e-01 -2.31008306e-01 9.08475444e-02 -2.19425231e-01 1.25248536e-01 -5.60107350e-01 -7.00256050e-01 5.46124339e-01 -7.41872117e-02 -5.34001470e-01 -1.16862878e-01 1.19460419e-01 -3.11307162e-01 2.28360206e-01 1.64230382e+00 1.49437144e-01 -9.36199874e-02 2.84697682e-01 -1.61735547e+00 -4.40487593e-01 4.75724757e-01 -9.57398951e-01 -1.89182714e-01 4.85144973e-01 -5.91813385e-01 7.05805779e-01 -2.02706978e-01 4.96447325e-01 8.92037034e-01 6.86147749e-01 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3410b928-b08b-49f4-a01c-ad8efa4eebfb	real-time-sentiment-change-detection-of	1804.00482	null	http://arxiv.org/abs/1804.00482v1	http://arxiv.org/pdf/1804.00482v1.pdf	Real Time Sentiment Change Detection of Twitter Data Streams	In the past few years, there has been a huge growth in Twitter sentiment analysis having already provided a fair amount of research on sentiment detection of public opinion among Twitter users. Given the fact that Twitter messages are generated constantly with dizzying rates, a huge volume of streaming data is created, thus there is an imperative need for accurate methods for knowledge discovery and mining of this information. Although there exists a plethora of twitter sentiment analysis methods in the recent literature, the researchers have shifted to real-time sentiment identification on twitter streaming data, as expected. A major challenge is to deal with the Big Data challenges arising in Twitter streaming applications concerning both Volume and Velocity. Under this perspective, in this paper, a methodological approach based on open source tools is provided for real-time detection of changes in sentiment that is ultra efficient with respect to both memory consumption and computational cost. This is achieved by iteratively collecting tweets in real time and discarding them immediately after their process. For this purpose, we employ the Lexicon approach for sentiment characterizations, while change detection is achieved through appropriate control charts that do not require historical information. We believe that the proposed methodology provides the trigger for a potential large-scale monitoring of threads in an attempt to discover fake news spread or propaganda efforts in their early stages. Our experimental real-time analysis based on a recent hashtag provides evidence that the proposed approach can detect meaningful sentiment changes across a hashtags lifetime.	['Vassilis P. Plagianakos', 'Aristidis G. Vrahatis', 'Spiros V. Georgakopoulos', 'Sotiris K. Tasoulis']	2018-04-02	null	null	null	null	['twitter-sentiment-analysis']	['natural-language-processing']	[ 2.01361522e-01 -1.75221682e-01 -1.82503745e-01 -2.77152896e-01 -3.99631023e-01 -7.39444733e-01 9.30805624e-01 1.11204517e+00 -6.45242572e-01 5.07709503e-01 1.10467188e-01 -2.77973503e-01 1.14578560e-01 -1.11964977e+00 -1.96575120e-01 -6.41437292e-01 -1.66348264e-01 2.10735977e-01 4.58518028e-01 -6.97021961e-01 6.44275069e-01 5.76168716e-01 -1.92014813e+00 9.45802107e-02 4.75943148e-01 1.10198689e+00 -3.23058993e-01 6.16206586e-01 -3.31807524e-01 7.17951059e-01 -8.67281795e-01 -5.80669522e-01 -4.30194242e-03 -2.03765795e-01 -5.73490262e-01 -3.11542321e-02 -1.71402171e-01 1.08203150e-01 3.68659556e-01 1.08528674e+00 4.54042792e-01 -1.71890825e-01 1.37049049e-01 -1.12503052e+00 2.46306956e-01 5.95440149e-01 -7.75500536e-01 5.43943763e-01 3.97465825e-01 -5.71890585e-02 8.52087021e-01 -5.51998258e-01 5.66737592e-01 8.05251837e-01 5.15770674e-01 -1.93675041e-01 -8.25678110e-01 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5.05970478e-01 3.82726669e-01 -4.14848596e-01 -7.47470677e-01 -2.37447545e-02]	[10.843572616577148, 6.962477207183838]
f9fc6caa-bd09-4e45-97f1-572930ecdc71	simultaneously-optimizing-perturbations-and	2212.12995	null	https://arxiv.org/abs/2212.12995v1	https://arxiv.org/pdf/2212.12995v1.pdf	Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attacks	Adversarial patch is an important form of real-world adversarial attack that brings serious risks to the robustness of deep neural networks. Previous methods generate adversarial patches by either optimizing their perturbation values while fixing the pasting position or manipulating the position while fixing the patch's content. This reveals that the positions and perturbations are both important to the adversarial attack. For that, in this paper, we propose a novel method to simultaneously optimize the position and perturbation for an adversarial patch, and thus obtain a high attack success rate in the black-box setting. Technically, we regard the patch's position, the pre-designed hyper-parameters to determine the patch's perturbations as the variables, and utilize the reinforcement learning framework to simultaneously solve for the optimal solution based on the rewards obtained from the target model with a small number of queries. Extensive experiments are conducted on the Face Recognition (FR) task, and results on four representative FR models show that our method can significantly improve the attack success rate and query efficiency. Besides, experiments on the commercial FR service and physical environments confirm its practical application value. We also extend our method to the traffic sign recognition task to verify its generalization ability.	['Bo Zhang', 'Jie Yu', 'Ying Guo', 'Xingxing Wei']	2022-12-26	null	null	null	null	['real-world-adversarial-attack', 'traffic-sign-recognition']	['adversarial', 'computer-vision']	[ 1.80329546e-01 -1.48077354e-01 2.35483218e-02 -5.88384084e-02 -8.59678388e-01 -8.40426862e-01 1.92326158e-01 -6.52560294e-01 -3.92726630e-01 5.38309634e-01 -3.68143469e-01 -3.57312083e-01 -1.48676172e-01 -8.59351158e-01 -9.73698318e-01 -1.11153400e+00 -5.54755367e-02 -4.66693453e-02 2.34731421e-01 -4.36688513e-01 2.41301313e-01 7.28531718e-01 -1.01176500e+00 -5.22886068e-02 7.17244208e-01 1.22671330e+00 -2.05106556e-01 6.04417801e-01 3.69570285e-01 8.05144668e-01 -1.33243442e+00 -6.69654310e-01 6.86660528e-01 -2.28363067e-01 -3.89566004e-01 -1.78863809e-01 4.44295973e-01 -5.92906952e-01 -7.40989685e-01 1.28327239e+00 7.94241965e-01 1.20378032e-01 2.27750167e-01 -1.49710190e+00 -5.60886562e-01 3.42221081e-01 -5.48436344e-01 2.83474803e-01 1.06777221e-01 4.68310505e-01 8.39590132e-01 -3.42935175e-01 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c2e0ccac-4ca6-44ed-a94a-9b4570ada8d4	exploring-rich-and-efficient-spatial-temporal	2008.02973	null	https://arxiv.org/abs/2008.02973v1	https://arxiv.org/pdf/2008.02973v1.pdf	Exploring Rich and Efficient Spatial Temporal Interactions for Real Time Video Salient Object Detection	The current main stream methods formulate their video saliency mainly from two independent venues, i.e., the spatial and temporal branches. As a complementary component, the main task for the temporal branch is to intermittently focus the spatial branch on those regions with salient movements. In this way, even though the overall video saliency quality is heavily dependent on its spatial branch, however, the performance of the temporal branch still matter. Thus, the key factor to improve the overall video saliency is how to further boost the performance of these branches efficiently. In this paper, we propose a novel spatiotemporal network to achieve such improvement in a full interactive fashion. We integrate a lightweight temporal model into the spatial branch to coarsely locate those spatially salient regions which are correlated with trustworthy salient movements. Meanwhile, the spatial branch itself is able to recurrently refine the temporal model in a multi-scale manner. In this way, both the spatial and temporal branches are able to interact with each other, achieving the mutual performance improvement. Our method is easy to implement yet effective, achieving high quality video saliency detection in real-time speed with 50 FPS.	['Yuming Fang', 'Dingwen Zhang', 'Chenglizhao Chen', 'Hong Qin', 'Chong Peng', 'Guotao Wang']	2020-08-07	null	null	null	null	['video-salient-object-detection', 'video-saliency-detection']	['computer-vision', 'computer-vision']	[-5.51891662e-02 -2.18695953e-01 -4.39892292e-01 -4.12344038e-02 -2.65701354e-01 -2.95705348e-01 1.90302223e-01 9.11423862e-02 -1.47417441e-01 4.91347522e-01 4.52711850e-01 1.04180530e-01 -6.87258765e-02 -8.78280103e-01 -5.64216554e-01 -6.99427724e-01 1.83704212e-01 -3.88633519e-01 1.26088023e+00 -3.96871120e-01 2.93541491e-01 3.94630767e-02 -1.51774263e+00 -2.92105712e-02 1.19215977e+00 9.77183223e-01 6.08679354e-01 2.61672944e-01 1.61993294e-03 9.03907955e-01 -9.73835662e-02 3.66630889e-02 -5.86353727e-02 -4.76014733e-01 -4.32928711e-01 -2.71215755e-02 -1.93690360e-01 -4.84263927e-01 -5.57024300e-01 1.14050722e+00 1.55248120e-01 5.97305633e-02 3.28304507e-02 -1.22813427e+00 -3.93395633e-01 7.31403649e-01 -9.45698261e-01 4.88217652e-01 2.13722393e-01 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ca96252d-b1f6-436a-8a65-8e802c6e286d	byte-pair-encoding-for-symbolic-music	2301.11975	null	https://arxiv.org/abs/2301.11975v1	https://arxiv.org/pdf/2301.11975v1.pdf	Byte Pair Encoding for Symbolic Music	The symbolic music modality is nowadays mostly represented as discrete and used with sequential models such as Transformers, for deep learning tasks. Recent research put efforts on the tokenization, i.e. the conversion of data into sequences of integers intelligible to such models. This can be achieved by many ways as music can be composed of simultaneous tracks, of simultaneous notes with several attributes. Until now, the proposed tokenizations are based on small vocabularies describing the note attributes and time events, resulting in fairly long token sequences. In this paper, we show how Byte Pair Encoding (BPE) can improve the results of deep learning models while improving its performances. We experiment on music generation and composer classification, and study the impact of BPE on how models learn the embeddings, and show that it can help to increase their isotropy, i.e., the uniformity of the variance of their positions in the space.	['Nicolas Gutowski', 'Amal El Fallah Seghrouchni', 'Fabien Chhel', 'Jean-Pierre Briot', 'Nathan Fradet']	2023-01-27	null	null	null	null	['music-generation', 'music-generation']	['audio', 'music']	[ 1.07052609e-01 -1.37380153e-01 -1.23039536e-01 -1.27473876e-01 -1.71526894e-01 -9.21097398e-01 7.13395178e-01 3.30395460e-01 -4.91192251e-01 5.44330597e-01 4.70187038e-01 -1.16200790e-01 -1.21502675e-01 -9.90543664e-01 -8.18478882e-01 -6.51840150e-01 -1.58703893e-01 4.13828820e-01 1.81680117e-02 -3.04851010e-02 1.34040732e-02 3.69411319e-01 -1.69342542e+00 3.53626430e-01 5.02836049e-01 8.86981428e-01 -9.53036360e-04 5.70975661e-01 -6.31387174e-01 8.78608763e-01 -7.83001363e-01 -6.88298821e-01 2.33782858e-01 -3.81484330e-01 -4.94262576e-01 -4.50786114e-01 2.26925686e-01 -2.35010222e-01 -3.75053376e-01 9.84590590e-01 3.36366475e-01 1.13509275e-01 7.41386116e-01 -1.36047900e+00 -6.18060827e-01 1.47749722e+00 7.77972788e-02 -3.25266480e-01 1.84935167e-01 -2.91020602e-01 1.44866848e+00 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70cf72fb-b4c1-4864-97a0-e7ce5e14a827	functional-integrative-bayesian-analysis-of	2212.14165	null	https://arxiv.org/abs/2212.14165v1	https://arxiv.org/pdf/2212.14165v1.pdf	Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Genomic Data	Rapid advancements in collection and dissemination of multi-platform molecular and genomics data has resulted in enormous opportunities to aggregate such data in order to understand, prevent, and treat human diseases. While significant improvements have been made in multi-omic data integration methods to discover biological markers and mechanisms underlying both prognosis and treatment, the precise cellular functions governing these complex mechanisms still need detailed and data-driven de-novo evaluations. We propose a framework called Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Genomic Data (fiBAG), that allows simultaneous identification of upstream functional evidence of proteogenomic biomarkers and the incorporation of such knowledge in Bayesian variable selection models to improve signal detection. fiBAG employs a conflation of Gaussian process models to quantify (possibly non-linear) functional evidence via Bayes factors, which are then mapped to a novel calibrated spike-and-slab prior, thus guiding selection and providing functional relevance to the associations with patient outcomes. Using simulations, we illustrate how integrative methods with functional calibration have higher power to detect disease related markers than non-integrative approaches. We demonstrate the profitability of fiBAG via a pan-cancer analysis of 14 cancer types to identify and assess the cellular mechanisms of proteogenomic markers associated with cancer stemness and patient survival.	['Veerabhadran Baladandayuthapani', 'Nicholas Henderson', 'Rupam Bhattacharyya']	2022-12-29	null	null	null	null	['data-integration', 'variable-selection']	['knowledge-base', 'methodology']	[ 4.54288274e-01 -4.31186080e-01 -4.20660406e-01 -3.64210457e-01 -9.73948240e-01 -3.99612010e-01 2.82333970e-01 7.10635602e-01 -2.73887664e-01 8.44549835e-01 4.15387571e-01 -2.15852097e-01 -7.00457335e-01 -6.99702561e-01 -4.14546549e-01 -1.10045755e+00 -2.06136391e-01 6.80754781e-01 -2.33634822e-02 3.58233005e-01 5.25375381e-02 3.47247422e-01 -1.17024887e+00 1.16568267e-01 9.09207165e-01 4.63544548e-01 1.01138264e-01 7.04563022e-01 6.13178760e-02 -3.98596615e-01 -1.54834598e-01 -2.19171345e-01 -1.99875072e-01 -2.88571447e-01 1.25697888e-02 -3.54114175e-01 -2.09957823e-01 2.08721593e-01 1.47338822e-01 8.52958798e-01 7.31167376e-01 -4.10955250e-01 8.79505336e-01 -9.65447307e-01 -2.53839880e-01 3.47166836e-01 -5.31673491e-01 -2.72131469e-02 2.80424561e-02 3.01476866e-01 9.88371372e-01 -6.81539357e-01 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1.74520001e-01 -5.16979992e-01 -4.09044594e-01 -8.65095675e-01 -6.53062940e-01 -1.68488815e-01 -1.89771980e-01 -5.88783264e-01 -2.55232066e-01 -2.57755406e-02]	[6.563896179199219, 5.415600776672363]
fc63aede-3ea5-4cb9-96b9-d7a94c32c8de	a-cloud-based-machine-learning-pipeline-for	2306.07786	null	https://arxiv.org/abs/2306.07786v2	https://arxiv.org/pdf/2306.07786v2.pdf	A Cloud-based Machine Learning Pipeline for the Efficient Extraction of Insights from Customer Reviews	The efficiency of natural language processing has improved dramatically with the advent of machine learning models, particularly neural network-based solutions. However, some tasks are still challenging, especially when considering specific domains. In this paper, we present a cloud-based system that can extract insights from customer reviews using machine learning methods integrated into a pipeline. For topic modeling, our composite model uses transformer-based neural networks designed for natural language processing, vector embedding-based keyword extraction, and clustering. The elements of our model have been integrated and further developed to meet better the requirements of efficient information extraction, topic modeling of the extracted information, and user needs. Furthermore, our system can achieve better results than this task's existing topic modeling and keyword extraction solutions. Our approach is validated and compared with other state-of-the-art methods using publicly available datasets for benchmarking.	['Andras Hajdu', 'Marianna Szabo', 'Janos Toth', 'Attila Tiba', 'Istvan Lakatos', 'Balazs Harangi', 'Gergo Bogacsovics', 'Robert Lakatos']	2023-06-13	null	null	null	null	['keyword-extraction']	['natural-language-processing']	[-2.69157588e-01 1.87446237e-01 -3.17076951e-01 -4.06140089e-01 -7.70460963e-01 -3.46408278e-01 6.33959949e-01 7.29109585e-01 -4.75723803e-01 2.82255054e-01 3.31448972e-01 -4.39598620e-01 -1.18385926e-01 -9.85673785e-01 -2.34040156e-01 -7.38700181e-02 1.73091650e-01 7.59497702e-01 1.68643799e-02 -1.49656668e-01 4.71422493e-01 2.89061338e-01 -1.63392198e+00 6.41854763e-01 7.11868346e-01 1.14750803e+00 1.58847079e-01 4.66897339e-01 -9.12492275e-01 6.76993370e-01 -4.45321918e-01 -2.67345846e-01 8.78240168e-02 5.07171392e-01 -6.25921190e-01 -1.09495074e-01 1.20910302e-01 -2.18317032e-01 -3.40796560e-02 6.47459269e-01 2.27538958e-01 1.32986024e-01 5.89047849e-01 -1.20393550e+00 -4.46112126e-01 8.79630327e-01 -1.17021814e-01 -3.14113021e-01 1.75889149e-01 -3.24939698e-01 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a8e809ea-021f-466b-88da-a354bc09a14f	cleansing-jewel-a-neural-spelling-correction	2304.03427	null	https://arxiv.org/abs/2304.03427v1	https://arxiv.org/pdf/2304.03427v1.pdf	Cleansing Jewel: A Neural Spelling Correction Model Built On Google OCR-ed Tibetan Manuscripts	Scholars in the humanities rely heavily on ancient manuscripts to study history, religion, and socio-political structures in the past. Many efforts have been devoted to digitizing these precious manuscripts using OCR technology, but most manuscripts were blemished over the centuries so that an Optical Character Recognition (OCR) program cannot be expected to capture faded graphs and stains on pages. This work presents a neural spelling correction model built on Google OCR-ed Tibetan Manuscripts to auto-correct OCR-ed noisy output. This paper is divided into four sections: dataset, model architecture, training and analysis. First, we feature-engineered our raw Tibetan etext corpus into two sets of structured data frames -- a set of paired toy data and a set of paired real data. Then, we implemented a Confidence Score mechanism into the Transformer architecture to perform spelling correction tasks. According to the Loss and Character Error Rate, our Transformer + Confidence score mechanism architecture proves to be superior to Transformer, LSTM-2-LSTM and GRU-2-GRU architectures. Finally, to examine the robustness of our model, we analyzed erroneous tokens, visualized Attention and Self-Attention heatmaps in our model.	['Yung-Sung Chuang', 'Queenie Luo']	2023-04-07	null	null	null	null	['optical-character-recognition', 'spelling-correction']	['computer-vision', 'natural-language-processing']	[ 3.02651614e-01 -9.29059312e-02 4.22019243e-01 -1.54269710e-01 -5.65735936e-01 -4.25911039e-01 6.86837196e-01 1.42718777e-02 -4.41485852e-01 8.13642204e-01 1.10118993e-01 -3.65820169e-01 1.73489600e-01 -7.98221052e-01 -8.72810304e-01 -2.94823140e-01 4.23971489e-02 4.25194412e-01 1.71441481e-01 -3.90977085e-01 8.02423120e-01 3.66161197e-01 -1.33397317e+00 3.70388955e-01 1.22710109e+00 6.44512892e-01 2.27507696e-01 1.01595581e+00 -2.53423691e-01 8.31176162e-01 -9.44534719e-01 -9.22705531e-01 -1.33206069e-01 -3.36028486e-01 -5.99243641e-01 -3.14348966e-01 7.63348699e-01 -6.36250824e-02 -6.63480520e-01 1.22194219e+00 4.25631195e-01 -3.38794887e-02 7.22872376e-01 -5.44736087e-01 -1.65012932e+00 6.97045565e-01 -2.09245160e-01 3.28201771e-01 1.05298869e-01 -1.15056112e-01 7.48523831e-01 -1.02395022e+00 6.27770483e-01 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207e1d94-3c5a-4a5b-a642-760c72ea61af	3d-human-mesh-estimation-from-virtual-markers-1	2303.11726	null	https://arxiv.org/abs/2303.11726v2	https://arxiv.org/pdf/2303.11726v2.pdf	3D Human Mesh Estimation from Virtual Markers	Inspired by the success of volumetric 3D pose estimation, some recent human mesh estimators propose to estimate 3D skeletons as intermediate representations, from which, the dense 3D meshes are regressed by exploiting the mesh topology. However, body shape information is lost in extracting skeletons, leading to mediocre performance. The advanced motion capture systems solve the problem by placing dense physical markers on the body surface, which allows to extract realistic meshes from their non-rigid motions. However, they cannot be applied to wild images without markers. In this work, we present an intermediate representation, named virtual markers, which learns 64 landmark keypoints on the body surface based on the large-scale mocap data in a generative style, mimicking the effects of physical markers. The virtual markers can be accurately detected from wild images and can reconstruct the intact meshes with realistic shapes by simple interpolation. Our approach outperforms the state-of-the-art methods on three datasets. In particular, it surpasses the existing methods by a notable margin on the SURREAL dataset, which has diverse body shapes. Code is available at https://github.com/ShirleyMaxx/VirtualMarker.	['Yizhou Wang', 'Wentao Zhu', 'Chunyu Wang', 'Jiajun Su', 'Xiaoxuan Ma']	2023-03-21	3d-human-mesh-estimation-from-virtual-markers	http://openaccess.thecvf.com//content/CVPR2023/html/Ma_3D_Human_Mesh_Estimation_From_Virtual_Markers_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Ma_3D_Human_Mesh_Estimation_From_Virtual_Markers_CVPR_2023_paper.pdf	cvpr-2023-1	['3d-pose-estimation', '3d-human-pose-estimation']	['computer-vision', 'computer-vision']	[-1.02754742e-01 2.04339251e-01 -2.14653179e-01 1.31223977e-01 -5.75591683e-01 -3.60535979e-01 4.70478535e-01 -1.87041193e-01 -2.31970206e-01 6.38716936e-01 1.01588421e-01 4.74682838e-01 1.66396365e-01 -7.76164770e-01 -1.02063835e+00 -4.32970762e-01 -5.15321828e-02 8.16309988e-01 4.19874102e-01 -1.39901951e-01 -9.11275148e-02 4.10056442e-01 -1.41900516e+00 -2.52790153e-01 7.97777295e-01 8.49411428e-01 -7.72488117e-02 3.99733603e-01 1.55134469e-01 2.63553232e-01 -2.27488637e-01 -6.05256736e-01 2.15773582e-01 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fb0244d4-93fa-4703-ba26-7fa1605642a2	fdsnet-finger-dorsal-image-spoof-detection	1812.07444	null	http://arxiv.org/abs/1812.07444v1	http://arxiv.org/pdf/1812.07444v1.pdf	FDSNet: Finger dorsal image spoof detection network using light field camera	At present spoofing attacks via which biometric system is potentially vulnerable against a fake biometric characteristic, introduces a great challenge to recognition performance. Despite the availability of a broad range of presentation attack detection (PAD) or liveness detection algorithms, fingerprint sensors are vulnerable to spoofing via fake fingers. In such situations, finger dorsal images can be thought of as an alternative which can be captured without much user cooperation and are more appropriate for outdoor security applications. In this paper, we present a first feasibility study of spoofing attack scenarios on finger dorsal authentication system, which include four types of presentation attacks such as printed paper, wrapped printed paper, scan and mobile. This study also presents a CNN based spoofing attack detection method which employ state-of-the-art deep learning techniques along with transfer learning mechanism. We have collected 196 finger dorsal real images from 33 subjects, captured with a Lytro camera and also created a set of 784 finger dorsal spoofing images. Extensive experimental results have been performed that demonstrates the superiority of the proposed approach for various spoofing attacks.	['Aditya Nigam', 'Avantika Singh', 'Gaurav Jaswal']	2018-12-18	null	null	null	null	['finger-dorsal-image-spoof-detection']	['computer-vision']	[ 8.20384145e-01 -5.06571114e-01 -4.41060634e-03 -2.95571461e-02 -9.09011438e-02 -7.65868425e-01 6.97204769e-01 -3.70249152e-01 -3.33526820e-01 5.00198483e-01 -1.50629893e-01 -4.78377938e-01 -1.12555169e-01 -7.40117311e-01 -5.68426669e-01 -4.72187102e-01 -1.99712306e-01 -6.36791810e-03 1.05869129e-01 -3.38118076e-01 6.03387535e-01 7.75134087e-01 -1.19080687e+00 5.34732640e-01 3.28761816e-01 1.01811910e+00 -6.16370380e-01 9.02415812e-01 1.14177547e-01 -1.13669828e-01 -9.62393105e-01 -1.01287580e+00 4.94432658e-01 4.50681597e-02 -3.80784810e-01 -3.93468082e-01 9.69339609e-01 -8.67677450e-01 -6.62160635e-01 1.13026226e+00 8.52457821e-01 -5.36367416e-01 5.09555519e-01 -1.34731090e+00 -8.15657258e-01 2.57247180e-01 -7.35871315e-01 1.64274231e-01 7.78002024e-01 9.32331234e-02 -8.47406685e-03 -6.10827208e-01 2.29102120e-01 1.40694261e+00 8.53939176e-01 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44354cd7-4a03-4924-94f7-a3212c8ee92a	p2p-tuning-pre-trained-image-models-for-point	2208.02812	null	https://arxiv.org/abs/2208.02812v2	https://arxiv.org/pdf/2208.02812v2.pdf	P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting	Nowadays, pre-training big models on large-scale datasets has become a crucial topic in deep learning. The pre-trained models with high representation ability and transferability achieve a great success and dominate many downstream tasks in natural language processing and 2D vision. However, it is non-trivial to promote such a pretraining-tuning paradigm to the 3D vision, given the limited training data that are relatively inconvenient to collect. In this paper, we provide a new perspective of leveraging pre-trained 2D knowledge in 3D domain to tackle this problem, tuning pre-trained image models with the novel Point-to-Pixel prompting for point cloud analysis at a minor parameter cost. Following the principle of prompting engineering, we transform point clouds into colorful images with geometry-preserved projection and geometry-aware coloring to adapt to pre-trained image models, whose weights are kept frozen during the end-to-end optimization of point cloud analysis tasks. We conduct extensive experiments to demonstrate that cooperating with our proposed Point-to-Pixel Prompting, better pre-trained image model will lead to consistently better performance in 3D vision. Enjoying prosperous development from image pre-training field, our method attains 89.3% accuracy on the hardest setting of ScanObjectNN, surpassing conventional point cloud models with much fewer trainable parameters. Our framework also exhibits very competitive performance on ModelNet classification and ShapeNet Part Segmentation. Code is available at https://github.com/wangzy22/P2P.	['Jiwen Lu', 'Jie zhou', 'Yongming Rao', 'Xumin Yu', 'Ziyi Wang']	2022-08-04	null	null	null	null	['3d-point-cloud-classification', '3d-part-segmentation']	['computer-vision', 'computer-vision']	[ 5.86528592e-02 1.25615671e-02 -7.70327635e-03 -5.85003734e-01 -8.35022628e-01 -6.25037789e-01 4.91923720e-01 -2.03998491e-01 -4.03471261e-01 7.36527741e-02 -5.22291541e-01 -5.00010133e-01 5.20697311e-02 -8.27067435e-01 -1.17338359e+00 -5.70960879e-01 3.17692161e-01 6.83393002e-01 2.10358009e-01 -2.23092109e-01 2.04144359e-01 8.71994853e-01 -1.49225020e+00 5.62951416e-02 9.05923069e-01 1.11404955e+00 6.87645197e-01 4.17827785e-01 -4.31427091e-01 3.14914584e-01 -1.17586307e-01 -4.08506155e-01 6.55316055e-01 2.95333624e-01 -6.54770732e-01 3.16045940e-01 7.71794617e-01 -3.02656174e-01 -1.82306413e-02 1.14817452e+00 4.14682448e-01 -1.09074093e-01 4.73095834e-01 -1.01083398e+00 -8.51411819e-01 2.11915791e-01 -9.13787186e-01 -1.96263343e-02 -1.83097258e-01 4.13961679e-01 8.66208136e-01 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efc077b4-031f-4e6d-aef1-807662ee90f7	contrastive-enhanced-slide-filter-mixer-for	2305.04322	null	https://arxiv.org/abs/2305.04322v1	https://arxiv.org/pdf/2305.04322v1.pdf	Contrastive Enhanced Slide Filter Mixer for Sequential Recommendation	Sequential recommendation (SR) aims to model user preferences by capturing behavior patterns from their item historical interaction data. Most existing methods model user preference in the time domain, omitting the fact that users' behaviors are also influenced by various frequency patterns that are difficult to separate in the entangled chronological items. However, few attempts have been made to train SR in the frequency domain, and it is still unclear how to use the frequency components to learn an appropriate representation for the user. To solve this problem, we shift the viewpoint to the frequency domain and propose a novel Contrastive Enhanced \textbf{SLI}de Filter \textbf{M}ixEr for Sequential \textbf{Rec}ommendation, named \textbf{SLIME4Rec}. Specifically, we design a frequency ramp structure to allow the learnable filter slide on the frequency spectrums across different layers to capture different frequency patterns. Moreover, a Dynamic Frequency Selection (DFS) and a Static Frequency Split (SFS) module are proposed to replace the self-attention module for effectively extracting frequency information in two ways. DFS is used to select helpful frequency components dynamically, and SFS is combined with the dynamic frequency selection module to provide a more fine-grained frequency division. Finally, contrastive learning is utilized to improve the quality of user embedding learned from the frequency domain. Extensive experiments conducted on five widely used benchmark datasets demonstrate our proposed model performs significantly better than the state-of-the-art approaches. Our code is available at https://github.com/sudaada/SLIME4Rec.	['Xiaofang Zhou', 'Victor S. Sheng', 'Yanchi Liu', 'Guanfeng Liu', 'Junhua Fang', 'Pengpeng Zhao', 'Huanhuan Yuan', 'Xinyu Du']	2023-05-07	null	null	null	null	['sequential-recommendation']	['miscellaneous']	[ 7.78385848e-02 -6.74043238e-01 -7.05837011e-01 -5.01985490e-01 -2.14228675e-01 -5.41346848e-01 3.90953332e-01 9.39385593e-02 -4.31744963e-01 3.30570281e-01 5.44532657e-01 -2.21890360e-01 -4.80626911e-01 -7.27659523e-01 -4.88748223e-01 -7.43832231e-01 -1.50063023e-01 -6.45132139e-02 5.76327406e-02 -3.76406193e-01 1.91541865e-01 -3.33631895e-02 -1.54183137e+00 4.10660535e-01 1.00922310e+00 1.15096426e+00 3.50264251e-01 2.43600905e-01 -2.31542647e-01 3.03069562e-01 -4.15727526e-01 -2.24306551e-03 1.78824246e-01 -4.38598365e-01 -6.00801647e-01 -2.99570382e-01 1.20070770e-01 -2.28917316e-01 -5.15567601e-01 1.02285564e+00 4.23738360e-01 5.79768002e-01 5.68493187e-01 -9.26925361e-01 -1.02684689e+00 1.01130557e+00 -6.31240070e-01 6.53899491e-01 5.45306921e-01 -1.75302401e-02 1.31516576e+00 -7.87284613e-01 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1.04885399e+00 -8.06514084e-01 -6.42575622e-01 -9.67680275e-01 -7.06621528e-01 5.63068807e-01 5.74383438e-01 -8.38649333e-01 -4.37114120e-01 -1.91311643e-01]	[10.139878273010254, 5.565402507781982]
5571b2ec-0770-43b8-a000-f9b635623fa7	neural-time-warping-for-multiple-sequence	2006.15753	null	https://arxiv.org/abs/2006.15753v1	https://arxiv.org/pdf/2006.15753v1.pdf	Neural Time Warping For Multiple Sequence Alignment	Multiple sequences alignment (MSA) is a traditional and challenging task for time-series analyses. The MSA problem is formulated as a discrete optimization problem and is typically solved by dynamic programming. However, the computational complexity increases exponentially with respect to the number of input sequences. In this paper, we propose neural time warping (NTW) that relaxes the original MSA to a continuous optimization and obtains the alignments using a neural network. The solution obtained by NTW is guaranteed to be a feasible solution for the original discrete optimization problem under mild conditions. Our experimental results show that NTW successfully aligns a hundred time-series and significantly outperforms existing methods for solving the MSA problem. In addition, we show a method for obtaining average time-series data as one of applications of NTW. Compared to the existing barycenters, the mean time series data retains the features of the input time-series data.	['Keisuke Kawano', 'Takuro Kutsuna', 'Satoshi Koide']	2020-06-29	null	null	null	null	['multiple-sequence-alignment']	['medical']	[ 6.35993719e-01 -4.41371799e-01 -2.58713037e-01 -3.02442729e-01 -8.39902043e-01 -6.86199844e-01 1.96592063e-01 1.08034974e-02 -4.38994765e-01 7.22747147e-01 -3.44648585e-02 -5.10270655e-01 -2.90598840e-01 -4.81565535e-01 -7.44199634e-01 -9.15748775e-01 -4.25701380e-01 4.88632619e-01 4.66542207e-02 -4.60626960e-01 3.40584606e-01 7.54717171e-01 -1.18937242e+00 -9.99907628e-02 9.44620669e-01 7.45863497e-01 2.37513244e-01 6.52701080e-01 -7.40762874e-02 2.68362999e-01 -7.11888313e-01 3.89238298e-02 6.86498284e-01 -5.01361191e-01 -7.05947399e-01 -2.18650904e-02 2.46338934e-01 -1.30868018e-01 -2.26207420e-01 9.20301139e-01 3.85935277e-01 4.74907100e-01 2.55244970e-01 -1.79278696e+00 -3.70771796e-01 4.03482050e-01 -7.51338065e-01 3.16299468e-01 1.45282343e-01 -2.03218564e-01 9.15605009e-01 -5.53801835e-01 6.31966710e-01 8.66481125e-01 9.61814106e-01 1.12031519e-01 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-5.58316052e-01 -2.67445803e-01 2.73606598e-01 5.93343854e-01 -2.96473116e-01 -3.38804781e-01 3.34418237e-01]	[7.356597900390625, 3.378730297088623]
ac75ca6b-557c-4330-81ad-1951bbd74836	algorithms-incentives-and-democracy	2307.02319	null	https://arxiv.org/abs/2307.02319v1	https://arxiv.org/pdf/2307.02319v1.pdf	Algorithms, Incentives, and Democracy	Classification algorithms are increasingly used in areas such as housing, credit, and law enforcement in order to make decisions affecting peoples' lives. These algorithms can change individual behavior deliberately (a fraud prediction algorithm deterring fraud) or inadvertently (content sorting algorithms spreading misinformation), and they are increasingly facing public scrutiny and regulation. Some of these regulations, like the elimination of cash bail in some states, have focused on \textit{lowering the stakes of certain classifications}. In this paper we characterize how optimal classification by an algorithm designer can affect the distribution of behavior in a population -- sometimes in surprising ways. We then look at the effect of democratizing the rewards and punishments, or stakes, to algorithmic classification to consider how a society can potentially stem (or facilitate!) predatory classification. Our results speak to questions of algorithmic fairness in settings where behavior and algorithms are interdependent, and where typical measures of fairness focusing on statistical accuracy across groups may not be appropriate.	['John W. Patty', 'Elizabeth Maggie Penn']	2023-07-05	null	null	null	null	['fairness', 'classification-1', 'fairness', 'misinformation']	['computer-vision', 'methodology', 'miscellaneous', 'miscellaneous']	[ 3.81393492e-01 1.66652590e-01 -4.25673336e-01 -5.66785932e-01 -1.13723643e-01 -5.80906034e-01 4.09485012e-01 6.27367854e-01 -9.46523845e-01 9.65220451e-01 3.27288300e-01 -5.97158611e-01 -8.80356282e-02 -9.08228338e-01 -3.55339170e-01 -4.84045863e-01 3.45442921e-01 2.47729346e-01 -2.57823139e-01 1.25470579e-01 9.16060567e-01 3.52743149e-01 -1.36675072e+00 1.08779088e-01 1.09746134e+00 5.22160053e-01 -6.71832442e-01 4.11573321e-01 1.37134781e-02 1.02576709e+00 -5.63044190e-01 -1.02942944e+00 5.38894653e-01 -5.21684885e-01 -5.50472736e-01 5.07242698e-03 4.53899056e-01 -6.54913306e-01 -6.09992780e-02 1.35797358e+00 1.44034371e-01 -2.72428602e-01 9.09662187e-01 -1.41705871e+00 -8.74228776e-01 9.16149318e-01 -8.51366460e-01 2.02385798e-01 1.80738926e-01 3.89145702e-01 1.14568603e+00 1.35191783e-01 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5.73882878e-01 -1.61130950e-01 -9.56185341e-01 -3.89225364e-01 -1.05408251e+00 6.68250084e-01 2.14045256e-01 -4.46341306e-01 -7.87178218e-01 -2.77265549e-01]	[8.86113166809082, 5.453906536102295]
0388c80a-4907-4cd3-84c1-ed7bcd440e8d	qaid-question-answering-inspired-few-shot	2303.01593	null	https://arxiv.org/abs/2303.01593v2	https://arxiv.org/pdf/2303.01593v2.pdf	QAID: Question Answering Inspired Few-shot Intent Detection	Intent detection with semantically similar fine-grained intents is a challenging task. To address it, we reformulate intent detection as a question-answering retrieval task by treating utterances and intent names as questions and answers. To that end, we utilize a question-answering retrieval architecture and adopt a two stages training schema with batch contrastive loss. In the pre-training stage, we improve query representations through self-supervised training. Then, in the fine-tuning stage, we increase contextualized token-level similarity scores between queries and answers from the same intent. Our results on three few-shot intent detection benchmarks achieve state-of-the-art performance.	['Boaz Carmeli', 'Doron Cohen', 'Koren Lazar', 'Yosi Mass', 'Matan Vetzler', 'Asaf Yehudai']	2023-03-02	null	null	null	null	['intent-detection']	['natural-language-processing']	[ 2.27470726e-01 -3.45723689e-01 -3.38413268e-01 -6.87526584e-01 -1.23851144e+00 -3.83213490e-01 7.94635236e-01 3.25825870e-01 -6.51923895e-01 3.13875496e-01 7.75986850e-01 -5.44562712e-02 7.49447122e-02 -6.11436844e-01 -2.16680780e-01 -3.97437923e-02 1.69221580e-01 4.59304124e-01 4.94704187e-01 -4.93011594e-01 6.61975026e-01 -1.86256975e-01 -1.34711492e+00 6.86245382e-01 8.45518708e-01 1.14542615e+00 -1.14149660e-01 7.66514301e-01 -5.66559017e-01 1.45524228e+00 -5.65043747e-01 -4.80749667e-01 -2.30765820e-01 -4.76165116e-01 -1.25871682e+00 -8.23466331e-02 5.42778134e-01 -5.98493874e-01 -4.58254695e-01 7.70460188e-01 1.61044285e-01 5.81762552e-01 8.23632717e-01 -9.79832411e-01 -9.90349412e-01 2.26619095e-01 -2.03175709e-01 4.35934335e-01 8.81944239e-01 1.64568394e-01 1.58347058e+00 -1.13389099e+00 3.69800687e-01 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7f879c18-2812-4981-ba62-ea626c9eb08c	sign-language-recognition-via-skeleton-aware	2110.06161	null	https://arxiv.org/abs/2110.06161v1	https://arxiv.org/pdf/2110.06161v1.pdf	Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble	Sign language is commonly used by deaf or mute people to communicate but requires extensive effort to master. It is usually performed with the fast yet delicate movement of hand gestures, body posture, and even facial expressions. Current Sign Language Recognition (SLR) methods usually extract features via deep neural networks and suffer overfitting due to limited and noisy data. Recently, skeleton-based action recognition has attracted increasing attention due to its subject-invariant and background-invariant nature, whereas skeleton-based SLR is still under exploration due to the lack of hand annotations. Some researchers have tried to use off-line hand pose trackers to obtain hand keypoints and aid in recognizing sign language via recurrent neural networks. Nevertheless, none of them outperforms RGB-based approaches yet. To this end, we propose a novel Skeleton Aware Multi-modal Framework with a Global Ensemble Model (GEM) for isolated SLR (SAM-SLR-v2) to learn and fuse multi-modal feature representations towards a higher recognition rate. Specifically, we propose a Sign Language Graph Convolution Network (SL-GCN) to model the embedded dynamics of skeleton keypoints and a Separable Spatial-Temporal Convolution Network (SSTCN) to exploit skeleton features. The skeleton-based predictions are fused with other RGB and depth based modalities by the proposed late-fusion GEM to provide global information and make a faithful SLR prediction. Experiments on three isolated SLR datasets demonstrate that our proposed SAM-SLR-v2 framework is exceedingly effective and achieves state-of-the-art performance with significant margins. Our code will be available at https://github.com/jackyjsy/SAM-SLR-v2	['Yun Fu', 'Kunpeng Li', 'Yue Bai', 'Lichen Wang', 'Bin Sun', 'Songyao Jiang']	2021-10-12	null	null	null	null	['sign-language-recognition']	['computer-vision']	[ 1.09722130e-01 -4.72645789e-01 -2.85942346e-01 -2.40517244e-01 -9.53973114e-01 -1.63374484e-01 4.91726726e-01 -8.89071643e-01 -3.96745980e-01 3.79355401e-01 5.01891494e-01 8.91465619e-02 -1.35956123e-01 -3.87835473e-01 -3.25231105e-01 -9.34788525e-01 2.63990998e-01 1.07216887e-01 3.07039231e-01 -3.18937510e-01 2.89522689e-02 8.45018983e-01 -1.72217476e+00 2.21746881e-02 7.14982808e-01 7.99771965e-01 7.20092878e-02 7.26567805e-01 -6.17346093e-02 9.30261135e-01 -1.89044803e-01 -1.33890301e-01 3.29047501e-01 -4.26263690e-01 -4.60749328e-01 -2.81585976e-02 4.72721457e-01 -5.28678000e-01 -9.24026489e-01 8.40236306e-01 9.71186280e-01 2.62409568e-01 3.88729304e-01 -1.14196062e+00 -3.39864254e-01 4.57828976e-02 -7.16137528e-01 -3.00992966e-01 4.23609406e-01 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01b19d6f-ddcc-4853-a60d-7f3ea620d1c0	context-aware-retail-product-recommendation	2109.08561	null	https://arxiv.org/abs/2109.08561v1	https://arxiv.org/pdf/2109.08561v1.pdf	Context-aware Retail Product Recommendation with Regularized Gradient Boosting	In the FARFETCH Fashion Recommendation challenge, the participants needed to predict the order in which various products would be shown to a user in a recommendation impression. The data was provided in two phases - a validation phase and a test phase. The validation phase had a labelled training set that contained a binary column indicating whether a product has been clicked or not. The dataset comprises over 5,000,000 recommendation events, 450,000 products and 230,000 unique users. It represents real, unbiased, but anonymised, interactions of actual users of the FARFETCH platform. The final evaluation was done according to the performance in the second phase. A total of 167 participants participated in the challenge, and we secured the 6th rank during the final evaluation with an MRR of 0.4658 on the test set. We have designed a unique context-aware system that takes the similarity of a product to the user context into account to rank products more effectively. Post evaluation, we have been able to fine-tune our approach with an MRR of 0.4784 on the test set, which would have placed us at the 3rd position.	['Ayan Basak', 'Sourya Dipta Das']	2021-09-17	null	null	null	null	['product-recommendation', 'context-aware-product-recommendation']	['miscellaneous', 'miscellaneous']	[ 8.23047087e-02 5.83095178e-02 1.65606178e-02 -8.81411850e-01 -2.82277524e-01 -1.00333667e+00 6.77593052e-01 2.16273993e-01 -4.92724299e-01 5.02221227e-01 2.84938693e-01 -1.41543418e-01 -2.43292123e-01 -7.85603762e-01 -5.63309491e-01 -2.64265478e-01 -2.65202671e-01 6.81701720e-01 1.92236692e-01 2.80216653e-02 3.46997172e-01 9.55893248e-02 -1.76598120e+00 8.98867667e-01 4.84662324e-01 1.21510577e+00 -5.21542616e-02 8.27867448e-01 4.44699079e-01 2.77589709e-01 -6.75699055e-01 -9.16621089e-01 6.23063684e-01 -1.01864412e-01 -5.82295239e-01 -2.96766579e-01 7.09517896e-01 -3.42206955e-01 -1.10825479e-01 7.81132877e-01 4.51957077e-01 4.59394693e-01 4.17055577e-01 -1.06537271e+00 -5.37798941e-01 8.74127150e-01 -9.04583335e-02 -1.20399632e-01 8.06231678e-01 -7.88588673e-02 1.30194581e+00 -7.14769900e-01 8.62542748e-01 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e2ba8662-c75e-45fa-b48f-126a4c4f5ff6	mrsn-multi-relation-support-network-for-video	2304.11975	null	https://arxiv.org/abs/2304.11975v1	https://arxiv.org/pdf/2304.11975v1.pdf	MRSN: Multi-Relation Support Network for Video Action Detection	Action detection is a challenging video understanding task, requiring modeling spatio-temporal and interaction relations. Current methods usually model actor-actor and actor-context relations separately, ignoring their complementarity and mutual support. To solve this problem, we propose a novel network called Multi-Relation Support Network (MRSN). In MRSN, Actor-Context Relation Encoder (ACRE) and Actor-Actor Relation Encoder (AARE) model the actor-context and actor-actor relation separately. Then Relation Support Encoder (RSE) computes the supports between the two relations and performs relation-level interactions. Finally, Relation Consensus Module (RCM) enhances two relations with the long-term relations from the Long-term Relation Bank (LRB) and yields a consensus. Our experiments demonstrate that modeling relations separately and performing relation-level interactions can achieve and outperformer state-of-the-art results on two challenging video datasets: AVA and UCF101-24.	['Tong Lu', 'Minglei Yuan', 'Guo Chen', 'Yin-Dong Zheng']	2023-04-24	null	null	null	null	['video-understanding']	['computer-vision']	[ 2.90938437e-01 2.86048412e-01 -7.51062930e-01 -6.53047383e-01 -1.27045929e-01 -2.44897947e-01 9.35698986e-01 8.10645074e-02 -1.21463582e-01 5.73412657e-01 7.83269346e-01 -1.17962502e-01 -4.37296748e-01 -6.79470420e-01 -6.96978986e-01 -1.26920408e-04 -4.43148434e-01 3.23144227e-01 6.74282610e-01 -2.47526675e-01 -2.33701095e-01 1.92837626e-01 -1.29855657e+00 1.09442055e+00 4.31293607e-01 1.10051417e+00 -2.23616242e-01 8.87831569e-01 1.70501083e-01 1.91247964e+00 -3.70166808e-01 -6.19648576e-01 7.14700297e-02 -5.42228580e-01 -1.37593603e+00 -1.58518571e-02 1.36997983e-01 -3.50690305e-01 -7.88790286e-01 8.42844367e-01 -2.41654478e-02 2.11949766e-01 3.93912137e-01 -1.56666219e+00 -4.04808670e-01 1.04189575e+00 -6.06804907e-01 4.58495468e-01 8.14689696e-01 2.76657729e-03 1.40560472e+00 -6.32127762e-01 1.05495930e+00 1.33012128e+00 4.48061526e-01 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1.52893782e-01 -1.06896865e+00 -2.82075793e-01 5.99386394e-01 -3.40355784e-02 -7.49018729e-01 2.33616799e-01 5.23727238e-01]	[8.604284286499023, 0.7236707806587219]
5b52b578-6da2-4c7a-a17c-1c02dff2f6b9	variational-laws-of-visual-attention-for	null	null	http://papers.nips.cc/paper/6972-variational-laws-of-visual-attention-for-dynamic-scenes	http://papers.nips.cc/paper/6972-variational-laws-of-visual-attention-for-dynamic-scenes.pdf	Variational Laws of Visual Attention for Dynamic Scenes	Computational models of visual attention are at the crossroad of disciplines like cognitive science, computational neuroscience, and computer vision. This paper proposes a model of attentional scanpath that is based on the principle that there are foundational laws that drive the emergence of visual attention. We devise variational laws of the eye-movement that rely on a generalized view of the Least Action Principle in physics. The potential energy captures details as well as peripheral visual features, while the kinetic energy corresponds with the classic interpretation in analytic mechanics. In addition, the Lagrangian contains a brightness invariance term, which characterizes significantly the scanpath trajectories. We obtain differential equations of visual attention as the stationary point of the generalized action, and we propose an algorithm to estimate the model parameters. Finally, we report experimental results to validate the model in tasks of saliency detection.	['Marco Gori', 'Dario Zanca']	2017-12-01	null	null	null	neurips-2017-12	['scanpath-prediction']	['computer-vision']	[ 9.58290994e-02 4.23515961e-03 -9.35798287e-02 -5.64670339e-02 1.41645685e-01 -2.15137646e-01 5.11124790e-01 -1.01766795e-01 -4.53065723e-01 2.89282590e-01 8.95512700e-02 -2.76155919e-01 -3.02739382e-01 -3.87499422e-01 -5.65039635e-01 -8.13141227e-01 2.10932434e-01 -4.88848761e-02 4.29756641e-01 -3.82602066e-01 8.15577567e-01 4.21489179e-01 -1.63997269e+00 -3.01258624e-01 1.08019316e+00 6.85696125e-01 4.48013902e-01 7.32449532e-01 -1.16816778e-02 4.78139281e-01 -6.81428239e-03 -1.99501500e-01 2.02161729e-01 -7.51155317e-01 -8.78436029e-01 -9.27465931e-02 3.46968859e-01 1.28479183e-01 -5.29573679e-01 1.38872278e+00 1.05093420e-01 5.12933314e-01 7.67708004e-01 -1.17207682e+00 -1.35907376e+00 3.07733770e-02 -7.35808134e-01 9.32508826e-01 1.12485275e-01 2.21047238e-01 1.27090287e+00 -9.70419109e-01 5.63436687e-01 1.26947284e+00 2.40251020e-01 6.11884177e-01 -1.08646846e+00 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1.76764935e-01 6.60513103e-01 -8.84008527e-01 -7.08013892e-01 1.80562735e-01]	[9.964715003967285, 1.5788871049880981]
f99c26ab-5c48-4ffe-b601-fcb003fe00ce	3d-compositional-zero-shot-learning-with	2111.14673	null	https://arxiv.org/abs/2111.14673v2	https://arxiv.org/pdf/2111.14673v2.pdf	3D Compositional Zero-shot Learning with DeCompositional Consensus	Parts represent a basic unit of geometric and semantic similarity across different objects. We argue that part knowledge should be composable beyond the observed object classes. Towards this, we present 3D Compositional Zero-shot Learning as a problem of part generalization from seen to unseen object classes for semantic segmentation. We provide a structured study through benchmarking the task with the proposed Compositional-PartNet dataset. This dataset is created by processing the original PartNet to maximize part overlap across different objects. The existing point cloud part segmentation methods fail to generalize to unseen object classes in this setting. As a solution, we propose DeCompositional Consensus, which combines a part segmentation network with a part scoring network. The key intuition to our approach is that a segmentation mask over some parts should have a consensus with its part scores when each part is taken apart. The two networks reason over different part combinations defined in a per-object part prior to generate the most suitable segmentation mask. We demonstrate that our method allows compositional zero-shot segmentation and generalized zero-shot classification, and establishes the state of the art on both tasks.	['Federico Tombari', 'Luc van Gool', 'Yongqin Xian', 'Evin Pınar Örnek', 'Muhammad Ferjad Naeem']	2021-11-29	null	null	null	null	['zero-shot-segmentation', 'compositional-zero-shot-learning']	['computer-vision', 'computer-vision']	[ 5.64495683e-01 5.17948508e-01 -1.77271068e-01 -3.90708178e-01 -7.56141961e-01 -7.72831261e-01 4.39873546e-01 7.03173205e-02 1.85033128e-01 1.38944928e-02 -2.87347175e-02 3.33665609e-01 -1.79188728e-01 -7.60156035e-01 -9.69533443e-01 -6.80146813e-01 2.40365118e-01 9.81529951e-01 8.99943948e-01 -1.20276459e-01 9.34139043e-02 6.88984275e-01 -1.92545521e+00 4.63327199e-01 5.46202540e-01 1.01268625e+00 2.41171315e-01 3.98975521e-01 -4.55244035e-01 2.89603144e-01 -3.74725699e-01 -3.81316900e-01 8.93830895e-01 -4.16494578e-01 -1.21183228e+00 9.06421065e-01 8.04519355e-01 2.17559058e-02 1.86895326e-01 1.23415148e+00 1.64276123e-01 4.26943064e-01 9.29659486e-01 -1.75595021e+00 -5.07173955e-01 8.91274035e-01 -2.45453432e-01 -3.35486621e-01 1.18667968e-01 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2.66721994e-01 4.16729987e-01 3.96312445e-01 -1.29774094e+00 -2.90254205e-01 -7.83091724e-01 -8.93694878e-01 9.31764603e-01 1.86119661e-01 -7.17904150e-01 -6.50021672e-01 4.23674919e-02]	[9.293172836303711, 0.6440396904945374]
e01c6c17-68af-4b2c-ac75-d978a7944db7	deep-learning-based-bio-medical-image	2305.14841	null	https://arxiv.org/abs/2305.14841v1	https://arxiv.org/pdf/2305.14841v1.pdf	Deep Learning-based Bio-Medical Image Segmentation using UNet Architecture and Transfer Learning	Image segmentation is a branch of computer vision that is widely used in real world applications including biomedical image processing. With recent advancement of deep learning, image segmentation has achieved at a very high level performance. Recently, UNet architecture is found as the core of novel deep learning segmentation methods. In this paper we implement UNet architecture from scratch with using basic blocks in Pytorch and evaluate its performance on multiple biomedical image datasets. We also use transfer learning to apply novel modified UNet segmentation packages on the biomedical image datasets. We fine tune the pre-trained transferred model with each specific dataset. We compare its performance with our fundamental UNet implementation. We show that transferred learning model has better performance in image segmentation than UNet model that is implemented from scratch.	['Abouzar Ghavami', 'Nima Hassanpour']	2023-05-24	null	null	null	null	['unet-segmentation']	['computer-vision']	[ 3.81631285e-01 8.57387707e-02 -3.40448059e-02 -5.63672245e-01 -7.00619161e-01 -2.52098680e-01 1.08419545e-01 -2.26041395e-02 -9.22357082e-01 4.40196276e-01 -3.85056257e-01 -4.70082045e-01 3.55501562e-01 -7.62135684e-01 -7.83767939e-01 -5.32737613e-01 1.09419726e-01 7.03149974e-01 7.26739705e-01 -6.17410243e-02 1.74981207e-01 3.91028464e-01 -8.56666744e-01 6.75723433e-01 7.20913351e-01 7.49844193e-01 2.60549039e-01 9.89961624e-01 -4.01845217e-01 3.73477817e-01 -7.61566341e-01 -1.70317460e-02 3.81989986e-01 -4.10060167e-01 -1.18072951e+00 -1.48793444e-01 4.07983273e-01 -1.28886700e-01 2.11222798e-01 8.75697076e-01 6.73317850e-01 -2.94374555e-01 6.82679951e-01 -9.05838788e-01 -4.87376414e-02 6.35857403e-01 -8.40512037e-01 3.47785860e-01 -2.54932106e-01 6.51748553e-02 2.32144654e-01 -4.24276441e-01 5.37926495e-01 1.07747066e+00 1.00777793e+00 7.29956448e-01 -1.07863438e+00 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6.60161078e-02 1.81855559e-01 -9.99099612e-02 1.80005625e-01 3.56253564e-01]	[14.576254844665527, -2.4951319694519043]
eef6b20c-b889-4e25-aec5-d62719ee7689	mahalo-unifying-offline-reinforcement	2303.17156	null	https://arxiv.org/abs/2303.17156v1	https://arxiv.org/pdf/2303.17156v1.pdf	MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations	We study a new paradigm for sequential decision making, called offline Policy Learning from Observation (PLfO). Offline PLfO aims to learn policies using datasets with substandard qualities: 1) only a subset of trajectories is labeled with rewards, 2) labeled trajectories may not contain actions, 3) labeled trajectories may not be of high quality, and 4) the overall data may not have full coverage. Such imperfection is common in real-world learning scenarios, so offline PLfO encompasses many existing offline learning setups, including offline imitation learning (IL), ILfO, and reinforcement learning (RL). In this work, we present a generic approach, called Modality-agnostic Adversarial Hypothesis Adaptation for Learning from Observations (MAHALO), for offline PLfO. Built upon the pessimism concept in offline RL, MAHALO optimizes the policy using a performance lower bound that accounts for uncertainty due to the dataset's insufficient converge. We implement this idea by adversarially training data-consistent critic and reward functions in policy optimization, which forces the learned policy to be robust to the data deficiency. We show that MAHALO consistently outperforms or matches specialized algorithms across a variety of offline PLfO tasks in theory and experiments.	['Ching-An Cheng', 'Byron Boots', 'Anqi Li']	2023-03-30	null	null	null	null	['offline-rl']	['playing-games']	[-1.08985290e-01 4.71639574e-01 -7.39066541e-01 6.42026886e-02 -9.69893754e-01 -8.90029728e-01 5.13693929e-01 9.40959305e-02 -3.96086663e-01 1.19250560e+00 6.41715229e-02 -4.79790121e-01 -1.86829463e-01 -5.22158206e-01 -1.21300507e+00 -7.45718241e-01 -2.34831139e-01 7.39890099e-01 -5.58684058e-02 6.81658799e-04 -4.11035260e-03 5.83564878e-01 -1.13662410e+00 -7.03800023e-02 8.26906681e-01 1.10213089e+00 -3.54210734e-02 7.10749865e-01 2.45318741e-01 1.17138278e+00 -5.99342287e-01 -1.26496702e-01 8.43275666e-01 -4.78539616e-01 -6.45909786e-01 1.67070597e-01 -1.43234804e-02 -9.83793318e-01 -6.52375400e-01 1.12446320e+00 3.05313230e-01 4.52905238e-01 5.73464394e-01 -1.60482383e+00 -4.68462586e-01 6.44735277e-01 4.04609926e-02 1.45179534e-03 1.60478875e-01 7.10438669e-01 7.34765768e-01 -2.90550172e-01 6.25674844e-01 1.37849832e+00 3.81155968e-01 9.54801977e-01 -1.04449427e+00 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abc3bbd3-650a-47e2-b8c1-98b7902235ed	a-multi-stream-convolutional-neural-network	1812.10328	null	http://arxiv.org/abs/1812.10328v1	http://arxiv.org/pdf/1812.10328v1.pdf	A Multi-Stream Convolutional Neural Network Framework for Group Activity Recognition	In this work, we present a framework based on multi-stream convolutional neural networks (CNNs) for group activity recognition. Streams of CNNs are separately trained on different modalities and their predictions are fused at the end. Each stream has two branches to predict the group activity based on person and scene level representations. A new modality based on the human pose estimation is presented to add extra information to the model. We evaluate our method on the Volleyball and Collective Activity datasets. Experimental results show that the proposed framework is able to achieve state-of-the-art results when multiple or single frames are given as input to the model with 90.50% and 86.61% accuracy on Volleyball dataset, respectively, and 87.01% accuracy of multiple frames group activity on Collective Activity dataset.	['Mina Ghadimi Atigh', 'Ahmad Nickabadi', 'Sina Mokhtarzadeh Azar']	2018-12-26	null	null	null	null	['group-activity-recognition']	['computer-vision']	[ 1.77885190e-01 -1.27579838e-01 -3.53891492e-01 -3.16510528e-01 -3.62766027e-01 -1.64316699e-01 9.14208531e-01 -4.23146747e-02 -7.67605007e-01 6.15853250e-01 4.21812177e-01 4.35515642e-01 1.31604522e-01 -8.25616181e-01 -7.79900670e-01 -3.95167619e-01 -1.19816333e-01 2.25173488e-01 4.31882411e-01 -5.89578822e-02 -4.04484645e-02 3.88590634e-01 -1.84908307e+00 9.06561553e-01 2.73468733e-01 1.42608082e+00 -1.59815878e-01 9.10655081e-01 3.11644256e-01 1.48067284e+00 -6.61669135e-01 -1.40156984e-01 3.60052764e-01 -4.94559079e-01 -6.88756645e-01 3.06351185e-01 6.92514777e-01 -2.73690701e-01 -5.80690145e-01 3.34114552e-01 5.06714761e-01 3.67129356e-01 5.34352601e-01 -1.32196808e+00 -1.54012991e-02 9.25720930e-02 -3.42745483e-01 4.10774827e-01 8.16438556e-01 2.41514057e-01 6.01009905e-01 -1.01039207e+00 6.39632344e-01 9.17328238e-01 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732ad4c4-d35e-4509-88a7-7e60c1194fd5	cmr3d-contextualized-multi-stage-refinement	2209.06641	null	https://arxiv.org/abs/2209.06641v1	https://arxiv.org/pdf/2209.06641v1.pdf	CMR3D: Contextualized Multi-Stage Refinement for 3D Object Detection	Existing deep learning-based 3D object detectors typically rely on the appearance of individual objects and do not explicitly pay attention to the rich contextual information of the scene. In this work, we propose Contextualized Multi-Stage Refinement for 3D Object Detection (CMR3D) framework, which takes a 3D scene as input and strives to explicitly integrate useful contextual information of the scene at multiple levels to predict a set of object bounding-boxes along with their corresponding semantic labels. To this end, we propose to utilize a context enhancement network that captures the contextual information at different levels of granularity followed by a multi-stage refinement module to progressively refine the box positions and class predictions. Extensive experiments on the large-scale ScanNetV2 benchmark reveal the benefits of our proposed method, leading to an absolute improvement of 2.0% over the baseline. In addition to 3D object detection, we investigate the effectiveness of our CMR3D framework for the problem of 3D object counting. Our source code will be publicly released.	['Hisham Cholakkal', 'Rao Muhammad Anwer', 'Fahad Shahbaz Khan', 'Jean Lahoud', 'Dhanalaxmi Gaddam']	2022-09-13	null	null	null	null	['object-counting']	['computer-vision']	[ 1.13099672e-01 -1.58082202e-01 -3.22928168e-02 -5.86801171e-01 -4.87170249e-01 -4.56206620e-01 6.98587298e-01 4.54269350e-01 -5.57286620e-01 9.45192855e-03 4.26629893e-02 -2.35301912e-01 2.92842895e-01 -6.48780286e-01 -8.23509455e-01 -3.29152584e-01 3.00399866e-02 4.62509662e-01 1.00249922e+00 7.79634416e-02 2.69221008e-01 8.78674686e-01 -1.47851741e+00 2.50846207e-01 4.85288382e-01 1.15260649e+00 4.23776656e-01 6.53612912e-01 -1.46479502e-01 6.98819876e-01 -3.39546263e-01 -2.81216819e-02 4.75938976e-01 1.18488811e-01 -5.95922172e-01 4.15605038e-01 7.00536847e-01 -7.83909261e-01 -2.27216139e-01 9.89985466e-01 2.90415764e-01 5.22954389e-02 5.68168521e-01 -8.50669026e-01 -2.24047244e-01 2.19351202e-01 -9.88685250e-01 4.67373073e-01 -8.79439637e-02 3.66746485e-01 1.03586042e+00 -1.12500405e+00 3.42703134e-01 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17e482a5-f394-49c6-ba74-3802ce02a7f6	multi-task-instruction-tuning-of-llama-for	2305.13225	null	https://arxiv.org/abs/2305.13225v1	https://arxiv.org/pdf/2305.13225v1.pdf	Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A Preliminary Study on Writing Assistance	ChatGPT and GPT-4 have attracted substantial interest from both academic and industrial circles, owing to their remarkable few-shot (or even zero-shot) ability to handle various tasks. Recent work shows that, after being fine-tuned with a few sets of instruction-driven data, the recently proposed LLM, LLaMa, exhibits an impressive capability to address a broad range of tasks. However, the zero-shot performance of LLMs does not consistently outperform that of models fined-tuned for specific scenarios. To explore whether the capabilities of LLMs can be further enhanced for specific scenarios, we choose the writing-assistance scenario as the testbed, including seven writing tasks. We collect training data for these tasks, reframe them in an instruction-following format, and subsequently refine LLaMa via instruction tuning. Experimental results show that continually fine-tuning LLaMa on writing instruction data significantly improves its ability on writing tasks. We also conduct more experiments and analyses to offer insights for future work on effectively fine-tuning LLaMa for specific scenarios.	['Wei Bi', 'Tao Fang', 'Xinting Huang', 'Deng Cai', 'Leyang Cui', 'Yue Zhang']	2023-05-22	null	null	null	null	['instruction-following']	['natural-language-processing']	[ 7.20238760e-02 -3.24729621e-01 -5.63194871e-01 -4.94141638e-01 -8.21915448e-01 -3.47134441e-01 6.73934221e-01 -1.34412020e-01 -2.62770891e-01 5.90797067e-01 1.28860220e-01 -7.55012810e-01 -1.02044933e-01 -4.81033325e-01 -5.87893605e-01 -2.85753369e-01 8.88777375e-02 4.00880635e-01 5.15026569e-01 -5.90117931e-01 6.26962185e-01 2.77958035e-01 -1.77835917e+00 2.54469693e-01 1.13484883e+00 2.95622438e-01 5.55718541e-01 7.15182424e-01 -2.45826513e-01 8.12169909e-01 -7.10741341e-01 -1.51050776e-01 6.27661794e-02 -1.09655164e-01 -8.61330569e-01 -1.24037847e-01 5.26495755e-01 -4.56663370e-01 -1.79128930e-01 4.92825836e-01 5.94352067e-01 5.05556464e-01 4.18264627e-01 -1.17226291e+00 -7.19197094e-01 7.16906130e-01 -6.62704468e-01 4.71534759e-01 2.55822629e-01 5.09685457e-01 8.12397599e-01 -7.67822385e-01 2.17896193e-01 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3.73309523e-01 -8.74656379e-01 -1.06963563e+00 -1.06485379e+00 -3.32524717e-01 2.02511728e-01 4.06401679e-02 -8.37864995e-01 -1.46155193e-01 -1.91564098e-01]	[10.654145240783691, 8.31674861907959]
1372390f-f2be-48f7-8e3b-aae9810950ed	falrr-a-fast-low-rank-representation-solver	null	null	http://openaccess.thecvf.com/content_cvpr_2015/html/Xiao_FaLRR_A_Fast_2015_CVPR_paper.html	http://openaccess.thecvf.com/content_cvpr_2015/papers/Xiao_FaLRR_A_Fast_2015_CVPR_paper.pdf	FaLRR: A Fast Low Rank Representation Solver	Low rank representation (LRR) has shown promising performance for various computer vision applications such as face clustering. Existing algorithms for solving LRR usually depend on its two-variable formulation which contains the original data matrix. In this paper, we develop a fast LRR solver called FaLRR, by reformulating LRR as a new optimization problem with regard to factorized data (which is obtained by skinny SVD of the original data matrix). The new formulation benefits the corresponding optimization and theoretical analysis. Specifically, to solve the resultant optimization problem, we propose a new algorithm which is not only efficient but also theoretically guaranteed to obtain a globally optimal solution. Regarding the theoretical analysis, the new formulation is helpful for deriving some interesting properties of LRR. Last but not least, the proposed algorithm can be readily incorporated into an existing distributed framework of LRR for further acceleration. Extensive experiments on synthetic and real-world datasets demonstrate that our FaLRR achieves order-of-magnitude speedup over existing LRR solvers, and the efficiency can be further improved by incorporating our algorithm into the distributed framework of LRR.	['DaCheng Tao', 'Shijie Xiao', 'Wen Li', 'Dong Xu']	2015-06-01	null	null	null	cvpr-2015-6	['face-clustering']	['computer-vision']	[ 1.60350457e-01 -2.42767945e-01 -2.41504878e-01 -2.25873917e-01 -8.44549537e-01 -4.44834590e-01 4.75562178e-02 -3.83542925e-01 -1.09154426e-01 5.27026355e-01 1.63542554e-01 -4.11725134e-01 -4.38874781e-01 -4.80963647e-01 -6.56616092e-01 -8.98456693e-01 -3.09231039e-02 2.54711151e-01 -3.34845573e-01 -7.55613670e-02 -3.17662358e-02 6.37409210e-01 -1.30154872e+00 4.57896218e-02 9.27377164e-01 9.76055741e-01 2.87813455e-01 3.00976574e-01 2.74293274e-01 6.78767323e-01 -2.12494031e-01 -7.80627877e-02 6.16497040e-01 -1.87370539e-01 -7.51781344e-01 3.92471969e-01 2.91750640e-01 -1.30679101e-01 -4.69838023e-01 1.08841217e+00 3.38148981e-01 3.77434075e-01 2.48148888e-01 -1.27629864e+00 -6.89425945e-01 3.90818626e-01 -1.23478162e+00 -2.32181046e-02 4.89107907e-01 -2.95263797e-01 1.07479525e+00 -1.20366573e+00 5.51379204e-01 1.44529831e+00 4.59466070e-01 2.00866699e-01 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-3.92910391e-01 -5.72462201e-01 2.84468800e-01 1.06211662e-01 -7.23040938e-01 -4.23425406e-01 -1.71037376e-01]	[7.454444408416748, 4.4902729988098145]
83eacd85-cd54-4037-ba8b-46d387635bd7	intel-tau-a-color-constancy-dataset	1910.10404	null	https://arxiv.org/abs/1910.10404v5	https://arxiv.org/pdf/1910.10404v5.pdf	INTEL-TAU: A Color Constancy Dataset	In this paper, we describe a new large dataset for illumination estimation. This dataset, called INTEL-TAU, contains 7022 images in total, which makes it the largest available high-resolution dataset for illumination estimation research. The variety of scenes captured using three different camera models, namely Canon 5DSR, Nikon D810, and Sony IMX135, makes the dataset appropriate for evaluating the camera and scene invariance of the different illumination estimation techniques. Privacy masking is done for sensitive information, e.g., faces. Thus, the dataset is coherent with the new General Data Protection Regulation (GDPR). Furthermore, the effect of color shading for mobile images can be evaluated with INTEL-TAU dataset, as both corrected and uncorrected versions of the raw data are provided. Furthermore, this paper benchmarks several color constancy approaches on the proposed dataset.	['Alexandros Iosifidis', 'Moncef Gabbouj', 'Jenni Raitoharju', 'Jarno Nikkanen', 'Firas Laakom']	2019-10-23	null	null	null	null	['image-declipping', 'color-constancy', 'few-shot-camera-adaptive-color-constancy', 'few-shot-camera-adaptive-color-constancy', 'multi-target-regression']	['computer-vision', 'computer-vision', 'computer-vision', 'methodology', 'miscellaneous']	[ 6.41994178e-02 -6.66837692e-01 2.09142324e-02 -4.92168605e-01 -1.76420778e-01 -6.21343732e-01 1.61917105e-01 -4.91752893e-01 -6.16264820e-01 7.50131786e-01 -1.54437497e-01 2.13619713e-02 2.51161039e-01 -5.79719961e-01 -5.84489703e-01 -7.57695556e-01 4.63209718e-01 -3.61894101e-01 -4.61385474e-02 3.24183442e-02 2.53394693e-01 6.37974143e-01 -1.86576569e+00 7.38938227e-02 7.48138368e-01 1.33050287e+00 6.08953722e-02 4.68053371e-01 4.01864171e-01 4.89655495e-01 -6.34466171e-01 -6.75210774e-01 5.88258028e-01 9.78448987e-03 -2.35362008e-01 3.61523032e-01 7.11548388e-01 -7.62403131e-01 -2.89186597e-01 1.24298108e+00 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7dc7c68e-707c-4def-9d68-e99a2595666f	boosting-theory-of-mind-performance-in-large	2304.11490	null	https://arxiv.org/abs/2304.11490v3	https://arxiv.org/pdf/2304.11490v3.pdf	Boosting Theory-of-Mind Performance in Large Language Models via Prompting	Large language models (LLMs) excel in many tasks in 2023, but they still face challenges in complex reasoning. Theory-of-mind (ToM) tasks, which require understanding agents' beliefs, goals, and mental states, are essential for common-sense reasoning involving humans, making it crucial to enhance LLM performance in this area. This study measures the ToM performance of GPT-4 and three GPT-3.5 variants (Davinci-2, Davinci-3, GPT-3.5-Turbo), and investigates the effectiveness of in-context learning in improving their ToM comprehension. We evaluated prompts featuring two-shot chain of thought reasoning and step-by-step thinking instructions. We found that LLMs trained with Reinforcement Learning from Human Feedback (RLHF) (all models excluding Davinci-2) improved their ToM accuracy via in-context learning. GPT-4 performed best in zero-shot settings, reaching nearly 80% ToM accuracy, but still fell short of the 87% human accuracy on the test set. However, when supplied with prompts for in-context learning, all RLHF-trained LLMs exceeded 80% ToM accuracy, with GPT-4 reaching 100%. These results demonstrate that appropriate prompting enhances LLM ToM reasoning, and they underscore the context-dependent nature of LLM cognitive capacities.	['Christopher J. Honey', 'Shima Rahimi Moghaddam']	2023-04-22	null	null	null	null	['common-sense-reasoning']	['reasoning']	[ 7.84439594e-02 4.27731782e-01 1.13949506e-02 -3.39563750e-02 -5.21491170e-01 -2.45674461e-01 7.77903259e-01 4.97297198e-01 -5.61605990e-01 4.45344001e-01 3.44439805e-01 -8.85674894e-01 -3.61030251e-01 -7.79423058e-01 -3.75676870e-01 6.40846882e-03 1.15430333e-01 7.38345563e-01 1.42017230e-01 -6.39661074e-01 6.64202809e-01 9.23667401e-02 -1.22023070e+00 4.97177869e-01 1.10643053e+00 2.81604022e-01 5.05867660e-01 8.30383778e-01 -3.18982303e-02 1.86228406e+00 -5.95407188e-01 -1.47471681e-01 -4.24358733e-02 -4.64494526e-01 -1.11630571e+00 -2.15944096e-01 1.87325776e-01 -7.75996089e-01 -2.32257366e-01 5.37049115e-01 1.11040659e-01 5.78673542e-01 4.86492693e-01 -1.12897980e+00 -9.08643484e-01 7.60711908e-01 -1.89755723e-01 3.72432321e-01 9.60043490e-01 8.27911496e-01 7.01344371e-01 -5.46812713e-01 1.99820474e-01 1.67001772e+00 6.29470408e-01 6.88531339e-01 -1.09048629e+00 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3.20559561e-01 1.56566456e-01 -7.57952750e-01 -2.29566276e-01 -3.52276564e-01]	[9.601364135742188, 7.310346603393555]
8277a68e-45c9-4ac0-b8ea-e9d72fa4b93e	latent-ransac	1802.07045	null	http://arxiv.org/abs/1802.07045v2	http://arxiv.org/pdf/1802.07045v2.pdf	Latent RANSAC	We present a method that can evaluate a RANSAC hypothesis in constant time, i.e. independent of the size of the data. A key observation here is that correct hypotheses are tightly clustered together in the latent parameter domain. In a manner similar to the generalized Hough transform we seek to find this cluster, only that we need as few as two votes for a successful detection. Rapidly locating such pairs of similar hypotheses is made possible by adapting the recent "Random Grids" range-search technique. We only perform the usual (costly) hypothesis verification stage upon the discovery of a close pair of hypotheses. We show that this event rarely happens for incorrect hypotheses, enabling a significant speedup of the RANSAC pipeline. The suggested approach is applied and tested on three robust estimation problems: camera localization, 3D rigid alignment and 2D-homography estimation. We perform rigorous testing on both synthetic and real datasets, demonstrating an improvement in efficiency without a compromise in accuracy. Furthermore, we achieve state-of-the-art 3D alignment results on the challenging "Redwood" loop-closure challenge.	['Simon Korman', 'Roee Litman']	2018-02-20	latent-ransac-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Korman_Latent_RANSAC_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Korman_Latent_RANSAC_CVPR_2018_paper.pdf	cvpr-2018-6	['robust-face-alignment', '3d-plane-detection', 'camera-localization', 'homography-estimation']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision']	[ 1.78165555e-01 -2.10446581e-01 8.76150355e-02 -1.26775816e-01 -1.17650914e+00 -1.05814242e+00 8.75670254e-01 3.57575476e-01 -4.81988966e-01 5.20606458e-01 -1.74952656e-01 -1.93298012e-01 -1.07320873e-02 -3.04570258e-01 -8.76976490e-01 -6.53501213e-01 -4.53801490e-02 8.20979476e-01 4.64267880e-01 2.08298992e-02 6.38716519e-01 7.71971941e-01 -1.47177374e+00 -2.42268547e-01 5.83966494e-01 8.53630900e-01 1.10499106e-01 8.38742018e-01 6.06112182e-01 1.60529464e-01 -2.10644677e-01 -2.59337753e-01 7.05406666e-01 -2.43032843e-01 -6.65409863e-01 4.48776722e-01 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ecfadcdd-d0c2-4504-9928-7c175923a1eb	probabilistic-querying-of-continuous-time	2211.08499	null	https://arxiv.org/abs/2211.08499v1	https://arxiv.org/pdf/2211.08499v1.pdf	Probabilistic Querying of Continuous-Time Event Sequences	Continuous-time event sequences, i.e., sequences consisting of continuous time stamps and associated event types ("marks"), are an important type of sequential data with many applications, e.g., in clinical medicine or user behavior modeling. Since these data are typically modeled autoregressively (e.g., using neural Hawkes processes or their classical counterparts), it is natural to ask questions about future scenarios such as "what kind of event will occur next" or "will an event of type $A$ occur before one of type $B$". Unfortunately, some of these queries are notoriously hard to address since current methods are limited to naive simulation, which can be highly inefficient. This paper introduces a new typology of query types and a framework for addressing them using importance sampling. Example queries include predicting the $n^\text{th}$ event type in a sequence and the hitting time distribution of one or more event types. We also leverage these findings further to be applicable for estimating general "$A$ before $B$" type of queries. We prove theoretically that our estimation method is effectively always better than naive simulation and show empirically based on three real-world datasets that it is on average 1,000 times more efficient than existing approaches.	['Padhraic Smyth', 'Stephan Mandt', 'Yuxin Chang', 'Alex Boyd']	2022-11-15	null	null	null	null	['type']	['speech']	[ 1.87124074e-01 -3.66744012e-01 -2.97390431e-01 -3.51719737e-01 -7.46265352e-01 -4.33843225e-01 3.81905317e-01 8.28590810e-01 -6.26689970e-01 1.03742075e+00 2.74208337e-02 -9.34113622e-01 -1.96671575e-01 -1.04661810e+00 -9.56777275e-01 -5.93004107e-01 -5.49278259e-01 5.62742770e-01 3.07304934e-02 6.05820864e-02 1.81336120e-01 3.46265137e-01 -1.30403423e+00 -1.67883694e-01 5.38758278e-01 9.39366460e-01 -9.90632083e-03 6.92923665e-01 -1.44496456e-01 6.87630653e-01 -7.92863905e-01 -3.64968359e-01 -7.51780868e-02 -2.00417116e-01 -5.02488732e-01 -3.50721121e-01 -3.71023148e-01 -4.13372517e-01 -2.70744771e-01 8.06971312e-01 3.11673760e-01 3.44385535e-01 7.48686016e-01 -1.16922498e+00 2.19918206e-01 5.82269132e-01 -6.23333573e-01 6.04282379e-01 4.28870618e-01 1.77181110e-01 6.97168350e-01 -3.60427916e-01 3.42338592e-01 1.12656808e+00 6.62286103e-01 2.50608712e-01 -1.29592466e+00 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ef139e2e-3c58-45cb-8ba7-89271102030e	vqnet-2-0-a-new-generation-machine-learning	2301.03251	null	https://arxiv.org/abs/2301.03251v1	https://arxiv.org/pdf/2301.03251v1.pdf	VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum	With the rapid development of classical and quantum machine learning, a large number of machine learning frameworks have been proposed. However, existing machine learning frameworks usually only focus on classical or quantum, rather than both. Therefore, based on VQNet 1.0, we further propose VQNet 2.0, a new generation of unified classical and quantum machine learning framework that supports hybrid optimization. The core library of the framework is implemented in C++, and the user level is implemented in Python, and it supports deployment on quantum and classical hardware. In this article, we analyze the development trend of the new generation machine learning framework and introduce the design principles of VQNet 2.0 in detail: unity, practicality, efficiency, and compatibility, as well as full particulars of implementation. We illustrate the functions of VQNet 2.0 through several basic applications, including classical convolutional neural networks, quantum autoencoders, hybrid classical-quantum networks, etc. After that, through extensive experiments, we demonstrate that the operation speed of VQNet 2.0 is higher than the comparison method. Finally, through extensive experiments, we demonstrate that VQNet 2.0 can deploy on different hardware platforms, the overall calculation speed is faster than the comparison method. It also can be mixed and optimized with quantum circuits composed of multiple quantum computing libraries.	['Guoping Guo', 'Zhaoyun Chen', 'Nenghai Yu', 'Weiming Zhang', 'Yang Yang', 'Ye Li', 'Wenyu Zhu', 'Wei Wang', 'Zhaohui Zhou', 'Hanchao Wang', 'Yiming Zhao', 'Lei LI', 'Yuan Fang', 'Menghan Dou', 'Zhilong Jia', 'Huanyu Bian']	2023-01-09	null	null	null	null	['unity']	['computer-vision']	[-3.60235989e-01 -4.21662003e-01 4.79847454e-02 -1.92385301e-01 -3.17500472e-01 -3.10628980e-01 2.57546693e-01 -1.68416515e-01 -5.87363303e-01 6.91015959e-01 -6.03654802e-01 -4.86654431e-01 1.50014699e-01 -1.37905395e+00 -5.61439574e-01 -9.28627551e-01 9.73869264e-02 7.89739713e-02 2.44302019e-01 -7.46465325e-01 2.06690282e-01 1.67981878e-01 -1.47205591e+00 1.29683331e-01 7.45548844e-01 9.64962900e-01 5.25158038e-03 7.61840522e-01 3.71117145e-02 6.26572371e-01 -4.51171637e-01 -6.42871439e-01 3.11122477e-01 -2.90200770e-01 -7.82364488e-01 -9.38632965e-01 -1.33243531e-01 -4.57990080e-01 -1.03753054e+00 1.49204981e+00 8.99405539e-01 4.26162742e-02 3.35223109e-01 -1.30496430e+00 -8.06981146e-01 9.38531995e-01 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