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0f595064-8130-4483-8a03-45cd918ec1df	xdgan-multi-modal-3d-shape-generation-in-2d	2210.03007	null	https://arxiv.org/abs/2210.03007v1	https://arxiv.org/pdf/2210.03007v1.pdf	XDGAN: Multi-Modal 3D Shape Generation in 2D Space	Generative models for 2D images has recently seen tremendous progress in quality, resolution and speed as a result of the efficiency of 2D convolutional architectures. However it is difficult to extend this progress into the 3D domain since most current 3D representations rely on custom network components. This paper addresses a central question: Is it possible to directly leverage 2D image generative models to generate 3D shapes instead? To answer this, we propose XDGAN, an effective and fast method for applying 2D image GAN architectures to the generation of 3D object geometry combined with additional surface attributes, like color textures and normals. Specifically, we propose a novel method to convert 3D shapes into compact 1-channel geometry images and leverage StyleGAN3 and image-to-image translation networks to generate 3D objects in 2D space. The generated geometry images are quick to convert to 3D meshes, enabling real-time 3D object synthesis, visualization and interactive editing. Moreover, the use of standard 2D architectures can help bring more 2D advances into the 3D realm. We show both quantitatively and qualitatively that our method is highly effective at various tasks such as 3D shape generation, single view reconstruction and shape manipulation, while being significantly faster and more flexible compared to recent 3D generative models.	['Maria Shugrina', 'Sanja Fidler', 'André Knörig', 'Alara Dirik', 'Hassan Abu Alhaija']	2022-10-06	null	null	null	null	['3d-shape-generation']	['computer-vision']	[ 2.48519301e-01 3.04219753e-01 3.65182966e-01 -1.32284477e-01 -5.20489931e-01 -9.40563679e-01 9.04000401e-01 -4.99051005e-01 3.52573186e-01 3.83233845e-01 1.14626199e-01 -4.50288624e-01 3.45178008e-01 -1.17394996e+00 -8.61786604e-01 -3.68930221e-01 1.86272055e-01 7.33082294e-01 -1.12660699e-01 -3.77930343e-01 1.36782363e-01 1.19956458e+00 -1.56509638e+00 2.85910629e-03 4.93934244e-01 8.91902089e-01 -8.16493779e-02 8.39861095e-01 -3.28275710e-01 9.39638764e-02 -3.96877617e-01 -3.35733414e-01 6.86510086e-01 -6.25472665e-01 -4.76944089e-01 4.47800726e-01 6.52470171e-01 -5.10397732e-01 5.55387847e-02 6.84878886e-01 6.02660477e-01 -1.98611960e-01 8.48778844e-01 -1.07854152e+00 -9.81280446e-01 1.54744089e-02 -6.10585868e-01 -6.12073421e-01 4.29985642e-01 4.05964702e-01 5.87351859e-01 -8.97340298e-01 9.38100874e-01 1.56188953e+00 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35cbfe8a-64c8-4e75-b684-141e5ebb35a9	sparse-pairwise-re-ranking-with-pre-trained	2207.0447	null	https://arxiv.org/abs/2207.04470v1	https://arxiv.org/pdf/2207.04470v1.pdf	Sparse Pairwise Re-ranking with Pre-trained Transformers	Pairwise re-ranking models predict which of two documents is more relevant to a query and then aggregate a final ranking from such preferences. This is often more effective than pointwise re-ranking models that directly predict a relevance value for each document. However, the high inference overhead of pairwise models limits their practical application: usually, for a set of $k$ documents to be re-ranked, preferences for all $k^2-k$ comparison pairs excluding self-comparisons are aggregated. We investigate whether the efficiency of pairwise re-ranking can be improved by sampling from all pairs. In an exploratory study, we evaluate three sampling methods and five preference aggregation methods. The best combination allows for an order of magnitude fewer comparisons at an acceptable loss of retrieval effectiveness, while competitive effectiveness is already achieved with about one third of the comparisons.	['Martin Potthast', 'Matthias Hagen', 'Maik Fröbe', 'Lukas Gienapp']	2022-07-10	null	null	null	null	['passage-ranking']	['natural-language-processing']	[ 9.33806002e-02 -8.98553804e-02 -4.21756417e-01 -6.48451507e-01 -1.62317204e+00 -9.74264383e-01 7.41921365e-01 7.22739697e-01 -7.17685938e-01 1.00070107e+00 4.18032348e-01 -2.44334966e-01 -8.68849456e-01 -7.16770589e-01 -4.13997889e-01 -3.86329740e-01 -2.28225186e-01 9.28278029e-01 3.17032248e-01 -1.51282474e-01 9.29231763e-01 3.50876838e-01 -1.87262619e+00 6.81463242e-01 8.66936207e-01 8.58969867e-01 1.91550255e-01 6.12579465e-01 -8.75502080e-02 4.17342126e-01 -4.88187015e-01 -4.86893326e-01 4.24110830e-01 -2.37986356e-01 -1.07722080e+00 -5.92236638e-01 6.08051598e-01 -6.28518999e-01 1.93836123e-01 8.27637792e-01 5.67143440e-01 4.58923072e-01 7.61448860e-01 -7.67672062e-01 -3.66140276e-01 6.99699223e-01 -5.50481379e-01 8.77703056e-02 8.18543017e-01 -5.86449027e-01 1.72907317e+00 -9.81677949e-01 5.20917118e-01 1.18664396e+00 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3c481f72-9f37-435e-b707-6f41386b59b2	dominating-set-database-selection-for-visual	2303.05123	null	https://arxiv.org/abs/2303.05123v1	https://arxiv.org/pdf/2303.05123v1.pdf	Dominating Set Database Selection for Visual Place Recognition	This paper presents an approach for creating a visual place recognition (VPR) database for localization in indoor environments from RGBD scanning sequences. The proposed approach is formulated as a minimization problem in terms of dominating set algorithm for graph, constructed from spatial information, and referred as DominatingSet. Our algorithm shows better scene coverage in comparison to other methodologies that are used for database creation. Also, we demonstrate that using DominatingSet, a database size could be up to 250-1400 times smaller than the original scanning sequence while maintaining a recall rate of more than 80% on testing sequences. We evaluated our algorithm on 7-scenes and BundleFusion datasets and an additionally recorded sequence in a highly repetitive office setting. In addition, the database selection can produce weakly-supervised labels for fine-tuning neural place recognition algorithms to particular settings, improving even more their accuracy. The paper also presents a fully automated pipeline for VPR database creation from RGBD scanning sequences, as well as a set of metrics for VPR database evaluation. The code and released data are available on our web-page~ -- https://prime-slam.github.io/place-recognition-db/	['Gonzalo Ferrer', 'Rahim Tariverdizadeh', 'Fakhriddin Tojiboev', 'Timofei Pushkin', 'Ivan Moskalenko', 'Anastasiia Kornilova']	2023-03-09	null	null	null	null	['visual-place-recognition']	['computer-vision']	[ 3.58265162e-01 -2.95471340e-01 1.11629412e-01 -6.72110200e-01 -9.82062936e-01 -7.69956708e-01 2.41168350e-01 3.18262607e-01 -6.15361333e-01 5.65801919e-01 -6.17535599e-02 -2.86312521e-01 -6.36511594e-02 -8.00297797e-01 -1.03663063e+00 -5.90033889e-01 -2.09182546e-01 5.17374754e-01 3.20986599e-01 -1.81471050e-01 3.87996703e-01 8.37549567e-01 -1.81360531e+00 -8.38759467e-02 4.01825547e-01 1.05064559e+00 9.22587991e-01 7.77813554e-01 5.11132516e-02 6.59656167e-01 -5.12113154e-01 1.74495831e-01 5.27877450e-01 -2.32983917e-01 -6.16670012e-01 1.19946942e-01 6.78526103e-01 9.18300152e-02 -3.41401815e-01 8.49077940e-01 8.22853684e-01 3.61221135e-01 1.84859499e-01 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f15c9bfc-540f-43c7-bfc9-09d1297061d8	learning-word-embeddings-from-the-portuguese	1709.00947	null	http://arxiv.org/abs/1709.00947v1	http://arxiv.org/pdf/1709.00947v1.pdf	Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects	This paper describes a preliminary study for producing and distributing a large-scale database of embeddings from the Portuguese Twitter stream. We start by experimenting with a relatively small sample and focusing on three challenges: volume of training data, vocabulary size and intrinsic evaluation metrics. Using a single GPU, we were able to scale up vocabulary size from 2048 words embedded and 500K training examples to 32768 words over 10M training examples while keeping a stable validation loss and approximately linear trend on training time per epoch. We also observed that using less than 50\% of the available training examples for each vocabulary size might result in overfitting. Results on intrinsic evaluation show promising performance for a vocabulary size of 32768 words. Nevertheless, intrinsic evaluation metrics suffer from over-sensitivity to their corresponding cosine similarity thresholds, indicating that a wider range of metrics need to be developed to track progress.	['Eugénio Oliveira', 'Luís Sarmento', 'Pedro Saleiro', 'Eduarda Mendes Rodrigues', 'Carlos Soares']	2017-09-04	null	null	null	null	['learning-word-embeddings', '2048']	['methodology', 'playing-games']	[-2.48319298e-01 -2.11284831e-01 1.01722389e-01 -4.19005066e-01 -7.70699501e-01 -6.85806632e-01 8.40716243e-01 8.75463009e-01 -1.19433475e+00 7.13980854e-01 8.08389038e-02 -3.07446539e-01 8.37976672e-03 -8.15813243e-01 -2.62348443e-01 -2.81380147e-01 -5.41652977e-01 5.34248292e-01 3.34404320e-01 -2.64223129e-01 2.09009930e-01 3.63838732e-01 -1.86578190e+00 -1.09350644e-02 5.15148938e-01 9.87713695e-01 -1.22909714e-02 9.06923831e-01 -1.95491403e-01 2.61140227e-01 -1.00447965e+00 -5.66616476e-01 3.11222941e-01 1.96541563e-01 -6.65428281e-01 -3.19108456e-01 5.94783068e-01 -2.33420670e-01 -6.51067914e-03 8.19098830e-01 7.20274329e-01 2.34692797e-01 4.57517415e-01 -1.12441671e+00 -2.27663800e-01 4.94778425e-01 -1.16810277e-02 6.52901113e-01 2.22157732e-01 9.65584368e-02 1.03132546e+00 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d283e74a-a062-4aad-bdc0-6db0b0cf22a9	fast-and-unsupervised-action-boundary	null	null	http://openaccess.thecvf.com//content/CVPR2022/html/Du_Fast_and_Unsupervised_Action_Boundary_Detection_for_Action_Segmentation_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Du_Fast_and_Unsupervised_Action_Boundary_Detection_for_Action_Segmentation_CVPR_2022_paper.pdf	Fast and Unsupervised Action Boundary Detection for Action Segmentation	To deal with the great number of untrimmed videos produced every day, we propose an efficient unsupervised action segmentation method by detecting boundaries, named action boundary detection (ABD). In particular, the proposed method has the following advantages: no training stage and low-latency inference. To detect action boundaries, we estimate the similarities across smoothed frames, which inherently have the properties of internal consistency within actions and external discrepancy across actions. Under this circumstance, we successfully transfer the boundary detection task into the change point detection based on the similarity. Then, non-maximum suppression (NMS) is conducted in local windows to select the smallest points as candidate boundaries. In addition, a clustering algorithm is followed to refine the initial proposals. Moreover, we also extend ABD to the online setting, which enables real-time action segmentation in long untrimmed videos. By evaluating on four challenging datasets, our method achieves state-of-the-art performance. Moreover, thanks to the efficiency of ABD, we achieve the best trade-off between the accuracy and the inference time compared with existing unsupervised approaches.	['Qing Wang', 'Guoqing Zhou', 'Xue Wang', 'Zexing Du']	2022-01-01	null	null	null	cvpr-2022-1	['boundary-detection', 'action-segmentation']	['computer-vision', 'computer-vision']	[ 5.41750491e-01 -2.24313155e-01 -2.63297856e-01 -1.78181276e-01 -6.45305872e-01 -3.03378582e-01 4.09474611e-01 1.34995300e-02 -5.47401786e-01 3.88546288e-01 1.12905502e-01 1.12243667e-01 -1.49644792e-01 -5.14081895e-01 -3.66002470e-01 -8.14086914e-01 1.73370183e-01 1.25998974e-01 7.89493740e-01 1.97917044e-01 3.27202380e-01 3.19063604e-01 -1.55022252e+00 -1.18371518e-02 1.25483096e+00 1.20953751e+00 2.63183773e-01 2.06212595e-01 9.62103456e-02 6.91837251e-01 -3.87273431e-01 -7.77978599e-02 1.55422732e-01 -4.77144152e-01 -5.57245493e-01 5.86377144e-01 2.73329854e-01 -4.25949872e-01 -1.62740171e-01 1.07555079e+00 2.93134987e-01 3.83288234e-01 3.82926673e-01 -1.17161524e+00 -3.76267284e-02 2.52500415e-01 -8.35136354e-01 3.37148964e-01 3.86320293e-01 1.71894059e-01 9.38589871e-01 -6.86578453e-01 6.59972727e-01 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7.61689067e-01 -7.64184475e-01 -5.53303659e-01 -9.76832151e-01 -6.05597377e-01 7.26770341e-01 -5.35053276e-02 -8.13399196e-01 -2.93465942e-01 2.42065534e-01]	[8.52652645111084, 0.38884034752845764]
a0f7932e-ef94-40b5-babb-4529bf24d830	fully-automated-and-standardized-segmentation	2008.02251	null	https://arxiv.org/abs/2008.02251v1	https://arxiv.org/pdf/2008.02251v1.pdf	Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies	Purpose: To enable fast and reliable assessment of subcutaneous and visceral adipose tissue compartments derived from whole-body MRI. Methods: Quantification and localization of different adipose tissue compartments from whole-body MR images is of high interest to examine metabolic conditions. For correct identification and phenotyping of individuals at increased risk for metabolic diseases, a reliable automatic segmentation of adipose tissue into subcutaneous and visceral adipose tissue is required. In this work we propose a 3D convolutional neural network (DCNet) to provide a robust and objective segmentation. In this retrospective study, we collected 1000 cases (66$\pm$ 13 years; 523 women) from the Tuebingen Family Study and from the German Center for Diabetes research (TUEF/DZD), as well as 300 cases (53$\pm$ 11 years; 152 women) from the German National Cohort (NAKO) database for model training, validation, and testing with a transfer learning between the cohorts. These datasets had variable imaging sequences, imaging contrasts, receiver coil arrangements, scanners and imaging field strengths. The proposed DCNet was compared against a comparable 3D UNet segmentation in terms of sensitivity, specificity, precision, accuracy, and Dice overlap. Results: Fast (5-7seconds) and reliable adipose tissue segmentation can be obtained with high Dice overlap (0.94), sensitivity (96.6%), specificity (95.1%), precision (92.1%) and accuracy (98.4%) from 3D whole-body MR datasets (field of view coverage 450x450x2000mm${}^3$). Segmentation masks and adipose tissue profiles are automatically reported back to the referring physician. Conclusion: Automatic adipose tissue segmentation is feasible in 3D whole-body MR data sets and is generalizable to different epidemiological cohort studies with the proposed DCNet.	['Jürgen Machann', 'Fabian Bamberg', 'Hans-Ulrich Häring', 'Martin Schwartz', 'Thomas Küstner', 'Sergios Gatidis', 'Konstantin Nikolaou', 'Fritz Schick', 'Tobias Hepp', 'Marc Fischer', 'Andreas Fritsche', 'Bin Yang']	2020-08-05	null	null	null	null	['unet-segmentation']	['computer-vision']	[ 2.00662240e-01 -1.63468823e-01 -3.44493896e-01 -5.24659634e-01 -6.74117267e-01 -4.88781452e-01 1.93193648e-02 5.46289802e-01 -5.07987618e-01 6.43679202e-01 -7.92071074e-02 -2.86926448e-01 -2.14308724e-01 -6.41089678e-01 -3.29032332e-01 -5.37085176e-01 -9.12487268e-01 1.06552482e+00 1.31125674e-01 4.72716063e-01 -3.40056688e-01 5.30371785e-01 -5.77056468e-01 2.13817924e-01 1.03492618e+00 1.29552293e+00 1.58604570e-02 5.76875269e-01 -2.95120552e-02 -7.02250889e-03 -1.31621316e-01 -3.31822753e-01 5.52557766e-01 -4.77779239e-01 -6.74653232e-01 8.97532050e-03 6.17836416e-01 -3.55320394e-01 2.58126765e-01 9.30605948e-01 8.27738404e-01 -2.21343301e-02 7.63948500e-01 -7.41484463e-01 -3.16635311e-01 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2dc83534-e70d-4bf4-a3e2-074db14f6ab5	spherical-fourier-neural-operators-learning	2306.03838	null	https://arxiv.org/abs/2306.03838v1	https://arxiv.org/pdf/2306.03838v1.pdf	Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere	Fourier Neural Operators (FNOs) have proven to be an efficient and effective method for resolution-independent operator learning in a broad variety of application areas across scientific machine learning. A key reason for their success is their ability to accurately model long-range dependencies in spatio-temporal data by learning global convolutions in a computationally efficient manner. To this end, FNOs rely on the discrete Fourier transform (DFT), however, DFTs cause visual and spectral artifacts as well as pronounced dissipation when learning operators in spherical coordinates since they incorrectly assume a flat geometry. To overcome this limitation, we generalize FNOs on the sphere, introducing Spherical FNOs (SFNOs) for learning operators on spherical geometries. We apply SFNOs to forecasting atmospheric dynamics, and demonstrate stable auto\-regressive rollouts for a year of simulated time (1,460 steps), while retaining physically plausible dynamics. The SFNO has important implications for machine learning-based simulation of climate dynamics that could eventually help accelerate our response to climate change.	['Anima Anandkumar', 'Karthik Kashinath', 'Maximilian Baust', 'Jaideep Pathak', 'Christian Hundt', 'Thorsten Kurth', 'Boris Bonev']	2023-06-06	null	null	null	null	['operator-learning']	['miscellaneous']	[ 2.66541876e-02 -6.02275848e-01 5.64391494e-01 -1.30141333e-01 -1.91873550e-01 -6.20178521e-01 7.24732935e-01 -8.32407922e-02 -4.75885957e-01 9.62726116e-01 -5.26331551e-02 -6.39477968e-01 -3.31199408e-01 -7.63580143e-01 -8.68315876e-01 -8.78475249e-01 -7.00267971e-01 1.11054525e-01 1.74247310e-01 -2.43233293e-01 5.65443523e-02 1.12637472e+00 -1.45946968e+00 -1.24947906e-01 1.13974047e+00 6.79641783e-01 -5.30505851e-02 9.76059020e-01 1.38397276e-01 7.67023027e-01 -2.93268532e-01 1.63274124e-01 2.05557838e-01 -2.86779314e-01 -7.96211541e-01 -4.84613657e-01 4.37562197e-01 1.30203128e-01 -3.21837157e-01 8.01872253e-01 3.79504889e-01 8.93967450e-01 9.11343753e-01 -7.05639124e-01 -4.63795185e-01 5.65275066e-02 -2.73648888e-01 5.07862628e-01 -4.01231796e-01 1.99784279e-01 8.08553040e-01 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9.33321178e-01 4.73602027e-01 7.35851109e-01 4.74240035e-01 -1.25130141e+00 -4.04371887e-01 -1.02318561e+00 -4.73962814e-01 2.42593989e-01 7.52693653e-01 -7.17654109e-01 -8.87173653e-01 1.05127968e-01]	[6.578958034515381, 3.3266279697418213]
49e43879-9a96-42d3-b889-78e73044ba71	spatio-temporal-structure-consistency-for	2303.01707	null	https://arxiv.org/abs/2303.01707v1	https://arxiv.org/pdf/2303.01707v1.pdf	Spatio-Temporal Structure Consistency for Semi-supervised Medical Image Classification	Intelligent medical diagnosis has shown remarkable progress based on the large-scale datasets with precise annotations. However, fewer labeled images are available due to significantly expensive cost for annotating data by experts. To fully exploit the easily available unlabeled data, we propose a novel Spatio-Temporal Structure Consistent (STSC) learning framework. Specifically, a gram matrix is derived to combine the spatial structure consistency and temporal structure consistency together. This gram matrix captures the structural similarity among the representations of different training samples. At the spatial level, our framework explicitly enforces the consistency of structural similarity among different samples under perturbations. At the temporal level, we consider the consistency of the structural similarity in different training iterations by digging out the stable sub-structures in a relation graph. Experiments on two medical image datasets (i.e., ISIC 2018 challenge and ChestX-ray14) show that our method outperforms state-of-the-art SSL methods. Furthermore, extensive qualitative analysis on the Gram matrices and heatmaps by Grad-CAM are presented to validate the effectiveness of our method.	['Li Liu', 'Lei Liu', 'Wentao Lei']	2023-03-03	null	null	null	null	['medical-diagnosis', 'semi-supervised-medical-image-classification']	['medical', 'medical']	[ 1.43355027e-01 9.06296447e-02 -1.57026112e-01 -4.58425760e-01 -8.50437582e-01 -3.47705632e-01 2.55417019e-01 4.80191469e-01 1.16503946e-01 4.41929936e-01 4.13441479e-01 -7.22095668e-02 -5.68951070e-01 -2.98043847e-01 -3.95692438e-01 -9.52228844e-01 -4.05302286e-01 3.06813031e-01 3.26454222e-01 1.53183296e-01 -4.79117930e-02 2.01387763e-01 -1.02980888e+00 4.92541641e-01 9.07039881e-01 9.21056986e-01 2.59214073e-01 2.66582221e-01 -3.23689147e-03 1.07236528e+00 -8.53447616e-02 2.61904262e-02 -1.37187675e-01 -6.02007270e-01 -1.10980201e+00 5.58821082e-01 3.56887937e-01 7.78726265e-02 -3.34045351e-01 1.38974702e+00 3.88759673e-01 -1.21450566e-01 5.63202143e-01 -1.13716424e+00 -6.43098056e-01 4.17002439e-01 -7.88887024e-01 1.28416672e-01 3.38351756e-01 -9.72783267e-02 1.02602553e+00 -7.91819453e-01 1.06622720e+00 9.18783963e-01 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-3.41932356e-01 -8.17644358e-01 -7.43929803e-01 1.03325939e+00 2.83540487e-01 -7.36327708e-01 -1.61486611e-01 2.64786035e-01]	[14.806594848632812, -2.0544650554656982]
190ecbc5-2407-43bc-b402-24348924ddc8	pose-constraints-for-consistent-self	2304.08916	null	https://arxiv.org/abs/2304.08916v1	https://arxiv.org/pdf/2304.08916v1.pdf	Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motion	Self-supervised monocular depth estimation approaches suffer not only from scale ambiguity but also infer temporally inconsistent depth maps w.r.t. scale. While disambiguating scale during training is not possible without some kind of ground truth supervision, having scale consistent depth predictions would make it possible to calculate scale once during inference as a post-processing step and use it over-time. With this as a goal, a set of temporal consistency losses that minimize pose inconsistencies over time are introduced. Evaluations show that introducing these constraints not only reduces depth inconsistencies but also improves the baseline performance of depth and ego-motion prediction.	['Zeeshan Khan Suri']	2023-04-18	null	null	null	null	['motion-prediction', 'monocular-depth-estimation']	['computer-vision', 'computer-vision']	[ 1.69750139e-01 2.46244684e-01 -1.21208332e-01 -8.17014754e-01 -5.68371415e-01 -7.30016232e-01 6.53577864e-01 6.26437217e-02 -5.93023121e-01 8.35024774e-01 1.82500437e-01 -5.58059253e-02 1.34829938e-01 -9.04659986e-01 -7.44011283e-01 -4.37320679e-01 9.52955633e-02 4.41876680e-01 6.66454792e-01 1.96353719e-01 3.88589650e-01 7.35298514e-01 -1.65542078e+00 2.02920541e-01 8.05995226e-01 1.01072907e+00 7.11451992e-02 6.48801506e-01 6.53181504e-03 1.04897404e+00 -3.66996318e-01 -1.77430198e-01 5.32572448e-01 -2.64593482e-01 -7.47328699e-01 2.31711030e-01 1.16966593e+00 -7.38477588e-01 -5.19954443e-01 9.80532289e-01 3.83526981e-01 3.61458868e-01 3.34380925e-01 -1.10955393e+00 2.98004746e-01 9.37952753e-03 -6.27125382e-01 2.75410920e-01 4.11788553e-01 1.79360658e-01 9.00080442e-01 -5.62367439e-01 1.11080194e+00 1.18092752e+00 6.62286580e-01 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0687df05-4f42-4ac7-881a-d47d265f6dcb	blocks2world-controlling-realistic-scenes	2307.03847	null	https://arxiv.org/abs/2307.03847v1	https://arxiv.org/pdf/2307.03847v1.pdf	Blocks2World: Controlling Realistic Scenes with Editable Primitives	We present Blocks2World, a novel method for 3D scene rendering and editing that leverages a two-step process: convex decomposition of images and conditioned synthesis. Our technique begins by extracting 3D parallelepipeds from various objects in a given scene using convex decomposition, thus obtaining a primitive representation of the scene. These primitives are then utilized to generate paired data through simple ray-traced depth maps. The next stage involves training a conditioned model that learns to generate images from the 2D-rendered convex primitives. This step establishes a direct mapping between the 3D model and its 2D representation, effectively learning the transition from a 3D model to an image. Once the model is fully trained, it offers remarkable control over the synthesis of novel and edited scenes. This is achieved by manipulating the primitives at test time, including translating or adding them, thereby enabling a highly customizable scene rendering process. Our method provides a fresh perspective on 3D scene rendering and editing, offering control and flexibility. It opens up new avenues for research and applications in the field, including authoring and data augmentation.	['David Forsyth', 'Anand Bhattad', 'Rahul Vasanth', 'Seemandhar Jain', 'Vaibhav Vavilala']	2023-07-07	null	null	null	null	['data-augmentation']	['methodology']	[ 8.56156111e-01 2.08040431e-01 2.61973351e-01 -2.86377668e-01 -4.96640533e-01 -8.92308891e-01 9.78672564e-01 -1.27084360e-01 7.84197450e-02 2.17038468e-01 2.18049601e-01 -2.82551497e-01 3.98310483e-01 -9.53530252e-01 -9.47239041e-01 -5.85634887e-01 -1.46942586e-02 8.84132564e-01 -4.20800932e-02 -1.04714006e-01 2.44289503e-01 1.11537087e+00 -1.80254936e+00 4.12737191e-01 7.54904091e-01 7.10986316e-01 4.47910488e-01 9.99211788e-01 -2.87691951e-01 5.19773901e-01 -4.16308999e-01 -2.62822896e-01 5.53280830e-01 -3.19174439e-01 -5.82344413e-01 7.46547282e-01 4.89014179e-01 -6.33845985e-01 -1.16456434e-01 5.63137293e-01 2.93192655e-01 1.34249508e-01 6.38816357e-01 -9.53183055e-01 -6.54639244e-01 -4.56933528e-02 -5.63905537e-01 -4.98021811e-01 7.90877104e-01 1.60165593e-01 7.76790917e-01 -1.08610165e+00 9.05466735e-01 1.32521319e+00 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ca8cf5b7-3c3c-49ca-8959-8e154a3e3b91	bokehornot-transforming-bokeh-effect-with	2306.04032	null	https://arxiv.org/abs/2306.04032v1	https://arxiv.org/pdf/2306.04032v1.pdf	BokehOrNot: Transforming Bokeh Effect with Image Transformer and Lens Metadata Embedding	Bokeh effect is an optical phenomenon that offers a pleasant visual experience, typically generated by high-end cameras with wide aperture lenses. The task of bokeh effect transformation aims to produce a desired effect in one set of lenses and apertures based on another combination. Current models are limited in their ability to render a specific set of bokeh effects, primarily transformations from sharp to blur. In this paper, we propose a novel universal method for embedding lens metadata into the model and introducing a loss calculation method using alpha masks from the newly released Bokeh Effect Transformation Dataset(BETD) [3]. Based on the above techniques, we propose the BokehOrNot model, which is capable of producing both blur-to-sharp and sharp-to-blur bokeh effect with various combinations of lenses and aperture sizes. Our proposed model outperforms current leading bokeh rendering and image restoration models and renders visually natural bokeh effects. Our code is available at: https://github.com/indicator0/bokehornot.	['Siyuan Lai', 'Wenyi Lian', 'Zhihao Yang']	2023-06-06	null	null	null	null	['image-restoration']	['computer-vision']	[ 2.26381049e-01 -3.59123766e-01 4.64734375e-01 -1.20716304e-01 -3.17971289e-01 -6.13148272e-01 8.79294813e-01 -3.94892454e-01 -2.24119257e-02 6.87151790e-01 6.03849232e-01 -1.77916497e-01 -2.41321743e-01 -5.75442255e-01 -7.50178814e-01 -6.10567153e-01 2.36919105e-01 -2.08822399e-01 3.19466591e-01 -1.89909622e-01 5.44782877e-01 4.65293229e-01 -1.64555585e+00 2.19120353e-01 1.09884465e+00 8.13594699e-01 6.63843453e-01 1.20603669e+00 5.04378080e-01 7.44673669e-01 -4.43107069e-01 -4.53952700e-01 6.36771262e-01 -4.43768889e-01 -2.41367966e-01 4.19546093e-04 9.20150459e-01 -6.82299614e-01 -2.66635001e-01 1.01645923e+00 7.89746046e-01 1.90619081e-01 7.82769740e-01 -1.15357327e+00 -1.07291341e+00 -9.26464722e-02 -5.43403864e-01 -3.48679163e-03 5.28890789e-01 5.07356167e-01 6.74946249e-01 -1.16406596e+00 5.49408257e-01 1.21298993e+00 6.70702755e-01 4.74208772e-01 -1.25242317e+00 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aa1eec38-2bbe-4ec7-a52b-3913fbe8b2ca	a-method-for-discovering-novel-classes-in	2209.01217	null	https://arxiv.org/abs/2209.01217v3	https://arxiv.org/pdf/2209.01217v3.pdf	A Method for Discovering Novel Classes in Tabular Data	In Novel Class Discovery (NCD), the goal is to find new classes in an unlabeled set given a labeled set of known but different classes. While NCD has recently gained attention from the community, no framework has yet been proposed for heterogeneous tabular data, despite being a very common representation of data. In this paper, we propose TabularNCD, a new method for discovering novel classes in tabular data. We show a way to extract knowledge from already known classes to guide the discovery process of novel classes in the context of tabular data which contains heterogeneous variables. A part of this process is done by a new method for defining pseudo labels, and we follow recent findings in Multi-Task Learning to optimize a joint objective function. Our method demonstrates that NCD is not only applicable to images but also to heterogeneous tabular data. Extensive experiments are conducted to evaluate our method and demonstrate its effectiveness against 3 competitors on 7 diverse public classification datasets.	['Vincent Lemaire', 'Alexandre Reiffers-Masson', 'Sandrine Vaton', 'Stéphane Gosselin', 'Joachim Flocon-Cholet', 'Colin Troisemaine']	2022-09-02	null	null	null	null	['novel-class-discovery', 'novel-class-discovery']	['computer-vision', 'methodology']	[ 4.22993958e-01 -2.95835696e-02 -5.81419051e-01 -6.71646416e-01 -1.22718310e+00 -7.90409863e-01 3.96194547e-01 3.05928648e-01 -6.00068383e-02 1.18613482e+00 -1.91467345e-01 -1.80554911e-01 -2.93511003e-01 -6.52283907e-01 -5.77628613e-01 -9.63753045e-01 1.09572962e-01 1.19448411e+00 8.77899006e-02 3.05862337e-01 1.02667734e-01 6.03044212e-01 -1.68758035e+00 7.24456251e-01 6.39938712e-01 9.96769905e-01 -1.81045368e-01 2.90654507e-02 -3.46639097e-01 7.26070881e-01 -6.48344100e-01 -4.16577578e-01 5.42954028e-01 -5.39863527e-01 -1.10798538e+00 4.47526574e-01 8.95587981e-01 4.89709437e-01 1.33835554e-01 9.16210294e-01 5.08964956e-01 3.00837308e-01 8.57567966e-01 -1.44818151e+00 -3.60513687e-01 7.24793017e-01 -7.41562784e-01 5.00750169e-02 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8bdc6466-a4ef-48a4-b839-d544f4184e67	open-vocabulary-multi-label-classification	2207.01887	null	https://arxiv.org/abs/2207.01887v2	https://arxiv.org/pdf/2207.01887v2.pdf	Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfer	Real-world recognition system often encounters the challenge of unseen labels. To identify such unseen labels, multi-label zero-shot learning (ML-ZSL) focuses on transferring knowledge by a pre-trained textual label embedding (e.g., GloVe). However, such methods only exploit single-modal knowledge from a language model, while ignoring the rich semantic information inherent in image-text pairs. Instead, recently developed open-vocabulary (OV) based methods succeed in exploiting such information of image-text pairs in object detection, and achieve impressive performance. Inspired by the success of OV-based methods, we propose a novel open-vocabulary framework, named multi-modal knowledge transfer (MKT), for multi-label classification. Specifically, our method exploits multi-modal knowledge of image-text pairs based on a vision and language pre-training (VLP) model. To facilitate transferring the image-text matching ability of VLP model, knowledge distillation is employed to guarantee the consistency of image and label embeddings, along with prompt tuning to further update the label embeddings. To further enable the recognition of multiple objects, a simple but effective two-stream module is developed to capture both local and global features. Extensive experimental results show that our method significantly outperforms state-of-the-art methods on public benchmark datasets. The source code is available at https://github.com/sunanhe/MKT.	['Shu-Tao Xia', 'Bo Ren', 'Ruizhi Qiao', 'Tao Dai', 'Taian Guo', 'Sunan He']	2022-07-05	null	null	null	null	['multi-label-zero-shot-learning']	['computer-vision']	[ 2.69848257e-01 -3.56462419e-01 -3.96536738e-01 -4.48755980e-01 -1.09040749e+00 -5.07251918e-01 7.53127158e-01 3.30293477e-02 -3.71736646e-01 2.09058434e-01 -1.57213017e-01 1.06170245e-01 2.49265969e-01 -6.00821257e-01 -7.38695681e-01 -7.39764452e-01 5.92869103e-01 3.42076719e-01 1.13139138e-01 1.05244480e-01 3.15628387e-02 1.01044355e-02 -1.72801495e+00 3.54217231e-01 3.75516951e-01 1.28304267e+00 1.99830890e-01 3.23619574e-01 -3.98153037e-01 8.62329543e-01 2.47735362e-02 -4.76351768e-01 1.97121516e-01 -1.65590361e-01 -7.95504212e-01 3.43448341e-01 7.19125509e-01 -4.28018659e-01 -4.46789175e-01 1.15490580e+00 4.37595785e-01 4.28739451e-02 7.20628023e-01 -1.45854557e+00 -1.13702488e+00 1.11862883e-01 -4.73674655e-01 -1.77929565e-01 2.05227792e-01 3.15766454e-01 1.10599434e+00 -1.45709109e+00 5.81952810e-01 1.12027848e+00 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69254aa0-051a-4e8e-8cc4-4ef239142396	conformal-prediction-for-text-infilling-and	2111.02592	null	https://arxiv.org/abs/2111.02592v1	https://arxiv.org/pdf/2111.02592v1.pdf	Conformal prediction for text infilling and part-of-speech prediction	Modern machine learning algorithms are capable of providing remarkably accurate point-predictions; however, questions remain about their statistical reliability. Unlike conventional machine learning methods, conformal prediction algorithms return confidence sets (i.e., set-valued predictions) that correspond to a given significance level. Moreover, these confidence sets are valid in the sense that they guarantee finite sample control over type 1 error probabilities, allowing the practitioner to choose an acceptable error rate. In our paper, we propose inductive conformal prediction (ICP) algorithms for the tasks of text infilling and part-of-speech (POS) prediction for natural language data. We construct new conformal prediction-enhanced bidirectional encoder representations from transformers (BERT) and bidirectional long short-term memory (BiLSTM) algorithms for POS tagging and a new conformal prediction-enhanced BERT algorithm for text infilling. We analyze the performance of the algorithms in simulations using the Brown Corpus, which contains over 57,000 sentences. Our results demonstrate that the ICP algorithms are able to produce valid set-valued predictions that are small enough to be applicable in real-world applications. We also provide a real data example for how our proposed set-valued predictions can improve machine generated audio transcriptions.	['Jonathan P Williams', 'Emiliano Planchon', 'Maxwell Lovig', 'Carolina Kapper', 'Jack Ferrell', 'Jing Ding', 'Neil Dey']	2021-11-04	null	null	null	null	['text-infilling']	['natural-language-processing']	[ 4.34880674e-01 5.14601350e-01 -2.61182666e-01 -6.87011242e-01 -1.41433501e+00 -2.61183023e-01 5.38817883e-01 3.61865461e-01 -1.75573900e-01 8.41387331e-01 3.46365005e-01 -6.56728745e-01 -1.05396494e-01 -7.38322854e-01 -8.64404559e-01 -4.38927799e-01 -3.67929667e-01 6.01345420e-01 4.26651627e-01 -4.75213937e-02 2.94004828e-01 4.53307927e-02 -1.43902826e+00 6.29273713e-01 8.42668593e-01 9.00687099e-01 3.69672537e-01 8.44653487e-01 -8.49321932e-02 8.02617550e-01 -3.43111217e-01 -6.97536230e-01 -3.05555344e-01 -2.70992011e-01 -7.13381529e-01 -6.29491627e-01 7.31817558e-02 2.59381473e-01 1.77202597e-01 9.41352069e-01 2.87543148e-01 6.44333065e-02 1.02692735e+00 -1.13562393e+00 -4.70128745e-01 1.10655630e+00 -1.29222199e-01 -2.75281463e-02 2.58424401e-01 -6.23702407e-01 1.44161284e+00 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30453fd9-b54e-4d74-962c-d11a8e1b1364	mvimgnet-a-large-scale-dataset-of-multi-view	2303.06042	null	https://arxiv.org/abs/2303.06042v1	https://arxiv.org/pdf/2303.06042v1.pdf	MVImgNet: A Large-scale Dataset of Multi-view Images	Being data-driven is one of the most iconic properties of deep learning algorithms. The birth of ImageNet drives a remarkable trend of "learning from large-scale data" in computer vision. Pretraining on ImageNet to obtain rich universal representations has been manifested to benefit various 2D visual tasks, and becomes a standard in 2D vision. However, due to the laborious collection of real-world 3D data, there is yet no generic dataset serving as a counterpart of ImageNet in 3D vision, thus how such a dataset can impact the 3D community is unraveled. To remedy this defect, we introduce MVImgNet, a large-scale dataset of multi-view images, which is highly convenient to gain by shooting videos of real-world objects in human daily life. It contains 6.5 million frames from 219,188 videos crossing objects from 238 classes, with rich annotations of object masks, camera parameters, and point clouds. The multi-view attribute endows our dataset with 3D-aware signals, making it a soft bridge between 2D and 3D vision. We conduct pilot studies for probing the potential of MVImgNet on a variety of 3D and 2D visual tasks, including radiance field reconstruction, multi-view stereo, and view-consistent image understanding, where MVImgNet demonstrates promising performance, remaining lots of possibilities for future explorations. Besides, via dense reconstruction on MVImgNet, a 3D object point cloud dataset is derived, called MVPNet, covering 87,200 samples from 150 categories, with the class label on each point cloud. Experiments show that MVPNet can benefit the real-world 3D object classification while posing new challenges to point cloud understanding. MVImgNet and MVPNet will be publicly available, hoping to inspire the broader vision community.	['Xiaoguang Han', 'Shuguang Cui', 'GuanYing Chen', 'Tianyou Liang', 'Zhangyang Xiong', 'Chenming Zhu', 'Zizheng Yan', 'Yushuang Wu', 'Chongjie Ye', 'Haolin Liu', 'Yidan Zhang', 'Mutian Xu', 'Xianggang Yu']	2023-03-10	null	http://openaccess.thecvf.com//content/CVPR2023/html/Yu_MVImgNet_A_Large-Scale_Dataset_of_Multi-View_Images_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Yu_MVImgNet_A_Large-Scale_Dataset_of_Multi-View_Images_CVPR_2023_paper.pdf	cvpr-2023-1	['3d-object-classification']	['computer-vision']	[-0.11874713 -0.17988297 -0.11320394 -0.39356568 -0.49641985 -0.7257856 0.6775549 -0.46322247 -0.06587857 0.08496366 0.10527376 -0.10206644 -0.05390731 -0.8192262 -1.098685 -0.836285 0.07403466 0.5477881 0.12538946 -0.35228264 0.12690566 0.8562807 -1.8455703 0.37886658 0.2693822 1.2252151 0.6153729 0.29037675 -0.07878815 0.31948388 -0.3048131 -0.37889215 0.65896565 0.12069838 -0.5408521 0.3189666 0.991114 -0.34115955 -0.39441103 0.93088955 0.50906855 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a1db2729-8268-457e-842e-84d2b63c755f	gosum-extractive-summarization-of-long	2211.10247	null	https://arxiv.org/abs/2211.10247v2	https://arxiv.org/pdf/2211.10247v2.pdf	GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse state	Extracting summaries from long documents can be regarded as sentence classification using the structural information of the documents. How to use such structural information to summarize a document is challenging. In this paper, we propose GoSum, a novel graph and reinforcement learning based extractive model for long-paper summarization. In particular, GoSum encodes sentence states in reinforcement learning by building a heterogeneous graph for each input document at different discourse levels. An edge in the graph reflects the discourse hierarchy of a document for restraining the semantic drifts across section boundaries. We evaluate GoSum on two datasets of scientific articles summarization: PubMed and arXiv. The experimental results have demonstrated that GoSum achieve state-of-the-art results compared with strong baselines of both extractive and abstractive models. The ablation studies further validate that the performance of our GoSum benefits from the use of discourse information.	['Shanfeng Zhu', 'Hong Zhou', 'Xiaodi Huang', 'Junyi Bian']	2022-11-18	null	null	null	null	['sentence-classification', 'extractive-summarization', 'document-summarization']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[ 3.30007285e-01 9.00676370e-01 -7.01865613e-01 -1.44906342e-01 -9.00768042e-01 -5.38855791e-01 6.09455287e-01 7.47261405e-01 -2.14795679e-01 1.13324523e+00 1.15111923e+00 -9.70541984e-02 -1.83763262e-02 -5.18611670e-01 -1.05507255e+00 -4.34066594e-01 -4.46505658e-02 2.70502359e-01 1.85657933e-01 -3.05774689e-01 6.44375443e-01 -5.86934909e-02 -9.72861886e-01 7.76544929e-01 1.45081151e+00 1.77232966e-01 1.84574425e-01 8.99773180e-01 -4.02102828e-01 1.27838969e+00 -1.15330458e+00 -3.79909009e-01 -4.64166462e-01 -9.10762370e-01 -1.20291221e+00 -7.24879280e-02 7.72657394e-01 -1.78755343e-01 -6.99488282e-01 1.00823927e+00 5.93431473e-01 1.53981939e-01 7.89543331e-01 -8.10047328e-01 -9.13472891e-01 1.50774658e+00 -5.42456627e-01 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be29e395-c4cf-4a00-a4c1-948600bbb0c5	applying-multilingual-and-monolingual	null	null	https://aclanthology.org/2020.vardial-1.18	https://aclanthology.org/2020.vardial-1.18.pdf	Applying Multilingual and Monolingual Transformer-Based Models for Dialect Identification	We study the ability of large fine-tuned transformer models to solve a binary classification task of dialect identification, with a special interest in comparing the performance of multilingual to monolingual ones. The corpus analyzed contains Romanian and Moldavian samples from the news domain, as well as tweets for assessing the performance. We find that the monolingual models are superior to the multilingual ones and the best results are obtained using an SVM ensemble of 5 different transformer-based models. We provide our experimental results and an analysis of the attention mechanisms of the best-performing individual classifiers to explain their decisions. The code we used was released under an open-source license.	['Vlad Ștefănescu', 'Cristian Popa']	null	null	null	null	vardial-coling-2020-12	['dialect-identification']	['natural-language-processing']	[-4.20320958e-01 -9.19242278e-02 -3.97502393e-01 -5.26254773e-01 -9.33640003e-01 -8.66952300e-01 9.14920509e-01 1.25206783e-01 -5.23017228e-01 6.72825575e-01 4.30042028e-01 -6.34048700e-01 -2.45238289e-01 -4.98951763e-01 -5.01009107e-01 -6.61180735e-01 4.93287109e-02 1.11991286e+00 7.37659112e-02 -7.14003742e-01 2.50794083e-01 2.11478502e-01 -1.40563595e+00 4.98501837e-01 1.31477022e+00 8.07011306e-01 1.63417041e-01 3.23962957e-01 -1.00975975e-01 7.16431260e-01 -3.46534163e-01 -1.12254131e+00 1.51471585e-01 -9.60513279e-02 -9.42098141e-01 -3.90925437e-01 7.12480664e-01 2.29792431e-01 -3.04610342e-01 1.09430552e+00 4.17129785e-01 -3.52081805e-01 8.54225457e-01 -9.84055161e-01 -1.03897727e+00 1.18780565e+00 -3.47466604e-03 7.01710939e-01 4.39580023e-01 -1.77946091e-01 1.26299739e+00 -7.35462964e-01 5.72145283e-01 1.36993933e+00 8.17390442e-01 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482bec83-32f7-47d5-ad9a-89bd46732afc	end-to-end-sound-source-separation	1811.0185	null	https://arxiv.org/abs/1811.01850v2	https://arxiv.org/pdf/1811.01850v2.pdf	End-to-End Sound Source Separation Conditioned On Instrument Labels	Can we perform an end-to-end music source separation with a variable number of sources using a deep learning model? We present an extension of the Wave-U-Net model which allows end-to-end monaural source separation with a non-fixed number of sources. Furthermore, we propose multiplicative conditioning with instrument labels at the bottleneck of the Wave-U-Net and show its effect on the separation results. This approach leads to other types of conditioning such as audio-visual source separation and score-informed source separation.	['Olga Slizovskaia', 'Gloria Haro', 'Emilia Gomez', 'Leo Kim']	2018-11-05	null	null	null	null	['music-source-separation']	['music']	[ 2.90963873e-02 -2.72947192e-01 4.28183377e-01 -1.64768219e-01 -1.43907070e+00 -7.82183468e-01 2.42359638e-01 6.08774275e-02 -2.25536749e-01 5.70680380e-01 5.90288579e-01 6.48809597e-02 -3.85288894e-01 -2.73331881e-01 -7.06908882e-01 -6.81797683e-01 -2.45312303e-01 1.59624606e-01 1.40373483e-01 -1.93894550e-01 -9.17999297e-02 1.39505088e-01 -1.55942416e+00 4.65714097e-01 6.09121084e-01 1.05312264e+00 5.18467613e-02 1.32874489e+00 2.48554513e-01 7.79147923e-01 -9.39144194e-01 5.88590801e-02 4.20515329e-01 -1.06552255e+00 -3.21653098e-01 -4.30034637e-01 6.59932017e-01 -1.34075254e-01 3.24694440e-02 9.18937624e-01 1.26393139e+00 3.91125798e-01 6.75113857e-01 -1.09489822e+00 -1.90581575e-01 8.89354169e-01 -4.06531125e-01 1.58171177e-01 4.10318941e-01 -1.60771504e-01 1.07994306e+00 -6.46756113e-01 3.26471090e-01 9.67669606e-01 1.01544678e+00 5.96471541e-02 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-3.13382834e-01 -1.15587986e+00 -9.53559339e-01 3.13529432e-01 9.86805618e-01 -1.93205461e-01 -6.76268280e-01 3.95961434e-01]	[15.460905075073242, 5.590144157409668]
e304f0d6-2e1d-48db-b2e0-2de65cbc71a3	exemplar-based-face-parsing	null	null	http://openaccess.thecvf.com/content_cvpr_2013/html/Smith_Exemplar-Based_Face_Parsing_2013_CVPR_paper.html	http://openaccess.thecvf.com/content_cvpr_2013/papers/Smith_Exemplar-Based_Face_Parsing_2013_CVPR_paper.pdf	Exemplar-Based Face Parsing	In this work, we propose an exemplar-based face image segmentation algorithm. We take inspiration from previous works on image parsing for general scenes. Our approach assumes a database of exemplar face images, each of which is associated with a hand-labeled segmentation map. Given a test image, our algorithm first selects a subset of exemplar images from the database, Our algorithm then computes a nonrigid warp for each exemplar image to align it with the test image. Finally, we propagate labels from the exemplar images to the test image in a pixel-wise manner, using trained weights to modulate and combine label maps from different exemplars. We evaluate our method on two challenging datasets and compare with two face parsing algorithms and a general scene parsing algorithm. We also compare our segmentation results with contour-based face alignment results; that is, we first run the alignment algorithms to extract contour points and then derive segments from the contours. Our algorithm compares favorably with all previous works on all datasets evaluated.	['Brandon M. Smith', 'Li Zhang', 'Jianchao Yang', 'Zhe Lin', 'Jonathan Brandt']	2013-06-01	null	null	null	cvpr-2013-6	['face-parsing']	['computer-vision']	[ 8.57306063e-01 2.86919296e-01 -5.31711355e-02 -9.00983155e-01 -8.53607416e-01 -7.60055602e-01 4.83417839e-01 -2.62033314e-01 -3.37132841e-01 3.26890886e-01 -1.66733146e-01 1.25006571e-01 1.34455357e-02 -7.02380002e-01 -7.90561140e-01 -6.27062142e-01 1.33204937e-01 1.11777270e+00 2.64710665e-01 9.20668542e-02 2.30742618e-01 9.02613878e-01 -1.44499934e+00 2.58566380e-01 4.36713934e-01 8.67047548e-01 -1.41777620e-01 5.00902236e-01 -4.38039690e-01 1.48441464e-01 -5.23317158e-01 -7.24214137e-01 3.61861885e-01 -7.28776157e-01 -1.11717677e+00 7.99223065e-01 1.17158473e+00 -3.81531082e-02 1.81409389e-01 1.02285075e+00 2.13621810e-01 -3.70496213e-02 7.13980734e-01 -1.18038285e+00 -2.47992247e-01 5.57660401e-01 -1.00515366e+00 -1.61630496e-01 1.97420791e-01 -1.06560558e-01 6.05761588e-01 -8.53050172e-01 1.18627310e+00 1.45599723e+00 6.02158129e-01 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d69a8fcd-9630-44d4-8bfa-d294fff99536	ca-spacenet-counterfactual-analysis-for-6d	2207.07869	null	https://arxiv.org/abs/2207.07869v1	https://arxiv.org/pdf/2207.07869v1.pdf	CA-SpaceNet: Counterfactual Analysis for 6D Pose Estimation in Space	Reliable and stable 6D pose estimation of uncooperative space objects plays an essential role in on-orbit servicing and debris removal missions. Considering that the pose estimator is sensitive to background interference, this paper proposes a counterfactual analysis framework named CASpaceNet to complete robust 6D pose estimation of the spaceborne targets under complicated background. Specifically, conventional methods are adopted to extract the features of the whole image in the factual case. In the counterfactual case, a non-existent image without the target but only the background is imagined. Side effect caused by background interference is reduced by counterfactual analysis, which leads to unbiased prediction in final results. In addition, we also carry out lowbit-width quantization for CA-SpaceNet and deploy part of the framework to a Processing-In-Memory (PIM) accelerator on FPGA. Qualitative and quantitative results demonstrate the effectiveness and efficiency of our proposed method. To our best knowledge, this paper applies causal inference and network quantization to the 6D pose estimation of space-borne targets for the first time. The code is available at https://github.com/Shunli-Wang/CA-SpaceNet.	['Lihua Zhang', 'Chixiao Chen', 'Peng Zhai', 'Liuzhen Su', 'Dingkang Yang', 'Bo Jiao', 'Shuaibing Wang', 'Shunli Wang']	2022-07-16	null	null	null	null	['6d-pose-estimation-1']	['computer-vision']	[ 2.67013222e-01 9.47725326e-02 -8.76120403e-02 -1.54321909e-01 -2.90689290e-01 -5.41707039e-01 6.43691838e-01 -3.49370629e-01 -3.69959474e-01 1.06779742e+00 1.96275622e-01 -6.86073482e-01 -6.06312573e-01 -7.45953619e-01 -7.26148427e-01 -8.74516368e-01 -4.03712958e-01 7.75414109e-02 -1.06126722e-02 -2.13616073e-01 1.90166935e-01 8.60611379e-01 -1.38974619e+00 -1.14228301e-01 5.06132424e-01 1.25187194e+00 1.19455986e-01 4.46702629e-01 6.03687048e-01 4.97244745e-01 -7.38901496e-01 -1.69659898e-01 6.18816853e-01 -1.55512288e-01 -8.87619928e-02 -1.52470902e-01 2.04990134e-01 -5.06019711e-01 -6.68514073e-01 1.23006439e+00 6.89736307e-01 7.01042637e-02 5.23383915e-01 -1.41221297e+00 3.86526525e-01 3.53390604e-01 -5.37461460e-01 4.05450255e-01 8.70276242e-02 5.40684283e-01 4.35855895e-01 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febed65e-54e2-4ab0-8829-b0b227327451	end-to-end-attention-based-large-vocabulary	1508.04395	null	http://arxiv.org/abs/1508.04395v2	http://arxiv.org/pdf/1508.04395v2.pdf	End-to-End Attention-based Large Vocabulary Speech Recognition	Many of the current state-of-the-art Large Vocabulary Continuous Speech Recognition Systems (LVCSR) are hybrids of neural networks and Hidden Markov Models (HMMs). Most of these systems contain separate components that deal with the acoustic modelling, language modelling and sequence decoding. We investigate a more direct approach in which the HMM is replaced with a Recurrent Neural Network (RNN) that performs sequence prediction directly at the character level. Alignment between the input features and the desired character sequence is learned automatically by an attention mechanism built into the RNN. For each predicted character, the attention mechanism scans the input sequence and chooses relevant frames. We propose two methods to speed up this operation: limiting the scan to a subset of most promising frames and pooling over time the information contained in neighboring frames, thereby reducing source sequence length. Integrating an n-gram language model into the decoding process yields recognition accuracies similar to other HMM-free RNN-based approaches.	['Philemon Brakel', 'Jan Chorowski', 'Yoshua Bengio', 'Dmitriy Serdyuk', 'Dzmitry Bahdanau']	2015-08-18	null	null	null	null	['acoustic-modelling']	['speech']	[ 7.22189009e-01 5.18501103e-02 -2.42124721e-01 -4.48343515e-01 -8.46862555e-01 -2.48763517e-01 6.38867319e-01 -1.01512708e-01 -7.40942001e-01 5.92810333e-01 3.39125276e-01 -6.07362747e-01 6.01716816e-01 -5.14440536e-01 -5.92420399e-01 -8.30806077e-01 2.09158167e-01 4.80075210e-01 5.87174237e-01 5.20966388e-02 4.08779025e-01 7.07661688e-01 -1.61406243e+00 4.79798317e-01 2.78873175e-01 7.37552464e-01 7.09896505e-01 1.26325500e+00 -5.57299078e-01 1.07899058e+00 -5.37608862e-01 1.10902488e-01 -2.64934838e-01 -7.45171845e-01 -8.58605862e-01 2.01443717e-01 -5.20907305e-02 -3.78462821e-02 -3.73265177e-01 8.53024483e-01 4.19678658e-01 3.59569550e-01 3.44263315e-01 -4.16726261e-01 -4.08799410e-01 4.81207758e-01 -8.35309625e-02 3.53365451e-01 4.82238054e-01 8.55883360e-02 8.92824352e-01 -1.17653930e+00 5.85191965e-01 1.40361059e+00 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eb458e48-0279-467e-9f0a-b49e577874d5	a-locally-linear-procedure-for-word	null	null	https://aclanthology.org/2020.coling-main.528	https://aclanthology.org/2020.coling-main.528.pdf	A Locally Linear Procedure for Word Translation	Learning a mapping between word embeddings of two languages given a dictionary is an important problem with several applications. A common mapping approach is using an orthogonal matrix. The Orthogonal Procrustes Analysis (PA) algorithm can be applied to find the optimal orthogonal matrix. This solution restricts the expressiveness of the translation model which may result in sub-optimal translations. We propose a natural extension of the PA algorithm that uses multiple orthogonal translation matrices to model the mapping and derive an algorithm to learn these multiple matrices. We achieve better performance in a bilingual word translation task and a cross-lingual word similarity task compared to the single matrix baseline. We also show how multiple matrices can model multiple senses of a word.	['Jacob Goldberger', 'Hagai Taitelbaum', 'Soham Dan']	2020-12-01	null	null	null	coling-2020-8	['word-similarity']	['natural-language-processing']	[ 2.42928881e-02 -3.22362959e-01 -5.47096848e-01 -1.86063871e-01 -7.37205386e-01 -8.54602933e-01 7.30108440e-01 -9.56007242e-02 -5.47171116e-01 4.60774183e-01 3.80912423e-01 -5.73939621e-01 6.78276718e-02 -5.36351025e-01 -5.48539162e-01 -4.53801036e-01 2.16790766e-01 6.66208744e-01 -4.60609309e-02 -5.10594964e-01 1.62478507e-01 1.71306968e-01 -8.45378578e-01 1.16815053e-01 9.17654395e-01 2.92720139e-01 4.81826454e-01 3.51317257e-01 -4.71100777e-01 -1.44031972e-01 -1.17843829e-01 -6.61074698e-01 8.37367833e-01 -5.42674482e-01 -6.14768803e-01 1.49976918e-02 3.74192178e-01 1.68334439e-01 -7.44377822e-02 1.26599669e+00 3.43040019e-01 -1.23011498e-02 7.32380629e-01 -1.10518217e+00 -9.93751109e-01 6.06148005e-01 -6.50442958e-01 2.25137874e-01 4.47534919e-01 -3.78122091e-01 1.43586624e+00 -1.28228784e+00 5.22838891e-01 1.39127219e+00 5.82448840e-01 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6dd58f2f-2716-41a7-88e0-fdce11ed13f5	a-survey-of-pansharpening-methods-with-a-new	1606.05703	null	http://arxiv.org/abs/1606.05703v1	http://arxiv.org/pdf/1606.05703v1.pdf	A Survey of Pansharpening Methods with A New Band-Decoupled Variational Model	Most satellites decouple the acquisition of a panchromatic image at high spatial resolution from the acquisition of a multispectral image at lower spatial resolution. Pansharpening is a fusion technique used to increase the spatial resolution of the multispectral data while simultaneously preserving its spectral information. In this paper, we consider pansharpening as an optimization problem minimizing a cost function with a nonlocal regularization term. The energy functional which is to be minimized decouples for each band, thus permitting the application to misregistered spectral components. This requirement is achieved by dropping the, commonly used, assumption that relates the spectral and panchromatic modalities by a linear transformation. Instead, a new constraint that preserves the radiometric ratio between the panchromatic and each spectral component is introduced. An exhaustive performance comparison of the proposed fusion method with several classical and state-of-the-art pansharpening techniques illustrates its superiority in preserving spatial details, reducing color distortions, and avoiding the creation of aliasing artifacts.	['Gwendoline Blanchet', 'Bartomeu Coll', 'Catalina Sbert', 'Antoni Buades', 'Joan Duran']	2016-06-17	null	null	null	null	['pansharpening']	['computer-vision']	[ 8.58585536e-01 -5.93872190e-01 5.56112565e-02 -1.43228799e-01 -7.62658596e-01 -8.30585599e-01 4.84662473e-01 2.17710212e-01 -6.11556351e-01 7.37290859e-01 -1.53196946e-01 -8.86554047e-02 -4.62974787e-01 -1.05557823e+00 -2.85533994e-01 -1.29754639e+00 4.15049493e-01 -2.75396612e-02 -5.30465282e-02 -2.12626308e-01 -6.44283593e-02 8.79732132e-01 -1.57305396e+00 -3.15631144e-02 1.25338638e+00 8.14245403e-01 2.73187816e-01 4.80110854e-01 3.06573212e-01 1.99231625e-01 -3.06715637e-01 -6.22140579e-02 6.81068957e-01 -6.05072498e-01 -3.98638725e-01 4.97737557e-01 6.57970369e-01 -2.48790160e-03 -6.47505224e-02 1.67482555e+00 1.32963330e-01 2.75703967e-01 4.03354466e-01 -7.23625243e-01 -4.72952068e-01 3.26421820e-02 -1.14941525e+00 1.09665310e-02 -4.17558588e-02 -2.82930464e-01 9.03966248e-01 -3.68398130e-01 2.21271843e-01 5.39748013e-01 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70635493-9980-4df3-bc9c-4bbdc4d15081	context-endcoding-for-neural-network-based	1910.10798	null	https://arxiv.org/abs/1910.10798v1	https://arxiv.org/pdf/1910.10798v1.pdf	Context-endcoding for neural network based skull stripping in magnetic resonance imaging	Skull stripping is usually the first step for most brain analysisprocess in magnetic resonance images. A lot of deep learn-ing neural network based methods have been developed toachieve higher accuracy. Since the 3D deep learning modelssuffer from high computational cost and are subject to GPUmemory limit challenge, a variety of 2D deep learning meth-ods have been developed. However, existing 2D deep learn-ing methods are not equipped to effectively capture 3D se-mantic information that is needed to achieve higher accuracy.In this paper, we propose a context-encoding method to em-power the 2D network to capture the 3D context information.For the context-encoding method, firstly we encode the 2Dfeatures of original 2D network, secondly we encode the sub-volume of 3D MRI images, finally we fuse the encoded 2Dfeatures and 3D features with semantic encoding classifica-tion loss. To get computational efficiency, although we en-code the sub-volume of 3D MRI images instead of buildinga 3D neural network, extensive experiments on three bench-mark Datasets demonstrate our method can achieve superioraccuracy to state-of-the-art alternative methods with the dicescore 99.6% on NFBS and 99.09 % on LPBA40 and 99.17 %on OASIS.	['Yong Fan', 'Yuemeng Li', 'Borui Xiao', 'Zhen Liu']	2019-10-23	null	null	null	null	['skull-stripping']	['medical']	[-6.71983957e-02 -7.17039853e-02 9.74544659e-02 -5.26547968e-01 -6.58092856e-01 1.16231143e-01 2.76944220e-01 3.90809588e-02 -6.11896694e-01 7.17987776e-01 6.63300529e-02 -2.33112127e-01 -2.37358555e-01 -7.50100791e-01 -6.61383927e-01 -8.30766261e-01 -3.75757933e-01 4.08491939e-01 3.46432626e-01 9.35525633e-03 1.87877696e-02 6.47818089e-01 -1.24790370e+00 4.15326595e-01 7.14232922e-01 1.30923343e+00 3.44142765e-01 2.98157722e-01 -2.25980714e-01 6.03118241e-01 -3.87598962e-01 -2.84625348e-02 2.79411525e-01 -3.03893149e-01 -7.89258540e-01 -4.27965783e-02 3.23963702e-01 -5.23425221e-01 -4.51512486e-01 1.24157453e+00 8.00122738e-01 -6.80533890e-03 6.92039490e-01 -8.68249357e-01 -3.74441653e-01 3.05132747e-01 -7.07984984e-01 7.77520776e-01 -1.42161041e-01 -1.52233854e-01 2.46326044e-01 -9.86218810e-01 3.90561491e-01 1.03461838e+00 7.45034575e-01 4.60489750e-01 -1.02608895e+00 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a124e002-eb5a-47c4-afc0-f9285392ea77	rankdnn-learning-to-rank-for-few-shot	2211.1532	null	https://arxiv.org/abs/2211.15320v2	https://arxiv.org/pdf/2211.15320v2.pdf	RankDNN: Learning to Rank for Few-shot Learning	This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. In comparison to image classification, ranking relation classification is sample efficient and domain agnostic. Besides, it provides a new perspective on few-shot learning and is complementary to state-of-the-art methods. The core component of our deep neural network is a simple MLP, which takes as input an image triplet encoded as the difference between two vector-Kronecker products, and outputs a binary relevance ranking order. The proposed RankMLP can be built on top of any state-of-the-art feature extractors, and our entire deep neural network is called the ranking deep neural network, or RankDNN. Meanwhile, RankDNN can be flexibly fused with other post-processing methods. During the meta test, RankDNN ranks support images according to their similarity with the query samples, and each query sample is assigned the class label of its nearest neighbor. Experiments demonstrate that RankDNN can effectively improve the performance of its baselines based on a variety of backbones and it outperforms previous state-of-the-art algorithms on multiple few-shot learning benchmarks, including miniImageNet, tieredImageNet, Caltech-UCSD Birds, and CIFAR-FS. Furthermore, experiments on the cross-domain challenge demonstrate the superior transferability of RankDNN.The code is available at: https://github.com/guoqianyu-alberta/RankDNN.	['Wenqiang Zhang', 'Yizhou Yu', 'Weifeng Ge', 'Yanwei Fu', 'Xujun Wei', 'Hongtong Gong', 'Qianyu Guo']	2022-11-28	null	null	null	null	['relation-classification']	['natural-language-processing']	[ 2.06744745e-01 -2.77380854e-01 -5.89450657e-01 -4.60349768e-01 -1.07537007e+00 -1.41675817e-02 8.69385242e-01 5.64993657e-02 -4.23784941e-01 2.63110191e-01 2.48050630e-01 1.66138168e-02 -3.41103077e-01 -9.77834702e-01 -5.77479720e-01 -5.06284595e-01 -2.85070315e-02 5.43760955e-01 5.92582047e-01 -4.87186223e-01 9.92882773e-02 -2.08944343e-02 -1.95893133e+00 5.23473859e-01 5.57972550e-01 1.80118632e+00 1.72949079e-02 3.79305571e-01 1.92500040e-01 1.27517426e+00 -3.54385644e-01 -2.73624450e-01 2.25706428e-01 -2.00848922e-01 -8.86172950e-01 -3.43069136e-01 6.37771368e-01 -7.65273571e-01 -9.16553020e-01 1.22250068e+00 7.06846118e-01 5.91663957e-01 8.18994880e-01 -1.15341079e+00 -1.18019176e+00 4.68309551e-01 -3.81424427e-01 4.01301682e-01 8.78532678e-02 1.66169330e-01 1.42274845e+00 -1.24329805e+00 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e3a758d1-60b6-4c09-96c3-902abd98fd1a	learning-transferable-features-for-point	null	null	http://proceedings.neurips.cc/paper/2021/hash/b3b25a26a0828ea5d48d8f8aa0d6f9af-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/b3b25a26a0828ea5d48d8f8aa0d6f9af-Paper.pdf	Learning Transferable Features for Point Cloud Detection via 3D Contrastive Co-training	Most existing point cloud detection models require large-scale, densely annotated datasets. They typically underperform in domain adaptation settings, due to geometry shifts caused by different physical environments or LiDAR sensor configurations. Therefore, it is challenging but valuable to learn transferable features between a labeled source domain and a novel target domain, without any access to target labels. To tackle this problem, we introduce the framework of 3D Contrastive Co-training (3D-CoCo) with two technical contributions. First, 3D-CoCo is inspired by our observation that the bird-eye-view (BEV) features are more transferable than low-level geometry features. We thus propose a new co-training architecture that includes separate 3D encoders with domain-specific parameters, as well as a BEV transformation module for learning domain-invariant features. Second, 3D-CoCo extends the approach of contrastive instance alignment to point cloud detection, whose performance was largely hindered by the mismatch between the fictitious distribution of BEV features, induced by pseudo-labels, and the true distribution. The mismatch is greatly reduced by 3D-CoCo with transformed point clouds, which are carefully designed by considering specific geometry priors. We construct new domain adaptation benchmarks using three large-scale 3D datasets. Experimental results show that our proposed 3D-CoCo effectively closes the domain gap and outperforms the state-of-the-art methods by large margins.	['Chao Ma', 'Zhen Yang', 'Chaoqiang Ye', 'Hang Xu', 'Yunbo Wang', 'Chunwei Wang', 'Zeng Yihan']	2021-12-01	null	https://openreview.net/forum?id=iH1_KBzbwQq	https://openreview.net/pdf?id=iH1_KBzbwQq	neurips-2021-12	['cloud-detection']	['computer-vision']	[ 5.61599843e-02 -1.79949626e-01 -1.49671584e-01 -4.74365860e-01 -9.81571138e-01 -8.74318838e-01 8.77547920e-01 -6.59851506e-02 -2.64540076e-01 5.34310460e-01 -3.07386398e-01 -8.46793503e-02 1.53114095e-01 -7.99917340e-01 -1.20134330e+00 -4.74127322e-01 2.08946690e-01 9.68860865e-01 4.91750240e-01 -1.66757137e-01 1.30348623e-01 7.38732219e-01 -1.53756297e+00 3.63658066e-03 9.03149068e-01 9.71046209e-01 2.88974226e-01 2.32870087e-01 -2.38048062e-01 5.99775761e-02 -4.26136315e-01 -2.44745731e-01 7.18261778e-01 3.19542997e-02 -4.89079386e-01 2.32197925e-01 8.67979527e-01 -2.20906943e-01 -1.46617979e-01 9.25181091e-01 4.05239016e-01 -4.50975336e-02 1.02455127e+00 -1.51401484e+00 -7.28628576e-01 -1.50218397e-01 -6.85079217e-01 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518b77b4-60ab-4d1f-a52c-f266316f6d61	multilingual-word-sense-disambiguation-with-1	2210.07447	null	https://arxiv.org/abs/2210.07447v1	https://arxiv.org/pdf/2210.07447v1.pdf	Multilingual Word Sense Disambiguation with Unified Sense Representation	As a key natural language processing (NLP) task, word sense disambiguation (WSD) evaluates how well NLP models can understand the lexical semantics of words under specific contexts. Benefited from the large-scale annotation, current WSD systems have achieved impressive performances in English by combining supervised learning with lexical knowledge. However, such success is hard to be replicated in other languages, where we only have limited annotations.In this paper, based on the multilingual lexicon BabelNet describing the same set of concepts across languages, we propose building knowledge and supervised-based Multilingual Word Sense Disambiguation (MWSD) systems. We build unified sense representations for multiple languages and address the annotation scarcity problem for MWSD by transferring annotations from rich-sourced languages to poorer ones. With the unified sense representations, annotations from multiple languages can be jointly trained to benefit the MWSD tasks. Evaluations of SemEval-13 and SemEval-15 datasets demonstrate the effectiveness of our methodology.	['Tong Zhang', 'Yangqiu Song', 'Hongming Zhang', 'Ying Su']	2022-10-14	multilingual-word-sense-disambiguation-with	https://aclanthology.org/2022.coling-1.368	https://aclanthology.org/2022.coling-1.368.pdf	coling-2022-10	['word-sense-disambiguation']	['natural-language-processing']	[-1.61457360e-01 8.92008021e-02 -5.56239307e-01 -2.80717760e-01 -7.10943401e-01 -8.01961184e-01 5.80407739e-01 7.32325137e-01 -8.37782919e-01 9.63761449e-01 6.34090483e-01 -2.18903467e-01 -3.23789008e-03 -7.02569425e-01 -1.72703609e-01 -1.72181800e-01 2.85710931e-01 6.25542164e-01 2.82830089e-01 -7.83301234e-01 -1.35723159e-01 -1.14289135e-01 -1.01980138e+00 1.22817688e-01 1.31583464e+00 4.59738612e-01 6.68543696e-01 -1.83805645e-01 -7.89304018e-01 2.90254891e-01 -4.65514719e-01 -3.78635406e-01 6.35685995e-02 -1.03287129e-02 -1.22431576e+00 -4.07597184e-01 1.45789102e-01 5.20519853e-01 2.30718598e-01 1.20528972e+00 5.06522298e-01 6.97239563e-02 2.84130365e-01 -9.86606836e-01 -9.32194829e-01 1.05315554e+00 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895e7143-c2eb-457d-9817-bc6db7892fdc	augmented-transformers-with-adaptive-n-grams	2302.14261	null	https://arxiv.org/abs/2302.14261v1	https://arxiv.org/pdf/2302.14261v1.pdf	Augmented Transformers with Adaptive n-grams Embedding for Multilingual Scene Text Recognition	While vision transformers have been highly successful in improving the performance in image-based tasks, not much work has been reported on applying transformers to multilingual scene text recognition due to the complexities in the visual appearance of multilingual texts. To fill the gap, this paper proposes an augmented transformer architecture with n-grams embedding and cross-language rectification (TANGER). TANGER consists of a primary transformer with single patch embeddings of visual images, and a supplementary transformer with adaptive n-grams embeddings that aims to flexibly explore the potential correlations between neighbouring visual patches, which is essential for feature extraction from multilingual scene texts. Cross-language rectification is achieved with a loss function that takes into account both language identification and contextual coherence scoring. Extensive comparative studies are conducted on four widely used benchmark datasets as well as a new multilingual scene text dataset containing Indonesian, English, and Chinese collected from tourism scenes in Indonesia. Our experimental results demonstrate that TANGER is considerably better compared to the state-of-the-art, especially in handling complex multilingual scene texts.	['Yaochu Jin', 'Zhihang Fang', 'Xueming Yan']	2023-02-28	null	null	null	null	['scene-text-recognition']	['computer-vision']	[ 1.49168223e-01 -4.33383256e-01 1.81795269e-01 -2.30488136e-01 -8.86996388e-01 -4.78692770e-01 9.68742192e-01 -1.86664134e-01 -6.42836392e-01 2.47106016e-01 5.02290487e-01 -3.20710957e-01 2.41733566e-01 -2.77642757e-01 -5.43573737e-01 -6.99064195e-01 3.59995574e-01 3.01567614e-01 4.38740142e-02 -3.80385250e-01 2.79976398e-01 3.30508918e-01 -1.49963999e+00 5.55792689e-01 1.03452659e+00 7.02808022e-01 5.28042257e-01 5.06137908e-01 -3.53180081e-01 7.31862187e-01 -2.61900246e-01 -8.14793169e-01 1.37906805e-01 -9.16160420e-02 -4.30364877e-01 4.96587366e-01 1.01124609e+00 2.43674349e-02 -5.15374839e-01 1.02226591e+00 7.09959924e-01 -3.46632302e-02 7.07245290e-01 -9.22357321e-01 -1.16932023e+00 5.43952882e-01 -7.05440521e-01 3.69740009e-01 2.68982470e-01 5.55570722e-02 1.11637473e+00 -1.56344235e+00 5.07840276e-01 1.45312512e+00 7.39226699e-01 -1.12431534e-02 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-1.09576464e+00 -3.64158750e-01 3.50196213e-01 2.57487565e-01 -6.46625817e-01 -3.77143860e-01 2.79637337e-01]	[11.732962608337402, 1.9959323406219482]
6b517b8f-b7d9-4c56-adfc-a2029273f33d	spatio-temporal-perception-distortion-trade	2307.01556	null	https://arxiv.org/abs/2307.01556v1	https://arxiv.org/pdf/2307.01556v1.pdf	Spatio-Temporal Perception-Distortion Trade-off in Learned Video SR	Perception-distortion trade-off is well-understood for single-image super-resolution. However, its extension to video super-resolution (VSR) is not straightforward, since popular perceptual measures only evaluate naturalness of spatial textures and do not take naturalness of flow (temporal coherence) into account. To this effect, we propose a new measure of spatio-temporal perceptual video quality emphasizing naturalness of optical flow via the perceptual straightness hypothesis (PSH) for meaningful spatio-temporal perception-distortion trade-off. We also propose a new architecture for perceptual VSR (PSVR) to explicitly enforce naturalness of flow to achieve realistic spatio-temporal perception-distortion trade-off according to the proposed measures. Experimental results with PVSR support the hypothesis that a meaningful perception-distortion tradeoff for video should account for the naturalness of motion in addition to naturalness of texture.	['A. Murat Tekalp', 'Nasrin Rahimi']	2023-07-04	null	null	null	null	['image-super-resolution', 'video-super-resolution', 'optical-flow-estimation', 'super-resolution']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision']	[ 3.83435935e-01 -2.40722701e-01 -1.87075630e-01 -3.33925545e-01 -1.01966485e-01 -3.74949545e-01 6.00715518e-01 -1.96926057e-01 -4.70705256e-02 6.61313474e-01 4.67595607e-01 -1.31915063e-01 -2.37958372e-01 -1.00097907e+00 -5.05088508e-01 -4.40693259e-01 -2.82497853e-01 -5.34439325e-01 6.44493520e-01 -4.05175269e-01 4.28104639e-01 5.79571366e-01 -1.75509322e+00 3.24817032e-01 1.01759052e+00 8.30393553e-01 4.20697927e-01 9.08538461e-01 3.40672433e-01 1.02095246e+00 -3.07577133e-01 -5.83825894e-02 3.30184639e-01 -6.62158966e-01 -7.92315245e-01 2.47896731e-01 6.32299364e-01 -6.38571799e-01 -3.02737236e-01 1.35501039e+00 1.05349407e-01 3.47180963e-01 5.37802160e-01 -9.85193193e-01 -1.04424441e+00 1.68189391e-01 -5.73020220e-01 6.96938097e-01 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4010558d-df8e-4867-9723-f0d32c904ff3	a-strong-baseline-for-batch-imitation	2302.02788	null	https://arxiv.org/abs/2302.02788v1	https://arxiv.org/pdf/2302.02788v1.pdf	A Strong Baseline for Batch Imitation Learning	Imitation of expert behaviour is a highly desirable and safe approach to the problem of sequential decision making. We provide an easy-to-implement, novel algorithm for imitation learning under a strict data paradigm, in which the agent must learn solely from data collected a priori. This paradigm allows our algorithm to be used for environments in which safety or cost are of critical concern. Our algorithm requires no additional hyper-parameter tuning beyond any standard batch reinforcement learning (RL) algorithm, making it an ideal baseline for such data-strict regimes. Furthermore, we provide formal sample complexity guarantees for the algorithm in finite Markov Decision Problems. In doing so, we formally demonstrate an unproven claim from Kearns & Singh (1998). On the empirical side, our contribution is twofold. First, we develop a practical, robust and principled evaluation protocol for offline RL methods, making use of only the dataset provided for model selection. This stands in contrast to the vast majority of previous works in offline RL, which tune hyperparameters on the evaluation environment, limiting the practical applicability when deployed in new, cost-critical environments. As such, we establish precedent for the development and fair evaluation of offline RL algorithms. Second, we evaluate our own algorithm on challenging continuous control benchmarks, demonstrating its practical applicability and competitiveness with state-of-the-art performance, despite being a simpler algorithm.	['Kamil Ciosek', 'Zhenwen Dai', 'Lucas Maystre', 'Matthew Smith']	2023-02-06	null	null	null	null	['continuous-control']	['playing-games']	[ 2.78526157e-01 3.35151464e-01 -4.81973976e-01 1.16295867e-01 -7.97112048e-01 -8.18376660e-01 6.84400439e-01 -5.96036054e-02 -9.11661088e-01 1.03218281e+00 -3.23647350e-01 -6.30937934e-01 -3.92126471e-01 -3.89544666e-01 -8.06237817e-01 -8.93847644e-01 -3.95203412e-01 5.45670986e-01 1.41954839e-01 -2.95435250e-01 1.78523809e-01 4.23076540e-01 -1.58665919e+00 -4.19631809e-01 5.65351009e-01 9.62199569e-01 1.49585560e-01 8.47438455e-01 8.95899653e-01 8.62965882e-01 -4.09497857e-01 -5.28397560e-02 6.32743478e-01 -5.33045769e-01 -8.05305064e-01 1.19357631e-01 -2.04627797e-01 -8.89197648e-01 -1.20429739e-01 7.39057899e-01 7.42625952e-01 3.41627359e-01 5.06158352e-01 -1.32848167e+00 4.27545905e-02 5.78003705e-01 -1.56000704e-01 -6.12042658e-02 3.68991286e-01 8.31197858e-01 1.06973028e+00 -1.34812117e-01 5.68221867e-01 9.99957502e-01 5.26352644e-01 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63cbd75b-e62d-4add-a8ca-67afef2280a1	the-massively-multilingual-natural-language	2212.06346	null	https://arxiv.org/abs/2212.06346v1	https://arxiv.org/pdf/2212.06346v1.pdf	The Massively Multilingual Natural Language Understanding 2022 (MMNLU-22) Workshop and Competition	Despite recent progress in Natural Language Understanding (NLU), the creation of multilingual NLU systems remains a challenge. It is common to have NLU systems limited to a subset of languages due to lack of available data. They also often vary widely in performance. We launch a three-phase approach to address the limitations in NLU and help propel NLU technology to new heights. We release a 52 language dataset called the Multilingual Amazon SLU resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant Evaluation, or MASSIVE, in an effort to address parallel data availability for voice assistants. We organize the Massively Multilingual NLU 2022 Challenge to provide a competitive environment and push the state-of-the art in the transferability of models into other languages. Finally, we host the first Massively Multilingual NLU workshop which brings these components together. The MMNLU workshop seeks to advance the science behind multilingual NLU by providing a platform for the presentation of new research in the field and connecting teams working on this research direction. This paper summarizes the dataset, workshop and the competition and the findings of each phase.	['Kay Rottmann', 'Jack FitzGerald', 'Charith Peris', 'Christopher Hench']	2022-12-13	null	null	null	null	['intent-classification', 'slot-filling']	['natural-language-processing', 'natural-language-processing']	[-2.87831515e-01 1.25126019e-01 -5.72985053e-01 -3.37731689e-01 -1.39965355e+00 -1.05312991e+00 7.04936028e-01 2.44631603e-01 -5.45189679e-01 9.90074813e-01 6.03602946e-01 -8.35084796e-01 1.51399001e-01 -3.70375723e-01 -5.23530245e-01 2.66344190e-01 3.46189648e-01 1.29065561e+00 1.07460894e-01 -5.22449017e-01 -2.21914783e-01 2.64476627e-01 -1.28256404e+00 4.20212626e-01 1.09920943e+00 5.10453522e-01 5.84814191e-01 5.80881238e-01 -7.61000812e-01 9.16969299e-01 -3.13944489e-01 2.25780569e-02 1.73159555e-01 -1.88542441e-01 -1.29550922e+00 -2.29615420e-01 4.98950630e-01 -2.09889621e-01 -2.73348708e-02 6.08464956e-01 7.78660417e-01 1.87334046e-01 2.14178190e-01 -1.14601648e+00 -5.37820160e-01 9.41524565e-01 -2.02053487e-02 7.26445243e-02 9.96092319e-01 -1.89586833e-01 1.44919312e+00 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4f7335dd-77a9-4f9e-b2e9-e1225de44a60	inception-architecture-and-residual	1912.04619	null	https://arxiv.org/abs/1912.04619v1	https://arxiv.org/pdf/1912.04619v1.pdf	Inception Architecture and Residual Connections in Classification of Breast Cancer Histology Images	This paper presents results of applying Inception v4 deep convolutional neural network to ICIAR-2018 Breast Cancer Classification Grand Challenge, part a. The Challenge task is to classify breast cancer biopsy results, presented in form of hematoxylin and eosin stained images. Breast cancer classification is of primary interest to the medical practitioners and thus binary classification of breast cancer images have been under investigation by many researchers, but multi-class categorization of histology breast images have been challenging due to the subtle differences among the categories. In this work extensive data augmentation is conducted to reduce overfitting and effectiveness of committee of several Inception v4 networks is studied. We report 89% accuracy on 4 class classification task and 93.7% on carcinoma/non-carcinoma two class classification task using our test set of 80 images.	['Hyongsuk Kim', 'Dinar Akhmetzanov', 'Denis Tarasov', 'Mohammad Ibrahim Sarker']	2019-12-10	null	null	null	null	['classification-of-breast-cancer-histology']	['medical']	[ 3.22882414e-01 9.41610485e-02 -4.44360554e-01 -5.32855690e-01 -8.75994503e-01 -4.03564841e-01 3.71250272e-01 6.26516938e-01 -6.99631631e-01 7.57437587e-01 -1.91386312e-01 -7.41078019e-01 -3.60202566e-02 -4.68411028e-01 -4.27454293e-01 -8.48927379e-01 -1.95785210e-01 5.84136128e-01 -1.96702391e-01 3.53104658e-02 -7.10696653e-02 7.67129302e-01 -8.47547889e-01 9.39108968e-01 1.17461264e-01 1.28814852e+00 -3.32018137e-01 1.27382410e+00 6.76465183e-02 8.42532158e-01 -4.34962988e-01 -1.83134839e-01 -7.72452354e-03 -2.15759259e-02 -1.03131747e+00 -1.60353407e-01 5.60851038e-01 -2.60346353e-01 -1.34329304e-01 9.92979228e-01 5.07131219e-01 -5.77713072e-01 1.05837023e+00 -1.14040816e+00 -2.52418995e-01 5.58143556e-01 -6.16814017e-01 7.79830575e-01 -4.86921400e-01 -8.38774145e-02 6.63339734e-01 -7.04304874e-01 7.00311840e-01 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ec475d40-9771-43a9-8b9a-cf0fc45ff4f6	dip-learning-discriminative-implicit-parts	2212.13906	null	https://arxiv.org/abs/2212.13906v2	https://arxiv.org/pdf/2212.13906v2.pdf	DiP: Learning Discriminative Implicit Parts for Person Re-Identification	In person re-identification (ReID) tasks, many works explore the learning of part features to improve the performance over global image features. Existing methods explicitly extract part features by either using a hand-designed image division or keypoints obtained with external visual systems. In this work, we propose to learn Discriminative implicit Parts (DiPs) which are decoupled from explicit body parts. Therefore, DiPs can learn to extract any discriminative features that can benefit in distinguishing identities, which is beyond predefined body parts (such as accessories). Moreover, we propose a novel implicit position to give a geometric interpretation for each DiP. The implicit position can also serve as a learning signal to encourage DiPs to be more position-equivariant with the identity in the image. Lastly, an additional DiP weighting is introduced to handle the invisible or occluded situation and further improve the feature representation of DiPs. Extensive experiments show that the proposed method achieves state-of-the-art performance on multiple person ReID benchmarks.	['Lin Ma', 'Yujie Zhong', 'Siyu Chen', 'Dengjie Li']	2022-12-24	null	null	null	null	['person-re-identification']	['computer-vision']	[-1.25731677e-01 1.17867455e-01 -3.95938307e-01 -6.23817742e-01 -2.61994392e-01 -4.40251797e-01 7.88657784e-01 -1.17510103e-01 -2.80492991e-01 4.85814661e-01 4.85364676e-01 3.90192777e-01 -9.11165550e-02 -5.77444434e-01 -7.66473114e-01 -7.39551961e-01 5.11126406e-02 3.12811643e-01 1.47246525e-01 -2.62346059e-01 1.07500054e-01 5.88943303e-01 -1.61737061e+00 3.16584632e-02 7.44461000e-01 8.05618048e-01 -1.37415022e-01 6.83168694e-02 -1.96984354e-02 2.21859500e-01 -6.06014609e-01 -6.26292229e-01 7.35803187e-01 -2.57774204e-01 -6.91432297e-01 1.06870539e-01 9.06732976e-01 -5.17542839e-01 -3.21118057e-01 1.06463075e+00 6.02833867e-01 2.81298645e-02 7.85348773e-01 -1.25282800e+00 -6.99204922e-01 4.17373925e-01 -1.05057204e+00 7.77985379e-02 6.30975246e-01 9.38425511e-02 7.98116386e-01 -8.48566592e-01 5.71072698e-01 1.63540697e+00 8.11662376e-01 5.39893210e-01 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dd79e1f1-6638-484e-999f-6bc97f66d802	transint-embedding-implication-rules-in-1	2007.00271	null	https://arxiv.org/abs/2007.00271v1	https://arxiv.org/pdf/2007.00271v1.pdf	TransINT: Embedding Implication Rules in Knowledge Graphs with Isomorphic Intersections of Linear Subspaces	Knowledge Graphs (KG), composed of entities and relations, provide a structured representation of knowledge. For easy access to statistical approaches on relational data, multiple methods to embed a KG into f(KG) $\in$ R^d have been introduced. We propose TransINT, a novel and interpretable KG embedding method that isomorphically preserves the implication ordering among relations in the embedding space. Given implication rules, TransINT maps set of entities (tied by a relation) to continuous sets of vectors that are inclusion-ordered isomorphically to relation implications. With a novel parameter sharing scheme, TransINT enables automatic training on missing but implied facts without rule grounding. On a benchmark dataset, we outperform the best existing state-of-the-art rule integration embedding methods with significant margins in link Prediction and triple Classification. The angles between the continuous sets embedded by TransINT provide an interpretable way to mine semantic relatedness and implication rules among relations.	['So Yeon Min', 'Preethi Raghavan', 'Peter Szolovits']	2020-07-01	null	https://openreview.net/forum?id=shkmWLRBXH	https://openreview.net/pdf?id=shkmWLRBXH	akbc-2020-6	['triple-classification']	['graphs']	[ 7.47553706e-02 8.08072567e-01 -7.26618409e-01 -5.14883339e-01 3.31778228e-01 -6.21245205e-01 5.19408762e-01 5.79645574e-01 1.12590296e-02 6.44686460e-01 4.31972980e-01 -5.20935774e-01 -8.86372447e-01 -1.36535609e+00 -6.66124165e-01 -4.35318589e-01 -6.10633552e-01 7.10505247e-01 1.12364680e-01 -5.08741379e-01 -3.01735640e-01 1.97519958e-01 -1.63436985e+00 4.77884620e-01 8.45808327e-01 1.02795649e+00 -3.95816118e-01 1.71416759e-01 -3.86443049e-01 1.20770597e+00 -2.47301683e-01 -1.27753699e+00 1.12657584e-01 -2.63252854e-01 -1.10622609e+00 -4.64794755e-01 2.23789573e-01 1.82443783e-01 -5.81352949e-01 8.61410022e-01 4.02459363e-03 8.27929676e-02 8.84674966e-01 -1.70249188e+00 -1.24958146e+00 1.14980805e+00 -1.42623976e-01 7.80573413e-02 6.08734846e-01 -7.94974864e-01 1.86855793e+00 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06ffb00f-3c46-40b6-8719-307064865b13	sejarah-dan-perkembangan-teknik-natural	2304.02746	null	https://arxiv.org/abs/2304.02746v1	https://arxiv.org/pdf/2304.02746v1.pdf	Sejarah dan Perkembangan Teknik Natural Language Processing (NLP) Bahasa Indonesia: Tinjauan tentang sejarah, perkembangan teknologi, dan aplikasi NLP dalam bahasa Indonesia	This study provides an overview of the history of the development of Natural Language Processing (NLP) in the context of the Indonesian language, with a focus on the basic technologies, methods, and practical applications that have been developed. This review covers developments in basic NLP technologies such as stemming, part-of-speech tagging, and related methods; practical applications in cross-language information retrieval systems, information extraction, and sentiment analysis; and methods and techniques used in Indonesian language NLP research, such as machine learning, statistics-based machine translation, and conflict-based approaches. This study also explores the application of NLP in Indonesian language industry and research and identifies challenges and opportunities in Indonesian language NLP research and development. Recommendations for future Indonesian language NLP research and development include developing more efficient methods and technologies, expanding NLP applications, increasing sustainability, further research into the potential of NLP, and promoting interdisciplinary collaboration. It is hoped that this review will help researchers, practitioners, and the government to understand the development of Indonesian language NLP and identify opportunities for further research and development.	['Mukhlis Amien']	2023-03-28	null	null	null	null	['part-of-speech-tagging']	['natural-language-processing']	[ 1.63883239e-01 -2.01536432e-01 -6.10189855e-01 -3.40288401e-01 -7.38106251e-01 -9.84553516e-01 4.29806948e-01 7.63967454e-01 -5.59956849e-01 6.76417053e-01 5.85954785e-01 -8.11604440e-01 3.60575542e-02 -5.83443999e-01 -2.25264709e-02 -5.34746647e-01 4.11512971e-01 4.85233873e-01 -2.37288520e-01 -3.36894929e-01 8.73676777e-01 9.40887570e-01 -8.00745070e-01 2.15635851e-01 8.14827263e-01 2.78791189e-01 2.91396677e-01 3.13180894e-01 -9.86121178e-01 8.35433125e-01 -7.17261672e-01 -4.80435580e-01 1.49705306e-01 -2.99241602e-01 -8.61510813e-01 -1.12946823e-01 -1.73074678e-01 2.84872979e-01 3.78208131e-01 9.49629247e-01 5.13771355e-01 2.40689263e-01 3.53599131e-01 -8.94456148e-01 -8.44263911e-01 6.68486595e-01 -5.27783990e-01 2.14359313e-01 7.38033950e-01 -8.44728947e-02 8.33402455e-01 -9.80159342e-01 7.41620779e-01 1.32276690e+00 5.38752019e-01 4.89935845e-01 -3.41765761e-01 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e5e2a2ac-c719-4c62-9a8c-8f70ef0cd4ff	feature-augmented-hybrid-cnn-for-stress	2108.03166	null	https://arxiv.org/abs/2108.03166v1	https://arxiv.org/pdf/2108.03166v1.pdf	Feature Augmented Hybrid CNN for Stress Recognition Using Wrist-based Photoplethysmography Sensor	Stress is a physiological state that hampers mental health and has serious consequences to physical health. Moreover, the COVID-19 pandemic has increased stress levels among people across the globe. Therefore, continuous monitoring and detection of stress are necessary. The recent advances in wearable devices have allowed the monitoring of several physiological signals related to stress. Among them, wrist-worn wearable devices like smartwatches are most popular due to their convenient usage. And the photoplethysmography (PPG) sensor is the most prevalent sensor in almost all consumer-grade wrist-worn smartwatches. Therefore, this paper focuses on using a wrist-based PPG sensor that collects Blood Volume Pulse (BVP) signals to detect stress which may be applicable for consumer-grade wristwatches. Moreover, state-of-the-art works have used either classical machine learning algorithms to detect stress using hand-crafted features or have used deep learning algorithms like Convolutional Neural Network (CNN) which automatically extracts features. This paper proposes a novel hybrid CNN (H-CNN) classifier that uses both the hand-crafted features and the automatically extracted features by CNN to detect stress using the BVP signal. Evaluation on the benchmark WESAD dataset shows that, for 3-class classification (Baseline vs. Stress vs. Amusement), our proposed H-CNN outperforms traditional classifiers and normal CNN by 5% and 7% accuracy, and 10% and 7% macro F1 score, respectively. Also for 2-class classification (Stress vs. Non-stress), our proposed H-CNN outperforms traditional classifiers and normal CNN by 3% and ~5% accuracy, and ~3% and ~7% macro F1 score, respectively.	['Mohammad Abdullah Al Faruque', 'Peter Tseng', 'Abel Jimenez', 'Manik Dautta', 'Luke Chen', 'Nafiul Rashid']	2021-08-02	null	null	null	null	['photoplethysmography-ppg']	['medical']	[-6.74192607e-02 -2.29789868e-01 -9.94283035e-02 -3.65639478e-01 -6.83268607e-02 -9.16817635e-02 -6.75843284e-02 4.73747998e-01 -6.96980417e-01 8.47071588e-01 8.95048752e-02 -1.78674698e-01 1.33674487e-01 -7.20551968e-01 -1.27305269e-01 -7.70016611e-01 -2.27419078e-01 -1.95203170e-01 -1.85182989e-01 -1.84763283e-01 -3.33333276e-02 4.15110111e-01 -1.44336212e+00 -9.41535309e-02 8.22350144e-01 1.61464274e+00 -4.19533998e-01 4.14338022e-01 1.66373149e-01 1.18635837e-02 -7.90590465e-01 -7.51619861e-02 1.46262988e-01 -6.20010018e-01 -1.33381933e-01 -2.75824666e-01 1.51653126e-01 -1.23410508e-01 6.87507689e-02 7.66834676e-01 1.13524854e+00 8.69654119e-02 1.83217436e-01 -1.23490608e+00 -3.08024168e-01 1.73516184e-01 -3.76835227e-01 5.88376045e-01 5.00824809e-01 2.23939762e-01 1.14833333e-01 -5.92443824e-01 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6.35482550e-01 -8.72397661e-01 -1.75118834e-01 -1.45972359e+00 -8.71677816e-01 3.29715461e-01 3.09712619e-01 -7.31546938e-01 -2.79708445e-01 -1.95239291e-01]	[13.684279441833496, 3.0836682319641113]
ef1482a9-f5be-455d-a922-4b47acb56a05	reliable-and-interpretable-personalized	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Qin_Reliable_and_Interpretable_Personalized_Federated_Learning_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Qin_Reliable_and_Interpretable_Personalized_Federated_Learning_CVPR_2023_paper.pdf	Reliable and Interpretable Personalized Federated Learning	Federated learning can coordinate multiple users to participate in data training while ensuring data privacy. The collaboration of multiple agents allows for a natural connection between federated learning and collective intelligence. When there are large differences in data distribution among clients, it is crucial for federated learning to design a reliable client selection strategy and an interpretable client communication framework to better utilize group knowledge. Herein, a reliable personalized federated learning approach, termed RIPFL, is proposed and fully interpreted from the perspective of social learning. RIPFL reliably selects and divides the clients involved in training such that each client can use different amounts of social information and more effectively communicate with other clients. Simultaneously, the method effectively integrates personal information with the social information generated by the global model from the perspective of Bayesian decision rules and evidence theory, enabling individuals to grow better with the help of collective wisdom. An interpretable federated learning mind is well scalable, and the experimental results indicate that the proposed method has superior robustness and accuracy than other state-of-the-art federated learning algorithms.	['QinGhua Hu', 'Yahong Han', 'Qilong Wang', 'Liu Yang', 'Zixuan Qin']	2023-01-01	null	null	null	cvpr-2023-1	['personalized-federated-learning']	['methodology']	[-7.34845996e-01 2.23246679e-01 -4.88352269e-01 -6.11852229e-01 -4.45475310e-01 -4.50412273e-01 3.51441592e-01 4.03179564e-02 -1.34418994e-01 7.00165212e-01 3.66307199e-01 1.85649842e-01 -6.35735214e-01 -9.69996929e-01 -2.53522754e-01 -9.92522418e-01 -6.49947748e-02 6.15070879e-01 -2.37564057e-01 2.84741580e-01 -6.10635020e-02 2.13086739e-01 -1.51450539e+00 3.16304028e-01 8.84821117e-01 1.08398473e+00 -2.65836805e-01 2.20757961e-01 -4.71643001e-01 1.24416459e+00 -4.91086841e-01 -8.53724599e-01 4.59354758e-01 -2.73033530e-01 -6.86147630e-01 7.89358616e-02 2.79024571e-01 -5.48299134e-01 -2.79160738e-01 1.05678308e+00 4.78665739e-01 1.79338396e-01 3.61145616e-01 -1.42248213e+00 -7.97882438e-01 1.17316115e+00 1.19474083e-02 -4.68673050e-01 1.31087124e-01 2.54072577e-01 1.06918919e+00 -2.58451343e-01 4.84145314e-01 1.31536269e+00 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6fcf354c-e286-4813-a21e-32d19f9b2085	joint-radar-communication-waveform-design	2006.16096	null	https://arxiv.org/abs/2006.16096v2	https://arxiv.org/pdf/2006.16096v2.pdf	Joint Radar-Communication Waveform Design Based on Composite Modulation	Joint radar-communication (JRC) waveform can be used for simultaneous radar detection and communication in the same frequency band. However, radar detection processing requires the prior knowledge of the waveform including the embedded information for matched filtering. To remove this requirement, we propose a unimodular JRC waveform based on composite modulation where the internal modulation embeds information by mapping the bit sequence to different orthogonal signals, and the external modulation performs phase modulation on the internal waveform to satisfy the demand of detection. By adjusting the number of the orthogonal signals, a trade-off between the detection and the communication performance can be made. Besides, a new parameter, dissimilarity, is defined to evaluate the detection performance robustness to unknown embedded information. The numerical results show that the SER performance of the proposed system is similar to that of the multilevel frequency shift keying system, the ambiguity function resembles that of the phase coded signal, and the dissimilarity performance is better than other JRC systems.	['Jiazhi Ma', 'Longfei Shi', 'Fulai Wang', 'Yuan Quan']	2020-06-29	null	null	null	null	['joint-radar-communication']	['robots']	[ 6.61118388e-01 -4.80891794e-01 -8.20690393e-02 -1.07280217e-01 -3.84693265e-01 -4.37587261e-01 3.78531814e-01 -3.24468702e-01 -3.44131619e-01 6.70158565e-01 9.29176584e-02 -3.20643455e-01 -4.77034569e-01 -8.18723142e-01 1.05146438e-01 -1.06980896e+00 -2.51798034e-01 -2.50026435e-01 1.78200915e-01 -2.31303409e-01 4.29997832e-01 5.12111545e-01 -1.25009143e+00 -2.63639957e-01 9.45845783e-01 1.26839578e+00 2.33960703e-01 5.86192369e-01 6.43532351e-02 -9.56825446e-03 -1.18831217e+00 -6.70411391e-03 4.24561352e-01 -6.89036191e-01 6.70947731e-01 -2.21665308e-01 -1.24990791e-01 5.43577969e-03 -6.10308468e-01 1.45630598e+00 6.43969834e-01 -4.62100655e-01 9.93067086e-01 -1.16695368e+00 -4.74267244e-01 3.94839317e-01 -7.51805544e-01 1.57801971e-01 3.22651714e-02 -5.77109344e-02 4.22721237e-01 -3.35446030e-01 7.85986483e-02 1.15060198e+00 2.11560011e-01 7.07742348e-02 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3ecb5408-9934-455c-8493-b1ee4ec524cc	identification-of-causal-relationship-between	2307.01389	null	https://arxiv.org/abs/2307.01389v1	https://arxiv.org/pdf/2307.01389v1.pdf	Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference	Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition. The accumulation of amyloid-beta in the brain, measured through 18F-florbetapir (AV45) positron emission tomography (PET) imaging, has been widely used for early diagnosis of AD. However, the relationship between amyloid-beta accumulation and AD pathophysiology remains unclear, and causal inference approaches are needed to uncover how amyloid-beta levels can impact AD development. In this paper, we propose a graph varying coefficient neural network (GVCNet) for estimating the individual treatment effect with continuous treatment levels using a graph convolutional neural network. We highlight the potential of causal inference approaches, including GVCNet, for measuring the regional causal connections between amyloid-beta accumulation and AD pathophysiology, which may serve as a robust tool for early diagnosis and tailored care.	['Xiang Li', 'Tianming Liu', 'Sheng Li', 'Quanzheng Li', 'Manhua Liu', 'Xingyu Gao', 'Fan Zhang', 'Jorge Sepulcre', 'Ibai Diez', 'Dajiang Zhu', 'Lin Zhao', 'Lu Zhang', 'Qing Li', 'Mengxuan Hu', 'Haixing Dai']	2023-07-03	null	null	null	null	['causal-inference', 'counterfactual-inference', 'causal-inference']	['knowledge-base', 'miscellaneous', 'miscellaneous']	[ 1.75860941e-01 -3.60858887e-01 -2.55253106e-01 -4.53997880e-01 -2.96028495e-01 -1.84624299e-01 2.57005960e-01 2.28078857e-01 -4.30049151e-01 1.32169139e+00 4.00279969e-01 -3.34900677e-01 -8.59143138e-02 -9.06559944e-01 -6.27420902e-01 -5.76375067e-01 -6.78445160e-01 8.06261957e-01 1.50424898e-01 1.09913692e-01 -4.05894369e-01 4.42573041e-01 -5.15207708e-01 3.75834972e-01 9.89636838e-01 7.55523622e-01 4.12740082e-01 3.99995238e-01 8.42072293e-02 4.56367284e-01 -4.41442013e-01 -1.05978481e-01 -2.49048322e-01 -4.79043782e-01 -5.06676197e-01 -2.02622831e-01 5.08967899e-02 -7.20257998e-01 -3.04415971e-01 1.01633477e+00 3.71643275e-01 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f9fdc818-098c-4767-8a36-1402cb87d88b	generative-tertiary-structure-based-rna	2301.10774	null	https://arxiv.org/abs/2301.10774v2	https://arxiv.org/pdf/2301.10774v2.pdf	Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design	While artificial intelligence has made remarkable strides in revealing the relationship between biological macromolecules' primary sequence and tertiary structure, designing RNA sequences based on specified tertiary structures remains challenging. Though existing approaches in protein design have thoroughly explored structure-to-sequence dependencies in proteins, RNA design still confronts difficulties due to structural complexity and data scarcity. Adding to the problem, direct transplantation of protein design methodologies into RNA design fails to achieve satisfactory outcomes although sharing similar structural components. In this study, we aim to systematically construct a data-driven RNA design pipeline. We crafted a large, well-curated benchmark dataset and designed a comprehensive structural modeling approach to represent the complex RNA tertiary structure. More importantly, we proposed a hierarchical data-efficient representation learning framework that learns structural representations through contrastive learning at both cluster-level and sample-level to fully leverage the limited data. By constraining data representations within a limited hyperspherical space, the intrinsic relationships between data points could be explicitly imposed. Moreover, we incorporated extracted secondary structures with base pairs as prior knowledge to facilitate the RNA design process. Extensive experiments demonstrate the effectiveness of our proposed method, providing a reliable baseline for future RNA design tasks. The source code and benchmark dataset will be released publicly.	['Hanqun Cao', 'Zhangyang Gao', 'Yijie Zhang', 'Stan Z. Li', 'Cheng Tan']	2023-01-25	null	null	null	null	['protein-design']	['medical']	[ 4.09474701e-01 -8.22007209e-02 -2.08429605e-01 -5.33252478e-01 -7.42574990e-01 -8.54256630e-01 5.04719794e-01 1.42865241e-01 -3.64440233e-02 9.80476499e-01 5.26839018e-01 -6.05201423e-01 -1.57539830e-01 -5.65347195e-01 -8.03310990e-01 -1.05388868e+00 -2.84889787e-02 4.78901744e-01 -3.70504230e-01 -1.06294572e-01 5.47460854e-01 8.75572562e-01 -1.46082795e+00 4.25998360e-01 1.15073550e+00 5.61167955e-01 5.10620296e-01 3.06258529e-01 -2.82245100e-01 6.13732159e-01 -2.47877166e-01 1.58952489e-01 1.77164540e-01 -6.75979912e-01 -8.70866537e-01 1.41167268e-01 -1.38155803e-01 2.00046986e-01 -1.16082422e-01 4.84952629e-01 6.71223581e-01 2.84323618e-02 8.23257864e-01 -6.69547796e-01 -1.04847467e+00 3.24621409e-01 -4.46082860e-01 -7.74812177e-02 3.22394699e-01 4.70838338e-01 1.42783415e+00 -1.05824065e+00 8.30106318e-01 1.01571155e+00 3.18097770e-01 5.77896059e-01 -1.47617519e+00 -3.25824529e-01 1.87052667e-01 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cbea4c4c-7c0e-4752-8529-8b111ac50592	detect-faces-efficiently-a-survey-and	2112.01787	null	https://arxiv.org/abs/2112.01787v1	https://arxiv.org/pdf/2112.01787v1.pdf	Detect Faces Efficiently: A Survey and Evaluations	Face detection is to search all the possible regions for faces in images and locate the faces if there are any. Many applications including face recognition, facial expression recognition, face tracking and head-pose estimation assume that both the location and the size of faces are known in the image. In recent decades, researchers have created many typical and efficient face detectors from the Viola-Jones face detector to current CNN-based ones. However, with the tremendous increase in images and videos with variations in face scale, appearance, expression, occlusion and pose, traditional face detectors are challenged to detect various "in the wild" faces. The emergence of deep learning techniques brought remarkable breakthroughs to face detection along with the price of a considerable increase in computation. This paper introduces representative deep learning-based methods and presents a deep and thorough analysis in terms of accuracy and efficiency. We further compare and discuss the popular and challenging datasets and their evaluation metrics. A comprehensive comparison of several successful deep learning-based face detectors is conducted to uncover their efficiency using two metrics: FLOPs and latency. The paper can guide to choose appropriate face detectors for different applications and also to develop more efficient and accurate detectors.	['JianGuo Zhang', 'Yan-ran Li', 'Hanyang Peng', 'Shiqi Yu', 'Yuantao Feng']	2021-12-03	null	null	null	null	['head-pose-estimation']	['computer-vision']	[-4.59260076e-01 -6.73837245e-01 6.00141147e-03 -6.21711969e-01 -1.98991492e-01 -5.27948081e-01 2.67609358e-01 -2.65681446e-01 -4.06779647e-01 3.29015881e-01 -3.16710025e-01 2.25150019e-01 7.87222907e-02 -4.19696033e-01 -4.13085282e-01 -7.02785552e-01 -2.99579889e-01 4.20141548e-01 2.55705733e-02 -4.28120568e-02 8.96572173e-02 1.30298233e+00 -1.87909043e+00 -8.68647650e-04 -6.68305010e-02 1.21664369e+00 -1.71263173e-01 6.39719069e-01 3.01630069e-02 4.63466614e-01 -7.25781381e-01 -5.55491626e-01 3.19381803e-01 1.12767832e-03 -4.53582764e-01 1.32494017e-01 6.63916469e-01 -5.62755108e-01 -5.35711646e-01 1.14319468e+00 9.49787915e-01 -1.07083827e-01 3.39213282e-01 -1.59346581e+00 -4.94580209e-01 -1.85583144e-01 -1.12297952e+00 6.10482752e-01 5.68548441e-01 -7.60999322e-02 2.66169339e-01 -1.42637253e+00 4.79683846e-01 1.69248915e+00 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71800755-6bca-436e-afdd-1c75763eec7e	self-supervised-and-supervised-joint-training-1	2106.0406	null	https://arxiv.org/abs/2106.04060v1	https://arxiv.org/pdf/2106.04060v1.pdf	Self-supervised and Supervised Joint Training for Resource-rich Machine Translation	Self-supervised pre-training of text representations has been successfully applied to low-resource Neural Machine Translation (NMT). However, it usually fails to achieve notable gains on resource-rich NMT. In this paper, we propose a joint training approach, $F_2$-XEnDec, to combine self-supervised and supervised learning to optimize NMT models. To exploit complementary self-supervised signals for supervised learning, NMT models are trained on examples that are interbred from monolingual and parallel sentences through a new process called crossover encoder-decoder. Experiments on two resource-rich translation benchmarks, WMT'14 English-German and WMT'14 English-French, demonstrate that our approach achieves substantial improvements over several strong baseline methods and obtains a new state of the art of 46.19 BLEU on English-French when incorporating back translation. Results also show that our approach is capable of improving model robustness to input perturbations such as code-switching noise which frequently appears on social media.	['Wolfgang Macherey', 'Lu Jiang', 'Wei Wang', 'Yong Cheng']	2021-06-08	self-supervised-and-supervised-joint-training	https://openreview.net/forum?id=1yDrpckYHnN	https://openreview.net/pdf?id=1yDrpckYHnN	null	['low-resource-neural-machine-translation']	['natural-language-processing']	[ 6.27221107e-01 3.94930281e-02 -5.78674257e-01 -4.11105603e-01 -1.63129199e+00 -3.73257875e-01 7.32330799e-01 -2.95530111e-01 -4.32300955e-01 1.09723687e+00 3.46069217e-01 -7.34561801e-01 5.51972210e-01 -1.49734840e-01 -1.36860514e+00 -3.04549545e-01 2.86240608e-01 8.49843264e-01 -5.90260744e-01 -4.92546260e-01 -1.34302199e-01 -2.34751627e-01 -9.02476966e-01 5.73638856e-01 1.13219392e+00 2.64363438e-01 2.14197397e-01 5.13217270e-01 -4.39319499e-02 5.66326141e-01 -6.71210706e-01 -6.24970734e-01 3.41370463e-01 -7.71106839e-01 -8.24887931e-01 -2.54927993e-01 5.67825317e-01 5.37660122e-02 -2.79276848e-01 9.41852987e-01 8.36951554e-01 -7.14157298e-02 8.58263791e-01 -5.50847828e-01 -1.19928074e+00 1.36660385e+00 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733a4892-d820-47bb-84eb-8426736b87e4	multilingual-dependency-parsing-for-low	null	null	https://aclanthology.org/L18-1352	https://aclanthology.org/L18-1352.pdf	Multilingual Dependency Parsing for Low-Resource Languages: Case Studies on North Saami and Komi-Zyrian	null	['KyungTae Lim', 'Niko Partanen', 'Thierry Poibeau']	2018-05-01	multilingual-dependency-parsing-for-low-1	https://aclanthology.org/L18-1352	https://aclanthology.org/L18-1352.pdf	lrec-2018-5	['multilingual-word-embeddings']	['methodology']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 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90aa2168-dea8-4980-897d-37bfe52f356e	grill-grounded-vision-language-pre-training	2305.14676	null	https://arxiv.org/abs/2305.14676v1	https://arxiv.org/pdf/2305.14676v1.pdf	GRILL: Grounded Vision-language Pre-training via Aligning Text and Image Regions	Generalization to unseen tasks is an important ability for few-shot learners to achieve better zero-/few-shot performance on diverse tasks. However, such generalization to vision-language tasks including grounding and generation tasks has been under-explored; existing few-shot VL models struggle to handle tasks that involve object grounding and multiple images such as visual commonsense reasoning or NLVR2. In this paper, we introduce GRILL, GRounded vIsion Language aLigning, a novel VL model that can be generalized to diverse tasks including visual question answering, captioning, and grounding tasks with no or very few training instances. Specifically, GRILL learns object grounding and localization by exploiting object-text alignments, which enables it to transfer to grounding tasks in a zero-/few-shot fashion. We evaluate our model on various zero-/few-shot VL tasks and show that it consistently surpasses the state-of-the-art few-shot methods.	['Xiang Ren', 'Damien Jose', 'Ahmed Hassan Awadallah', 'Weizhu Chen', 'Yelong Shen', 'Yu Cheng', 'Subhabrata Mukherjee', 'Woojeong Jin']	2023-05-24	null	null	null	null	['visual-commonsense-reasoning']	['reasoning']	[ 2.32767493e-01 3.50933731e-01 -1.85678035e-01 -2.65278757e-01 -1.01343966e+00 -3.97139192e-01 9.13899183e-01 2.02002451e-01 -2.06255466e-01 5.81172407e-01 1.04522996e-01 -4.00371164e-01 1.89082444e-01 -7.36377001e-01 -1.08082116e+00 -1.87414378e-01 3.16800117e-01 6.81483388e-01 6.65518224e-01 -5.75025797e-01 7.54615217e-02 -1.16003849e-01 -1.75871325e+00 8.03528786e-01 6.16922140e-01 6.32280111e-01 3.78747731e-01 7.65277445e-01 -6.81172252e-01 1.30141318e+00 -5.63039362e-01 -6.13423347e-01 1.71065365e-03 -6.93512559e-01 -9.79300857e-01 6.79502860e-02 1.25226665e+00 -4.65699792e-01 -2.74391174e-01 1.04451287e+00 3.66975754e-01 5.41214585e-01 7.71016419e-01 -1.74431264e+00 -1.58956647e+00 3.25449824e-01 -3.96629810e-01 4.73461330e-01 4.99209315e-01 6.12220705e-01 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5bf74334-0af4-49e2-bf0c-cbd0892dfd2d	2305-14656	2305.14656	null	https://arxiv.org/abs/2305.14656v1	https://arxiv.org/pdf/2305.14656v1.pdf	RSRM: Reinforcement Symbolic Regression Machine	In nature, the behaviors of many complex systems can be described by parsimonious math equations. Automatically distilling these equations from limited data is cast as a symbolic regression process which hitherto remains a grand challenge. Keen efforts in recent years have been placed on tackling this issue and demonstrated success in symbolic regression. However, there still exist bottlenecks that current methods struggle to break when the discrete search space tends toward infinity and especially when the underlying math formula is intricate. To this end, we propose a novel Reinforcement Symbolic Regression Machine (RSRM) that masters the capability of uncovering complex math equations from only scarce data. The RSRM model is composed of three key modules: (1) a Monte Carlo tree search (MCTS) agent that explores optimal math expression trees consisting of pre-defined math operators and variables, (2) a Double Q-learning block that helps reduce the feasible search space of MCTS via properly understanding the distribution of reward, and (3) a modulated sub-tree discovery block that heuristically learns and defines new math operators to improve representation ability of math expression trees. Biding of these modules yields the state-of-the-art performance of RSRM in symbolic regression as demonstrated by multiple sets of benchmark examples. The RSRM model shows clear superiority over several representative baseline models.	['Hao Sun', 'Yang Liu', 'Yilong Xu']	2023-05-24	null	null	null	null	['q-learning']	['methodology']	[ 2.77988076e-01 1.31867141e-01 -3.69873106e-01 -1.81872457e-01 -9.29479301e-01 -4.57914501e-01 3.50548834e-01 -1.19412929e-01 -1.74193494e-02 8.72291863e-01 -4.61122602e-01 -7.24660516e-01 -3.79565984e-01 -7.31959999e-01 -7.73876309e-01 -3.95540476e-01 -1.92205772e-01 8.59593451e-01 1.79277241e-01 -6.03174686e-01 3.85148317e-01 4.62899655e-01 -1.46697676e+00 -6.19711317e-02 1.44582033e+00 9.39297676e-01 1.14654988e-01 6.06460571e-01 -8.03203359e-02 1.26383257e+00 -4.50932443e-01 -2.36248836e-01 2.16097206e-01 -5.67054629e-01 -8.81581843e-01 -5.06429553e-01 -3.14942412e-02 -2.71876842e-01 -2.84749687e-01 9.00869966e-01 3.57920006e-02 3.01897138e-01 5.32208979e-01 -1.33284938e+00 -3.25802743e-01 9.93894696e-01 -5.50025880e-01 1.36888355e-01 2.00860277e-01 5.34468889e-01 1.24180651e+00 -2.44093999e-01 3.80393356e-01 1.29634500e+00 5.09217799e-01 4.57184553e-01 -1.76071978e+00 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-2.08047166e-01 2.83960730e-01 -9.12085772e-01 -2.23195940e-01 -3.42290938e-01]	[8.500164031982422, 6.880970478057861]
3aea8da3-674c-460a-ba0f-aef274f71731	punctuation-prediction-in-spontaneous	2004.05985	null	https://arxiv.org/abs/2004.05985v1	https://arxiv.org/pdf/2004.05985v1.pdf	Punctuation Prediction in Spontaneous Conversations: Can We Mitigate ASR Errors with Retrofitted Word Embeddings?	Automatic Speech Recognition (ASR) systems introduce word errors, which often confuse punctuation prediction models, turning punctuation restoration into a challenging task. These errors usually take the form of homonyms. We show how retrofitting of the word embeddings on the domain-specific data can mitigate ASR errors. Our main contribution is a method for better alignment of homonym embeddings and the validation of the presented method on the punctuation prediction task. We record the absolute improvement in punctuation prediction accuracy between 6.2% (for question marks) to 9% (for periods) when compared with the state-of-the-art model.	['Piotr .Zelasko', 'Mikołaj Morzy', 'Łukasz Augustyniak', 'Yishay Carmiel', 'Jan Mizgajski', 'Adrian Szymczak', 'Piotr Szymanski', 'Najim Dehak']	2020-04-13	null	null	null	null	['punctuation-restoration']	['natural-language-processing']	[ 2.81205505e-01 3.90420943e-01 1.25665769e-01 -2.98242629e-01 -8.10636044e-01 -4.84154642e-01 5.27142823e-01 5.42186737e-01 -8.42850864e-01 5.57839572e-01 5.52151918e-01 -6.98432207e-01 4.49419282e-02 -3.24913502e-01 -5.08879244e-01 -2.94739157e-01 1.88605189e-01 4.22900975e-01 2.25779846e-01 -4.53281194e-01 1.08331755e-01 5.27075469e-01 -1.49652922e+00 2.41395235e-01 8.59048903e-01 7.19244659e-01 3.17429364e-01 6.00480378e-01 -7.20277488e-01 5.59298098e-01 -1.15643871e+00 -6.90158546e-01 -3.18096094e-02 1.80920318e-01 -8.23638499e-01 1.77837774e-01 5.13770819e-01 4.64314014e-01 -6.60949469e-01 1.09224749e+00 4.83302206e-01 2.74120718e-01 4.74521160e-01 -7.52775609e-01 -1.06989682e+00 9.75188375e-01 4.75619763e-01 2.19460547e-01 3.01902533e-01 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c96ebd61-18be-4a9f-ac37-b462dc0320fe	u-cam-visual-explanation-using-uncertainty	1908.06306	null	https://arxiv.org/abs/1908.06306v4	https://arxiv.org/pdf/1908.06306v4.pdf	U-CAM: Visual Explanation using Uncertainty based Class Activation Maps	Understanding and explaining deep learning models is an imperative task. Towards this, we propose a method that obtains gradient-based certainty estimates that also provide visual attention maps. Particularly, we solve for visual question answering task. We incorporate modern probabilistic deep learning methods that we further improve by using the gradients for these estimates. These have two-fold benefits: a) improvement in obtaining the certainty estimates that correlate better with misclassified samples and b) improved attention maps that provide state-of-the-art results in terms of correlation with human attention regions. The improved attention maps result in consistent improvement for various methods for visual question answering. Therefore, the proposed technique can be thought of as a recipe for obtaining improved certainty estimates and explanation for deep learning models. We provide detailed empirical analysis for the visual question answering task on all standard benchmarks and comparison with state of the art methods.	['Vinay P. Namboodiri', 'Shivansh Patel', 'Mayank Lunayach', 'Badri N. Patro']	2019-08-17	u-cam-visual-explanation-using-uncertainty-1	http://openaccess.thecvf.com/content_ICCV_2019/html/Patro_U-CAM_Visual_Explanation_Using_Uncertainty_Based_Class_Activation_Maps_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Patro_U-CAM_Visual_Explanation_Using_Uncertainty_Based_Class_Activation_Maps_ICCV_2019_paper.pdf	iccv-2019-10	['probabilistic-deep-learning']	['computer-vision']	[-2.94998318e-01 4.29750621e-01 -1.78235173e-01 -5.81536293e-01 -1.10699594e+00 -5.52250862e-01 7.79523075e-01 2.47661173e-01 -1.92096934e-01 6.43584967e-01 2.41047576e-01 -4.66763526e-01 -1.07385732e-01 -6.95731699e-01 -1.01253760e+00 -3.38428527e-01 1.92376867e-01 5.27712345e-01 2.53894508e-01 1.54309928e-01 5.51319122e-01 2.15168387e-01 -1.54551756e+00 7.11693525e-01 9.15001214e-01 1.29328144e+00 8.76717269e-02 8.39280307e-01 -6.46733940e-01 1.30791438e+00 -5.70467710e-01 -8.81262362e-01 -2.76988745e-01 -2.69944251e-01 -1.08299422e+00 -2.63119578e-01 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5d3368ed-7714-41dc-a3de-69811511a530	quantifying-the-controllability-of-coarsely	null	null	https://openreview.net/forum?id=okmZ6-zU6Lz	https://openreview.net/pdf?id=okmZ6-zU6Lz	Quantifying the Controllability of Coarsely Characterized Networked Dynamical Systems	We study the controllability of large-scale networked dynamical systems when complete knowledge of network structure is unavailable. In particular, we establish the power of learning community-based representations to understand the ability of a group of control nodes to steer the network to a target state. We are motivated by abundant real-world examples, ranging from power and water systems to brain networks, in which practitioners do not have access to fine-scale knowledge of the network. Rather, knowledge is limited to coarse summaries of network structure. Existing work on "model order reduction" starts with full knowledge of fine-scale structure and derives a coarse-scale (lower-dimensional) model that well-approximates the fine-scale system. In contrast, in this paper the controllability aspects of the coarse system are derived from coarse summaries {\em without} knowledge of the fine-scale structure. We study under what conditions measures of controllability for the (unobserved) fine-scale system can be well approximated by measures of controllability derived from the (observed) coarse-scale system. To accomplish this, we require knowledge of some inherent parametric structure of the fine-scale system that makes this type of inverse problem feasible. To this end, we assume that the underlying fine-scale network is generated by the stochastic block model (SBM) often studied in community detection. We quantify controllability using the ``average controllability'' metric and bound the difference between the controllability of the fine-scale system and that of the coarse-scale system. Our analysis indicates the necessity of underlying structure to make possible the learning of community-based representations, and to be able to quantify accurately the controllability of coarsely characterized networked dynamical systems.	['Stark Draper', 'Gautam Dasarathy', 'Rajasekhar Anguluri', 'Nafiseh Ghoroghchian']	2021-09-29	null	null	null	null	['stochastic-block-model']	['graphs']	[ 2.53320038e-01 3.90498668e-01 1.19352527e-01 4.05595243e-01 1.93048432e-01 -9.48381305e-01 7.24131405e-01 1.85852423e-01 1.05485119e-01 9.42457974e-01 9.12237242e-02 -1.53927565e-01 -6.41753912e-01 -9.53848004e-01 -6.90957546e-01 -9.49186623e-01 -5.87540984e-01 5.68085611e-01 3.03423516e-02 -5.95937848e-01 -1.34444758e-01 6.87612474e-01 -9.27091300e-01 -1.87990859e-01 7.55785108e-01 3.95516604e-01 1.75906554e-01 9.41781878e-01 4.49163467e-01 6.81396186e-01 -4.13506538e-01 6.51454210e-01 2.96883494e-01 -5.44105470e-01 -7.71223307e-01 2.26624072e-01 8.01601782e-02 2.50269413e-01 -6.17399037e-01 1.25211453e+00 9.28414613e-02 -7.08568096e-02 9.98714387e-01 -1.37668216e+00 -5.26790559e-01 6.36318624e-01 -2.98138052e-01 3.32729697e-01 9.02450830e-02 1.29674375e-01 1.04244995e+00 -1.73070312e-01 8.30959260e-01 1.25961041e+00 7.66071558e-01 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2074f0be-0954-4d55-9486-4d8f104f6057	a-positive-feedback-method-based-on-f-measure	2304.14619	null	https://arxiv.org/abs/2304.14619v1	https://arxiv.org/pdf/2304.14619v1.pdf	A positive feedback method based on F-measure value for Salient Object Detection	The majority of current salient object detection (SOD) models are focused on designing a series of decoders based on fully convolutional networks (FCNs) or Transformer architectures and integrating them in a skillful manner. These models have achieved remarkable high performance and made significant contributions to the development of SOD. Their primary research objective is to develop novel algorithms that can outperform state-of-the-art models, a task that is extremely difficult and time-consuming. In contrast, this paper proposes a positive feedback method based on F-measure value for SOD, aiming to improve the accuracy of saliency prediction using existing methods. Specifically, our proposed method takes an image to be detected and inputs it into several existing models to obtain their respective prediction maps. These prediction maps are then fed into our positive feedback method to generate the final prediction result, without the need for careful decoder design or model training. Moreover, our method is adaptive and can be implemented based on existing models without any restrictions. Experimental results on five publicly available datasets show that our proposed positive feedback method outperforms the latest 12 methods in five evaluation metrics for saliency map prediction. Additionally, we conducted a robustness experiment, which shows that when at least one good prediction result exists in the selected existing model, our proposed approach can ensure that the prediction result is not worse. Our approach achieves a prediction speed of 20 frames per second (FPS) when evaluated on a low configuration host and after removing the prediction time overhead of inserted models. These results highlight the effectiveness, efficiency, and robustness of our proposed approach for salient object detection.	['Yunchao Xu', 'Dongping Zhang', 'Chen Pan', 'Chao Dai', 'Ailing Pan']	2023-04-28	null	null	null	null	['saliency-prediction', 'salient-object-detection-1']	['computer-vision', 'computer-vision']	[ 4.40337449e-01 1.26765087e-01 -1.17413029e-01 -1.29086122e-01 -3.94436419e-01 2.16550697e-02 3.92633051e-01 2.42096279e-02 -3.73721123e-01 5.19326150e-01 1.16337016e-01 -1.54754534e-01 1.30122259e-01 -7.31179476e-01 -8.71445596e-01 -3.77511531e-01 1.12282977e-01 -7.40616173e-02 1.21269834e+00 -3.44652236e-01 6.26068294e-01 3.27274948e-01 -1.84872413e+00 6.22880645e-02 1.08480716e+00 1.14517522e+00 1.03239536e+00 5.23455322e-01 2.06608191e-01 9.48626101e-01 -3.27900827e-01 -3.46202791e-01 2.60037839e-01 -2.56183505e-01 -6.32421434e-01 -2.24972263e-01 1.62505463e-01 -4.94316936e-01 -1.02722414e-01 1.02569711e+00 6.13866806e-01 -1.55680493e-01 3.02613199e-01 -1.24122381e+00 -6.50232971e-01 5.91312945e-01 -6.13446474e-01 4.35380995e-01 6.93063289e-02 9.55148265e-02 7.77856290e-01 -1.02141941e+00 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72b7feca-cffa-44a9-a266-6de248b8e916	one-class-classification-robust-to-geometric	null	null	https://openreview.net/forum?id=oY7La6DBTLx	https://openreview.net/pdf?id=oY7La6DBTLx	One-class Classification Robust to Geometric Transformation	Recent studies on one-class classification have achieved a remarkable performance, by employing the self-supervised classifier that predicts the geometric transformation applied to in-class images. However, they cannot identify in-class images at all when the input images are geometrically-transformed (e.g., rotated images), because their classification-based in-class scores assume that input images always have a fixed viewpoint, as similar to the images used for training. Pointing out that humans can easily recognize such transformed images as the same class, in this work, we aim to propose a one-class classifier robust to geometrically-transformed inputs, named as GROC. To this end, we introduce a conformity score which indicates how strongly an input image agrees with one of the predefined in-class transformations, then utilize the conformity score with our proposed agreement measures for one-class classification. Our extensive experiments demonstrate that GROC is able to accurately distinguish in-class images from out-of-class images regardless of whether the inputs are geometrically-transformed or not, whereas the existing methods fail.	['Hwanjo Yu', 'SeongKu Kang', 'Dongha Lee', 'Hyunjun Ju']	2021-01-01	null	null	null	null	['one-class-classifier']	['methodology']	[ 4.25899595e-01 4.38292883e-02 -2.72372276e-01 -7.22512364e-01 -3.65951329e-01 -6.47086263e-01 5.99741101e-01 1.19093195e-01 -3.51294279e-01 3.42271656e-01 -4.95743990e-01 -2.39878982e-01 -2.15861991e-01 -8.34205747e-01 -6.53218687e-01 -7.63274074e-01 1.69930249e-01 4.68732417e-01 3.50218177e-01 9.29220617e-02 2.99820989e-01 6.58443868e-01 -1.93670988e+00 4.39274281e-01 8.51561964e-01 1.03870773e+00 -2.77059734e-01 5.52783906e-01 6.17537089e-02 4.45786297e-01 -7.30083227e-01 -3.28426033e-01 4.10457641e-01 -4.87513959e-01 -9.09976244e-01 3.82239550e-01 8.06238711e-01 1.46539658e-01 1.35498598e-01 1.41460109e+00 -1.48293972e-01 -4.74625081e-02 1.21585715e+00 -1.47395599e+00 -4.61462706e-01 1.51168332e-01 -3.55945498e-01 -2.00489476e-01 3.91419411e-01 -1.54029816e-01 7.64418244e-01 -8.77163947e-01 5.53158164e-01 9.77098644e-01 4.05309469e-01 2.92659134e-01 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-7.00237036e-01 -4.46623504e-01 1.00056946e+00 1.02147207e-01 -8.14438224e-01 -7.38610208e-01 1.53838813e-01]	[9.70783805847168, 2.6112921237945557]
5b96c07f-270d-4d49-be94-db4bd119a1ae	illuminati-towards-explaining-graph-neural	2303.14836	null	https://arxiv.org/abs/2303.14836v1	https://arxiv.org/pdf/2303.14836v1.pdf	Illuminati: Towards Explaining Graph Neural Networks for Cybersecurity Analysis	Graph neural networks (GNNs) have been utilized to create multi-layer graph models for a number of cybersecurity applications from fraud detection to software vulnerability analysis. Unfortunately, like traditional neural networks, GNNs also suffer from a lack of transparency, that is, it is challenging to interpret the model predictions. Prior works focused on specific factor explanations for a GNN model. In this work, we have designed and implemented Illuminati, a comprehensive and accurate explanation framework for cybersecurity applications using GNN models. Given a graph and a pre-trained GNN model, Illuminati is able to identify the important nodes, edges, and attributes that are contributing to the prediction while requiring no prior knowledge of GNN models. We evaluate Illuminati in two cybersecurity applications, i.e., code vulnerability detection and smart contract vulnerability detection. The experiments show that Illuminati achieves more accurate explanation results than state-of-the-art methods, specifically, 87.6% of subgraphs identified by Illuminati are able to retain their original prediction, an improvement of 10.3% over others at 77.3%. Furthermore, the explanation of Illuminati can be easily understood by the domain experts, suggesting the significant usefulness for the development of cybersecurity applications.	['H. Howie Huang', 'Yuede Ji', 'Haoyu He']	2023-03-26	null	null	null	null	['fraud-detection', 'vulnerability-detection']	['miscellaneous', 'miscellaneous']	[ 3.95856090e-02 7.79381931e-01 -3.75645429e-01 -4.33942564e-02 1.37012631e-01 -5.64419627e-01 1.30737662e-01 1.87341273e-01 6.20737612e-01 1.12970658e-01 6.05524965e-02 -1.14498723e+00 -3.69530648e-01 -9.11080420e-01 -6.77326024e-01 -3.98689806e-02 -3.07746112e-01 9.89888385e-02 9.03349072e-02 -3.48777324e-01 4.05996889e-01 3.32163602e-01 -7.94574082e-01 2.58318484e-01 1.01418090e+00 7.18884945e-01 -5.11274278e-01 4.58273649e-01 -3.46543342e-02 8.70645165e-01 -5.72542310e-01 -9.76260781e-01 2.70362973e-01 -1.10345982e-01 -6.46583736e-01 -4.74237770e-01 3.83351631e-02 -2.30663598e-01 -5.31276703e-01 1.43321657e+00 -2.78663129e-01 -4.55865890e-01 5.37620544e-01 -1.98566413e+00 -1.01324260e+00 1.23805678e+00 -5.74314237e-01 3.24941315e-02 2.51969755e-01 3.17993239e-02 1.23245943e+00 -5.86947918e-01 6.15780771e-01 1.20032275e+00 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a7a06c47-b8c5-439c-959c-890cc0acd46f	crosspoint-self-supervised-cross-modal	2203.0068	null	https://arxiv.org/abs/2203.00680v3	https://arxiv.org/pdf/2203.00680v3.pdf	CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding	Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object classification, segmentation and detection is often laborious owing to the irregular structure of point clouds. Self-supervised learning, which operates without any human labeling, is a promising approach to address this issue. We observe in the real world that humans are capable of mapping the visual concepts learnt from 2D images to understand the 3D world. Encouraged by this insight, we propose CrossPoint, a simple cross-modal contrastive learning approach to learn transferable 3D point cloud representations. It enables a 3D-2D correspondence of objects by maximizing agreement between point clouds and the corresponding rendered 2D image in the invariant space, while encouraging invariance to transformations in the point cloud modality. Our joint training objective combines the feature correspondences within and across modalities, thus ensembles a rich learning signal from both 3D point cloud and 2D image modalities in a self-supervised fashion. Experimental results show that our approach outperforms the previous unsupervised learning methods on a diverse range of downstream tasks including 3D object classification and segmentation. Further, the ablation studies validate the potency of our approach for a better point cloud understanding. Code and pretrained models are available at http://github.com/MohamedAfham/CrossPoint.	['Ranga Rodrigo', 'Kanchana Thilakarathna', 'Amaya Dharmasiri', 'Dinithi Dissanayake', 'Isuru Dissanayake', 'Mohamed Afham']	2022-03-01	null	http://openaccess.thecvf.com//content/CVPR2022/html/Afham_CrossPoint_Self-Supervised_Cross-Modal_Contrastive_Learning_for_3D_Point_Cloud_Understanding_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Afham_CrossPoint_Self-Supervised_Cross-Modal_Contrastive_Learning_for_3D_Point_Cloud_Understanding_CVPR_2022_paper.pdf	cvpr-2022-1	['3d-object-classification', '3d-point-cloud-linear-classification']	['computer-vision', 'computer-vision']	[-5.35481330e-03 2.09674295e-02 -3.87572765e-01 -4.58380938e-01 -8.66409659e-01 -8.87697637e-01 7.13710904e-01 2.99232334e-01 -8.19406807e-02 -3.80032584e-02 -3.97798270e-01 -7.04575628e-02 4.42302004e-02 -5.90434253e-01 -9.17556465e-01 -4.72608745e-01 -3.36932614e-02 7.87487209e-01 4.81423855e-01 6.05810732e-02 2.34618127e-01 9.23279464e-01 -1.61788952e+00 1.52062222e-01 6.30965889e-01 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eb5885ea-b3cb-47f6-8dcd-136c0ab67c7c	uscore-an-effective-approach-to-fully	2202.10062	null	https://arxiv.org/abs/2202.10062v3	https://arxiv.org/pdf/2202.10062v3.pdf	USCORE: An Effective Approach to Fully Unsupervised Evaluation Metrics for Machine Translation	The vast majority of evaluation metrics for machine translation are supervised, i.e., (i) are trained on human scores, (ii) assume the existence of reference translations, or (iii) leverage parallel data. This hinders their applicability to cases where such supervision signals are not available. In this work, we develop fully unsupervised evaluation metrics. To do so, we leverage similarities and synergies between evaluation metric induction, parallel corpus mining, and MT systems. In particular, we use an unsupervised evaluation metric to mine pseudo-parallel data, which we use to remap deficient underlying vector spaces (in an iterative manner) and to induce an unsupervised MT system, which then provides pseudo-references as an additional component in the metric. Finally, we also induce unsupervised multilingual sentence embeddings from pseudo-parallel data. We show that our fully unsupervised metrics are effective, i.e., they beat supervised competitors on 4 out of our 5 evaluation datasets. We make our code publicly available.	['Steffen Eger', 'Jonas Belouadi']	2022-02-21	null	null	null	null	['parallel-corpus-mining']	['natural-language-processing']	[ 3.58050466e-01 -6.27827644e-02 -4.74068761e-01 -3.73595774e-01 -1.15996099e+00 -1.00093806e+00 8.61371279e-01 2.09186345e-01 -5.70498109e-01 7.54383922e-01 5.06734490e-01 -7.20117807e-01 9.06799734e-02 -4.22483861e-01 -6.02468431e-01 -3.66718262e-01 3.44467163e-01 6.75382137e-01 -1.07280120e-01 -2.42068216e-01 2.54290849e-01 6.52227402e-02 -9.92969513e-01 1.01002693e-01 1.13379228e+00 4.81183469e-01 4.18982394e-02 6.02596283e-01 2.45325286e-02 6.48914099e-01 -2.98380017e-01 -6.87021196e-01 1.97613463e-01 -6.20366991e-01 -9.56417918e-01 9.01762992e-02 1.99500546e-01 -1.69743121e-01 -1.06557377e-01 9.55863595e-01 3.44734579e-01 -8.11232030e-02 9.22185302e-01 -9.28398490e-01 -6.00768030e-01 7.24141538e-01 -3.77627879e-01 1.26782775e-01 2.14828700e-01 1.16572008e-01 1.47719479e+00 -1.27808166e+00 7.63172328e-01 8.67739081e-01 4.72376972e-01 3.25988859e-01 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dd0684da-8322-4275-9269-20dd8341b545	sedroid-a-robust-android-malware-detector	1909.03837	null	https://arxiv.org/abs/1909.03837v1	https://arxiv.org/pdf/1909.03837v1.pdf	SEdroid: A Robust Android Malware Detector using Selective Ensemble Learning	For the dramatic increase of Android malware and low efficiency of manual check process, deep learning methods started to be an auxiliary means for Android malware detection these years. However, these models are highly dependent on the quality of datasets, and perform unsatisfactory results when the quality of training data is not good enough. In the real world, the quality of datasets without manually check cannot be guaranteed, even Google Play may contain malicious applications, which will cause the trained model failure. To address the challenge, we propose a robust Android malware detection approach based on selective ensemble learning, trying to provide an effective solution not that limited to the quality of datasets. The proposed model utilizes genetic algorithm to help find the best combination of the component learners and improve robustness of the model. Our results show that the proposed approach achieves a more robust performance than other approaches in the same area.	['Qi Jing', 'Jianbo Gao', 'Ji Wang']	2019-09-06	null	null	null	null	['android-malware-detection']	['miscellaneous']	[ 3.55863832e-02 -3.69557351e-01 -3.30155253e-01 4.96124364e-02 -3.33002776e-01 -4.21935678e-01 4.50166434e-01 -1.55453950e-01 -1.82166725e-01 6.44126654e-01 -2.90054142e-01 -4.69486892e-01 -2.40398690e-01 -7.82446802e-01 -5.03719270e-01 -6.41717851e-01 -4.18787003e-02 2.25036666e-01 3.62037629e-01 -2.89098740e-01 5.38265884e-01 1.19509131e-01 -1.80890179e+00 1.92203701e-01 1.30239189e+00 8.43343735e-01 1.49619639e-01 4.74843711e-01 -2.07696259e-01 6.56770647e-01 -7.64681995e-01 -2.87515193e-01 3.19908142e-01 -2.49503240e-01 -3.34079117e-01 -3.09300929e-01 -2.01918617e-01 -3.74960840e-01 5.32091707e-02 1.26392794e+00 3.62097412e-01 -2.79260606e-01 5.97958505e-01 -1.34099221e+00 -2.19880760e-01 3.17648798e-01 -6.53716147e-01 2.70526648e-01 1.50239259e-01 7.65348449e-02 3.36072534e-01 -2.92558402e-01 2.55433828e-01 7.98806071e-01 7.68444240e-01 5.83260000e-01 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-9.12470669e-02 2.80043781e-01 2.25629821e-01 -9.44871485e-01 -3.40944529e-01 -2.28061959e-01]	[14.422179222106934, 9.679648399353027]
4e3c79eb-b4ec-4704-941c-8d5858b2e2a3	mixed-curvature-multi-relational-graph-neural	null	null	https://dl.acm.org/doi/abs/10.1145/3442381.3450118	https://dl.acm.org/doi/pdf/10.1145/3442381.3450118	Mixed-Curvature Multi-Relational Graph Neural Network for Knowledge Graph Completion	Knowledge graphs (KGs) have gradually become valuable assets for many AI applications. In a KG, a node denotes an entity, and an edge (or link) denotes a relationship between the entities represented by the nodes. Knowledge graph completion infers and predicts missing edges in a KG automatically. Knowledge graph embeddings have shed light on addressing this task. Recent research embeds KGs in hyperbolic (negatively curved) space instead of conventional Euclidean (zero curved) space and is effective in capturing hierarchical structures. However, as multi-relational graphs, KGs are not structured uniformly and display intrinsic heterogeneous structures. They usually contain rich types of structures, such as hierarchical and cyclic typed structures. Embedding KGs in singlecurvature space, such as Euclidean or hyperbolic space, overlooks the intrinsic heterogeneous structures of KGs, and therefore cannot accurately capture their structures. To address this issue, we propose Mixed-Curvature Multi-Relational Graph Neural Network (M2GNN), a generic approach that embeds multi-relational KGs in a mixed-curvature space for knowledge graph completion. Specifically, we define and construct a mixed-curvature space through a product manifold combining multiple single-curvature spaces (e.g., spherical, hyperbolic, or Euclidean) with the purpose of modeling a variety of structures. However, constructing a mixed-curvature space typically requires manually defining the fixed curvatures, which needs domain knowledge and additional data analysis. Improperly defined curvature space also cannot capture the structures of KGs accurately. To address this problem, we set mixed-curvatures as trainable parameters to better capture the underlying structures of the KGs. Furthermore, we propose a Graph Neural Updater by leveraging the heterogeneous relational context in mixed-curvature space to improve the quality of the embedding. Experiments on three KG datasets demonstrate that the proposed M2GNN can out-perform its single geometry counterpart as well as state-of-the-art embedding methods on the KG completion task.	['Shen Wang∗']	2021-04-19	null	null	null	www-2021-4	['knowledge-graph-embeddings', 'knowledge-graph-embeddings']	['graphs', 'methodology']	[-3.24622631e-01 4.75262791e-01 -2.42079452e-01 -1.46615639e-01 -2.92763561e-01 -8.04374874e-01 2.35201985e-01 1.52869314e-01 1.94068909e-01 1.96738541e-01 3.08329135e-01 -2.76490778e-01 -4.21695054e-01 -1.13742757e+00 -9.10320699e-01 -6.49222791e-01 -2.05653667e-01 5.14930665e-01 1.26486957e-01 -2.61362374e-01 -2.07503662e-01 2.83259183e-01 -1.00737906e+00 -1.13832772e-01 9.35143650e-01 5.99736929e-01 8.81105512e-02 4.26503658e-01 -6.07792065e-02 3.93661886e-01 -9.47515145e-02 -7.44127929e-01 3.12683314e-01 8.54652673e-02 -8.17024589e-01 -6.63406700e-02 4.15117294e-01 -1.04522243e-01 -7.79634118e-01 9.78809476e-01 2.51853163e-03 -1.85554530e-02 7.51424491e-01 -1.39384663e+00 -1.29015625e+00 7.31988788e-01 -4.74509180e-01 -2.84192324e-01 2.06195518e-01 -1.31530896e-01 1.58477747e+00 -1.07004809e+00 5.50924659e-01 1.21209180e+00 9.35899496e-01 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5b2b747a-8273-4d42-ae3f-37ddfe1c5036	units-unsupervised-intermediate-training	2205.04683	null	https://arxiv.org/abs/2205.04683v1	https://arxiv.org/pdf/2205.04683v1.pdf	UNITS: Unsupervised Intermediate Training Stage for Scene Text Detection	Recent scene text detection methods are almost based on deep learning and data-driven. Synthetic data is commonly adopted for pre-training due to expensive annotation cost. However, there are obvious domain discrepancies between synthetic data and real-world data. It may lead to sub-optimal performance to directly adopt the model initialized by synthetic data in the fine-tuning stage. In this paper, we propose a new training paradigm for scene text detection, which introduces an \textbf{UN}supervised \textbf{I}ntermediate \textbf{T}raining \textbf{S}tage (UNITS) that builds a buffer path to real-world data and can alleviate the gap between the pre-training stage and fine-tuning stage. Three training strategies are further explored to perceive information from real-world data in an unsupervised way. With UNITS, scene text detectors are improved without introducing any parameters and computations during inference. Extensive experimental results show consistent performance improvements on three public datasets.	['Weiping Wang', 'Enze Xie', 'Xugong Qin', 'Yu Zhou', 'Youhui Guo']	2022-05-10	null	null	null	null	['scene-text-detection']	['computer-vision']	[ 6.02373481e-01 -3.84519547e-02 9.39389765e-02 -7.47218668e-01 -5.46014190e-01 -3.38130593e-01 7.42128789e-01 2.29600027e-01 -7.61143029e-01 5.18303990e-01 1.69047683e-01 -3.40822220e-01 4.03391987e-01 -1.05031800e+00 -8.59416723e-01 -6.29133999e-01 6.19725943e-01 5.64457655e-01 7.02042699e-01 -1.40740082e-01 3.53733957e-01 1.26548439e-01 -1.63469017e+00 7.29274035e-01 1.00243425e+00 7.57763147e-01 7.25948513e-01 6.32644415e-01 -5.59724629e-01 9.10540044e-01 -7.98570573e-01 -2.71667242e-01 2.86316574e-01 -1.83233917e-01 -6.05201900e-01 4.37600106e-01 4.60970640e-01 -5.21613657e-01 -4.34572667e-01 1.12377310e+00 6.67339087e-01 5.60182706e-02 4.90563691e-01 -1.06340981e+00 -2.78499484e-01 7.71576226e-01 -6.96170330e-01 1.73332483e-01 2.68177748e-01 3.64182591e-01 5.05695701e-01 -1.02178574e+00 5.43850780e-01 1.31154120e+00 4.94206518e-01 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8b56d6e3-353e-43f0-9d3a-86c2f13b1e3c	an-efficient-and-straightforward-online	2306.12574	null	https://arxiv.org/abs/2306.12574v1	https://arxiv.org/pdf/2306.12574v1.pdf	An efficient and straightforward online quantization method for a data stream through remove-birth updating	The growth of network-connected devices is creating an explosion of data, known as big data, and posing significant challenges to efficient data analysis. This data is generated continuously, creating a dynamic flow known as a data stream. The characteristics of a data stream may change dynamically, and this change is known as concept drift. Consequently, a method for handling data streams must efficiently reduce their volume while dynamically adapting to these changing characteristics. This paper proposes a simple online vector quantization method for concept drift. The proposed method identifies and replaces units with low win probability through remove-birth updating, thus achieving a rapid adaptation to concept drift. Furthermore, the results of this study show that the proposed method can generate minimal dead units even in the presence of concept drift. This study also suggests that some metrics calculated from the proposed method will be helpful for drift detection.	['Kazuhisa Fujita']	2023-06-21	null	null	null	null	['quantization']	['methodology']	[ 1.30964160e-01 -4.64455009e-01 -3.72130811e-01 -4.68481988e-01 1.85102060e-01 -3.23926419e-01 9.02800858e-02 7.19426394e-01 -3.03505927e-01 8.62036645e-01 3.15501764e-02 3.06209087e-01 2.96514877e-03 -1.02046835e+00 -3.09609026e-01 -6.33400798e-01 -3.61385942e-01 2.38517672e-01 4.59354162e-01 -1.30910203e-01 4.99867797e-01 2.87349612e-01 -1.98176229e+00 2.39650860e-01 8.82632375e-01 1.27037048e+00 1.42656147e-01 6.16978943e-01 -6.42951310e-01 3.54171783e-01 -1.29541361e+00 2.71773757e-03 7.46267512e-02 -3.84824961e-01 -2.18723297e-01 -9.89789069e-02 -3.03893536e-01 -3.05008918e-01 1.13603629e-01 1.05698526e+00 6.43824220e-01 -1.03915505e-01 2.42728069e-01 -1.44886851e+00 -1.58564404e-01 7.25148737e-01 -5.31013787e-01 8.02546859e-01 4.21227127e-01 -1.84884906e-01 3.50058973e-01 -3.90943319e-01 4.21878994e-01 1.21013224e+00 5.13107181e-01 6.91945851e-01 -7.28704274e-01 -1.01514208e+00 2.97749639e-01 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a566c217-faf0-40ee-ad24-f797d04dea47	fully-automated-binary-pattern-extraction-for	2205.0384	null	https://arxiv.org/abs/2205.03840v1	https://arxiv.org/pdf/2205.03840v1.pdf	Fully Automated Binary Pattern Extraction For Finger Vein Identification using Double Optimization Stages-Based Unsupervised Learning Approach	Today, finger vein identification is gaining popularity as a potential biometric identification framework solution. Machine learning-based unsupervised, supervised, and deep learning algorithms have had a significant influence on finger vein detection and recognition at the moment. Deep learning, on the other hand, necessitates a large number of training datasets that must be manually produced and labeled. In this research, we offer a completely automated unsupervised learning strategy for training dataset creation. Our method is intended to extract and build a decent binary mask training dataset completely automated. In this technique, two optimization steps are devised and employed. The initial stage of optimization is to create a completely automated unsupervised image clustering based on finger vein image localization. Worldwide finger vein pattern orientation estimation is employed in the second optimization to optimize the retrieved finger vein lines. Finally, the proposed system achieves 99.6 - percent pattern extraction accuracy, which is significantly higher than other common unsupervised learning methods like k-means and Fuzzy C-Means (FCM).	['Adil Al-Azzawi', 'Ali Salah Hameed']	2022-05-08	null	null	null	null	['image-clustering']	['computer-vision']	[ 3.12492609e-01 -3.32575113e-01 1.58915587e-03 -4.96950179e-01 3.40472907e-02 -5.99745274e-01 3.54222387e-01 -4.91755940e-02 -5.46858668e-01 5.11410594e-01 -3.06228399e-01 -1.64243713e-01 -2.78382868e-01 -8.40298414e-01 1.68724254e-01 -8.89536262e-01 4.03412640e-01 4.77727681e-01 -1.99801117e-01 3.40512574e-01 7.35622942e-01 1.06914210e+00 -1.35195267e+00 -1.02880031e-01 8.95305693e-01 1.07618606e+00 -1.21217318e-01 5.78542650e-01 -6.04925752e-01 6.37906551e-01 -4.23369825e-01 -4.73883390e-01 4.53079849e-01 -3.70347828e-01 -5.16871452e-01 4.25054669e-01 1.51407003e-01 -4.24292892e-01 -1.55601591e-01 8.57413650e-01 8.14344108e-01 -2.25040808e-01 1.14511323e+00 -8.63630652e-01 -6.19756989e-02 3.31037939e-01 -8.12968910e-01 -1.45198941e-01 -2.78074354e-01 3.22704554e-01 3.37962627e-01 -8.89538884e-01 2.22645745e-01 7.18325853e-01 6.43360913e-01 4.45420027e-01 -1.04884613e+00 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-6.22789204e-01 1.91718206e-01 -7.74739161e-02 -4.38472897e-01 -2.88492367e-02 2.08359584e-01]	[13.050249099731445, 1.0255918502807617]
4990e440-bb76-4252-b005-d45be34d74e2	imputing-knowledge-tracing-data-with-subject	2302.1291	null	https://arxiv.org/abs/2302.12910v1	https://arxiv.org/pdf/2302.12910v1.pdf	Imputing Knowledge Tracing Data with Subject-Based Training via LSTM Variational Autoencoders Frameworks	The issue of missing data poses a great challenge on boosting performance and application of deep learning models in the {\em Knowledge Tracing} (KT) problem. However, there has been the lack of understanding on the issue in the literature. %are not sufficient studies tackling this problem. In this work, to address this challenge, we adopt a subject-based training method to split and impute data by student IDs instead of row number splitting which we call non-subject based training. The benefit of subject-based training can retain the complete sequence for each student and hence achieve efficient training. Further, we leverage two existing deep generative frameworks, namely variational Autoencoders (VAE) and Longitudinal Variational Autoencoders (LVAE) frameworks and build LSTM kernels into them to form LSTM-VAE and LSTM LVAE (noted as VAE and LVAE for simplicity) models to generate quality data. In LVAE, a Gaussian Process (GP) model is trained to disentangle the correlation between the subject (i.e., student) descriptor information (e.g., age, gender) and the latent space. The paper finally compare the model performance between training the original data and training the data imputed with generated data from non-subject based model VAE-NS and subject-based training models (i.e., VAE and LVAE). We demonstrate that the generated data from LSTM-VAE and LSTM-LVAE can boost the original model performance by about 50%. Moreover, the original model just needs 10% more student data to surpass the original performance if the prediction model is small and 50\% more data if the prediction model is large with our proposed frameworks.	['Dongwon Lee', 'Jia Tracy Shen']	2023-02-24	null	null	null	null	['knowledge-tracing']	['miscellaneous']	[-1.81123778e-01 7.76156560e-02 -1.25737667e-01 -3.84024978e-01 -8.27838659e-01 -2.44242251e-01 4.15161341e-01 -2.50431687e-01 -2.84381241e-01 9.42428827e-01 1.76915541e-01 -3.71470690e-01 -2.97690719e-01 -1.09667361e+00 -1.10583639e+00 -7.21557081e-01 5.77454329e-01 4.76258010e-01 -3.39436829e-01 -3.63823846e-02 -1.39899835e-01 -2.08238252e-02 -1.52612126e+00 6.17991239e-02 1.43799162e+00 6.61667705e-01 2.25478172e-01 4.42263603e-01 -2.15149701e-01 7.59682953e-01 -7.06707001e-01 -8.10158730e-01 1.28958113e-02 -2.82738626e-01 -5.48433900e-01 -2.56454408e-01 4.07751054e-01 -4.27906841e-01 -3.20807964e-01 8.30618978e-01 5.94980121e-01 2.14843884e-01 1.01575136e+00 -1.58272183e+00 -1.27347040e+00 7.94101596e-01 -5.92535496e-01 -1.52074620e-01 -6.28171265e-02 -4.46944386e-02 7.17826068e-01 -9.92770851e-01 3.08617741e-01 1.13293183e+00 8.35920751e-01 6.23108685e-01 -1.39857316e+00 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87bf50b4-aa52-4395-b374-031991d29285	percqa-persian-community-question-answering	2112.13238	null	https://arxiv.org/abs/2112.13238v1	https://arxiv.org/pdf/2112.13238v1.pdf	PerCQA: Persian Community Question Answering Dataset	Community Question Answering (CQA) forums provide answers for many real-life questions. Thanks to the large size, these forums are very popular among machine learning researchers. Automatic answer selection, answer ranking, question retrieval, expert finding, and fact-checking are example learning tasks performed using CQA data. In this paper, we present PerCQA, the first Persian dataset for CQA. This dataset contains the questions and answers crawled from the most well-known Persian forum. After data acquisition, we provide rigorous annotation guidelines in an iterative process, and then the annotation of question-answer pairs in SemEvalCQA format. PerCQA contains 989 questions and 21,915 annotated answers. We make PerCQA publicly available to encourage more research in Persian CQA. We also build strong benchmarks for the task of answer selection in PerCQA by using mono- and multi-lingual pre-trained language models	['Hesham Faili', 'Yadollah Yaghoobzadeh', 'Naghme Jamali']	2021-12-25	null	https://aclanthology.org/2022.lrec-1.654	https://aclanthology.org/2022.lrec-1.654.pdf	lrec-2022-6	['answer-selection']	['natural-language-processing']	[-4.28092808e-01 1.11063376e-01 2.69858539e-01 -2.47760937e-01 -1.70709693e+00 -1.08122575e+00 5.67424178e-01 6.60224557e-01 -7.21287668e-01 8.13405573e-01 4.89690095e-01 -5.53431690e-01 -2.11170956e-01 -8.10205698e-01 -1.46972656e-01 3.08837481e-02 2.27954075e-01 1.08684206e+00 9.00153577e-01 -6.43924415e-01 5.24624705e-01 -3.04295868e-01 -1.23652256e+00 7.01044142e-01 1.40851331e+00 7.87151337e-01 3.53442989e-02 9.20412600e-01 -6.85508013e-01 1.53492856e+00 -8.07954013e-01 -1.27879727e+00 -2.61300057e-01 -4.94078785e-01 -1.99289179e+00 -4.79208320e-01 7.33954370e-01 9.61391330e-02 1.21442378e-02 5.37722588e-01 6.06094599e-01 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36fd92d1-ebc9-4b85-9d37-51d08d7a2cf1	towards-future-directions-in-data-integrative	2205.13088	null	https://arxiv.org/abs/2205.13088v1	https://arxiv.org/pdf/2205.13088v1.pdf	Towards future directions in data-integrative supervised prediction of human aging-related genes	Identification of human genes involved in the aging process is critical due to the incidence of many diseases with age. A state-of-the-art approach for this purpose infers a weighted dynamic aging-specific subnetwork by mapping gene expression (GE) levels at different ages onto the protein-protein interaction network (PPIN). Then, it analyzes this subnetwork in a supervised manner by training a predictive model to learn how network topologies of known aging- vs. non-aging-related genes change across ages. Finally, it uses the trained model to predict novel aging-related genes. However, the best current subnetwork resulting from this approach still yields suboptimal prediction accuracy. This could be because it was inferred using outdated GE and PPIN data. Here, we evaluate whether analyzing a weighted dynamic aging-specific subnetwork inferred from newer GE and PPIN data improves prediction accuracy upon analyzing the best current subnetwork inferred from outdated data. Unexpectedly, we find that not to be the case. To understand this, we perform aging-related pathway and Gene Ontology (GO) term enrichment analyses. We find that the suboptimal prediction accuracy, regardless of which GE or PPIN data is used, may be caused by the current knowledge about which genes are aging-related being incomplete, or by the current methods for inferring or analyzing an aging-specific subnetwork being unable to capture all of the aging-related knowledge. These findings can potentially guide future directions towards improving supervised prediction of aging-related genes via -omics data integration.	['Tijana Milenković', 'Khalique Newaz', 'Qi Li']	2022-05-26	null	null	null	null	['data-integration', 'human-aging']	['knowledge-base', 'miscellaneous']	[ 4.03700799e-01 -1.80170480e-02 -2.00322270e-01 -1.88662931e-01 -2.30360657e-01 -3.56686711e-01 -9.54499617e-02 5.42509556e-01 -2.42090896e-01 1.23520601e+00 3.12465906e-01 -5.32243371e-01 -3.88890177e-01 -9.01985347e-01 -7.30198085e-01 -5.88047624e-01 -5.70973575e-01 4.36689466e-01 -1.15183461e-02 -1.60509765e-01 -3.24651115e-02 1.59816071e-01 -1.62868607e+00 -3.37405615e-02 1.11438751e+00 3.90064478e-01 -4.25587073e-02 5.68252861e-01 2.05297932e-01 -2.92605996e-01 -3.08214545e-01 -1.55024692e-01 1.81754455e-02 -5.74088275e-01 -5.57043850e-01 -5.49270689e-01 3.07411462e-01 8.24518055e-02 -1.43078297e-01 8.88546288e-01 5.19879758e-01 -1.92826033e-01 5.07545292e-01 -1.24484599e+00 -8.17471463e-03 2.67391205e-01 -2.09028468e-01 2.57633418e-01 1.46874025e-01 2.30098948e-01 1.03856099e+00 -3.48191351e-01 9.55671191e-01 1.28251112e+00 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-7.27105662e-02 -3.63275379e-01 3.44816931e-02 -8.93619418e-01 -7.96599865e-01 4.87261899e-02 -1.16845004e-01 -7.61692762e-01 -4.54610258e-01 1.67071626e-01]	[6.5778632164001465, 5.503453731536865]
5a93cec6-79e4-4f79-91dd-e6c37a3a9731	forensic-similarity-for-digital-images	1902.04684	null	https://arxiv.org/abs/1902.04684v2	https://arxiv.org/pdf/1902.04684v2.pdf	Forensic Similarity for Digital Images	In this paper we introduce a new digital image forensics approach called forensic similarity, which determines whether two image patches contain the same forensic trace or different forensic traces. One benefit of this approach is that prior knowledge, e.g. training samples, of a forensic trace are not required to make a forensic similarity decision on it in the future. To do this, we propose a two part deep-learning system composed of a CNN-based feature extractor and a three-layer neural network, called the similarity network. This system maps pairs of image patches to a score indicating whether they contain the same or different forensic traces. We evaluated system accuracy of determining whether two image patches were 1) captured by the same or different camera model, 2) manipulated by the same or different editing operation, and 3) manipulated by the same or different manipulation parameter, given a particular editing operation. Experiments demonstrate applicability to a variety of forensic traces, and importantly show efficacy on "unknown" forensic traces that were not used to train the system. Experiments also show that the proposed system significantly improves upon prior art, reducing error rates by more than half. Furthermore, we demonstrated the utility of the forensic similarity approach in two practical applications: forgery detection and localization, and database consistency verification.	['Owen Mayer', 'Matthew C. Stamm']	2019-02-13	null	null	null	null	['image-forensics']	['computer-vision']	[ 2.67680228e-01 -4.41351354e-01 3.53489608e-01 -3.21810156e-01 -7.94295192e-01 -6.63718283e-01 6.10989571e-01 1.65516570e-01 -3.50921571e-01 2.44364426e-01 -4.63795304e-01 -3.67607474e-01 6.50481284e-02 -6.83225274e-01 -1.01478362e+00 -5.84165692e-01 2.94124167e-02 2.89029390e-01 3.19881052e-01 2.69628823e-01 7.59697020e-01 9.35784161e-01 -1.27746606e+00 5.32658100e-01 2.58252203e-01 1.26547599e+00 2.15059325e-01 5.74808538e-01 1.47937909e-01 7.87101507e-01 -1.07771564e+00 -7.47926116e-01 6.94265068e-01 -1.43645376e-01 -5.89804053e-01 1.18097089e-01 9.21674728e-01 -8.98923278e-01 -6.60214186e-01 1.19023335e+00 4.57728416e-01 -8.81099030e-02 3.18982273e-01 -1.12377071e+00 -9.58907068e-01 2.95518667e-01 -7.25953400e-01 7.57218122e-01 3.70714009e-01 5.21906078e-01 4.18202668e-01 -7.71223187e-01 6.45889580e-01 1.11121261e+00 8.75414848e-01 1.57730803e-01 -9.37909961e-01 -9.03497994e-01 -5.02098799e-01 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fb37c75f-aa0e-43f6-9f33-2bc0a0e9cc1a	visually-informed-binaural-audio-generation	2104.06162	null	https://arxiv.org/abs/2104.06162v1	https://arxiv.org/pdf/2104.06162v1.pdf	Visually Informed Binaural Audio Generation without Binaural Audios	Stereophonic audio, especially binaural audio, plays an essential role in immersive viewing environments. Recent research has explored generating visually guided stereophonic audios supervised by multi-channel audio collections. However, due to the requirement of professional recording devices, existing datasets are limited in scale and variety, which impedes the generalization of supervised methods in real-world scenarios. In this work, we propose PseudoBinaural, an effective pipeline that is free of binaural recordings. The key insight is to carefully build pseudo visual-stereo pairs with mono data for training. Specifically, we leverage spherical harmonic decomposition and head-related impulse response (HRIR) to identify the relationship between spatial locations and received binaural audios. Then in the visual modality, corresponding visual cues of the mono data are manually placed at sound source positions to form the pairs. Compared to fully-supervised paradigms, our binaural-recording-free pipeline shows great stability in cross-dataset evaluation and achieves comparable performance under subjective preference. Moreover, combined with binaural recordings, our method is able to further boost the performance of binaural audio generation under supervised settings.	['Dahua Lin', 'Xiaogang Wang', 'Bo Dai', 'Ziwei Liu', 'Hang Zhou', 'Xudong Xu']	2021-04-13	null	http://openaccess.thecvf.com//content/CVPR2021/html/Xu_Visually_Informed_Binaural_Audio_Generation_without_Binaural_Audios_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Xu_Visually_Informed_Binaural_Audio_Generation_without_Binaural_Audios_CVPR_2021_paper.pdf	cvpr-2021-1	['audio-generation']	['audio']	[ 9.65171978e-02 -4.85871464e-01 5.64409256e-01 -1.06094278e-01 -1.40717399e+00 -7.70886779e-01 3.18845570e-01 2.31699683e-02 3.13397758e-02 4.87474412e-01 5.63816011e-01 2.37423833e-02 6.37566522e-02 -5.36127150e-01 -8.63597572e-01 -6.95873201e-01 1.78512275e-01 3.20839696e-02 2.91363239e-01 -1.27784505e-01 5.86748160e-02 -2.38258559e-02 -2.39537501e+00 5.77595472e-01 5.81596851e-01 1.13730335e+00 5.11554718e-01 1.03187490e+00 3.77138257e-01 2.74513930e-01 -6.36806190e-01 -1.01592928e-01 2.16298074e-01 -5.93130291e-01 -2.41340965e-01 -4.85071540e-02 9.91460443e-01 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5dfcf918-789d-495d-8c4d-edd83c5e8b7b	soundify-matching-sound-effects-to-video	2112.09726	null	https://arxiv.org/abs/2112.09726v1	https://arxiv.org/pdf/2112.09726v1.pdf	Soundify: Matching Sound Effects to Video	In the art of video editing, sound is really half the story. A skilled video editor overlays sounds, such as effects and ambients, over footage to add character to an object or immerse the viewer within a space. However, through formative interviews with professional video editors, we found that this process can be extremely tedious and time-consuming. We introduce Soundify, a system that matches sound effects to video. By leveraging labeled, studio-quality sound effects libraries and extending CLIP, a neural network with impressive zero-shot image classification capabilities, into a "zero-shot detector", we are able to produce high-quality results without resource-intensive correspondence learning or audio generation. We encourage you to have a look at, or better yet, have a listen to the results at https://chuanenlin.com/soundify.	['Nikolas Martelaro', 'Yining Shi', 'Cristóbal Valenzuela', 'Anastasis Germanidis', 'David Chuan-En Lin']	2021-12-17	null	null	null	null	['audio-generation']	['audio']	[ 3.46038371e-01 -4.06234264e-01 2.51792818e-01 -1.70341939e-01 -9.99740839e-01 -7.40206599e-01 2.87137389e-01 -2.80011833e-01 1.88101754e-02 2.02010199e-01 3.43232960e-01 -3.25397402e-01 2.42074415e-01 -5.12805760e-01 -9.16873157e-01 -2.24600006e-02 -8.97149295e-02 -1.60058022e-01 3.55849624e-01 2.11021397e-02 5.23242772e-01 1.24481708e-01 -1.84382546e+00 5.49662113e-01 3.53745311e-01 7.09199786e-01 3.93084764e-01 1.23599863e+00 -2.28963405e-01 1.30304623e+00 -7.20747709e-01 -5.22905707e-01 1.07591622e-01 -4.02299166e-01 -4.43177521e-01 -2.27661133e-01 9.52799380e-01 -6.97674870e-01 -3.73658508e-01 9.99529123e-01 5.07325768e-01 6.59812018e-02 1.97611555e-01 -1.22070467e+00 -8.55207026e-01 7.35807180e-01 -2.87592083e-01 3.60077888e-01 9.99901295e-01 4.36169297e-01 8.06994140e-01 -1.05082691e+00 8.51934075e-01 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49053721-3b19-45c5-844a-f6b5a3d19798	bayesian-optimisation-with-formal-guarantees	2106.06067	null	https://arxiv.org/abs/2106.06067v1	https://arxiv.org/pdf/2106.06067v1.pdf	Bayesian Optimisation with Formal Guarantees	Application domains of Bayesian optimization include optimizing black-box functions or very complex functions. The functions we are interested in describe complex real-world systems applied in industrial settings. Even though they do have explicit representations, standard optimization techniques fail to provide validated solutions and correctness guarantees for them. In this paper we present a combination of Bayesian optimisation and SMT-based constraint solving to achieve safe and stable solutions with optimality guarantees.	['Konstantin Korovin', 'Zurab Khasidashvili', 'Franz Brauße']	2021-06-10	null	null	null	null	['bayesian-optimisation']	['methodology']	[-1.14823533e-02 2.15457648e-01 -3.58490705e-01 -5.47242999e-01 -4.55552548e-01 -5.64123452e-01 2.82050937e-01 -1.11888967e-01 -1.11261029e-02 1.18013418e+00 -5.43457031e-01 -6.94187820e-01 -8.29977155e-01 -4.69987541e-01 -5.77547431e-01 -8.51616800e-01 -3.40121120e-01 6.28705978e-01 9.91906077e-02 6.03981465e-02 2.66345561e-01 4.38176930e-01 -1.27568746e+00 -3.61966550e-01 6.71628773e-01 1.11189628e+00 -8.40323120e-02 8.39933515e-01 3.61342013e-01 2.81063467e-01 -7.80239403e-01 -1.59135997e-01 5.62192023e-01 -3.25197838e-02 -4.19238150e-01 3.83870415e-02 -1.37743413e-01 2.52687670e-02 3.66032571e-02 1.20545089e+00 4.31836486e-01 5.41852295e-01 6.34372354e-01 -1.62699747e+00 -4.00819778e-02 4.92788643e-01 -1.42465338e-01 -1.15215324e-01 5.20051122e-01 5.19645393e-01 7.78597414e-01 -5.24903715e-01 1.41951203e-01 1.40694070e+00 5.47182679e-01 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1188b774-a4ac-48fa-91a9-49689951dfe9	persistent-homology-meets-object-unity-object	2305.03815	null	https://arxiv.org/abs/2305.03815v1	https://arxiv.org/pdf/2305.03815v1.pdf	Persistent Homology Meets Object Unity: Object Recognition in Clutter	Recognition of occluded objects in unseen and unstructured indoor environments is a challenging problem for mobile robots. To address this challenge, we propose a new descriptor, TOPS, for point clouds generated from depth images and an accompanying recognition framework, THOR, inspired by human reasoning. The descriptor employs a novel slicing-based approach to compute topological features from filtrations of simplicial complexes using persistent homology, and facilitates reasoning-based recognition using object unity. Apart from a benchmark dataset, we report performance on a new dataset, the UW Indoor Scenes (UW-IS) Occluded dataset, curated using commodity hardware to reflect real-world scenarios with different environmental conditions and degrees of object occlusion. THOR outperforms state-of-the-art methods on both the datasets and achieves substantially higher recognition accuracy for all the scenarios of the UW-IS Occluded dataset. Therefore, THOR, is a promising step toward robust recognition in low-cost robots, meant for everyday use in indoor settings.	['Ashis G. Banerjee', 'Ekta U. Samani']	2023-05-05	null	null	null	null	['unity', 'object-recognition']	['computer-vision', 'computer-vision']	[ 1.80874884e-01 -2.90254075e-02 1.71927556e-01 -2.71256387e-01 -4.05495197e-01 -5.26299000e-01 8.13872337e-01 -3.41292769e-02 -2.71805644e-01 5.86159408e-01 -1.25851125e-01 -1.53885871e-01 -4.35701698e-01 -7.85190046e-01 -9.23015952e-01 -3.08532953e-01 -3.83661568e-01 1.05325687e+00 6.22167885e-01 -4.96488184e-01 4.39098835e-01 8.87832165e-01 -2.23240900e+00 2.54298151e-01 7.15308189e-01 9.55098569e-01 3.95227253e-01 5.28545737e-01 -1.28444239e-01 4.98307914e-01 -3.24161172e-01 -2.03078881e-01 5.61170042e-01 4.88561064e-01 -7.46840417e-01 1.68043405e-01 8.67569327e-01 6.10513650e-02 -4.08121020e-01 7.56793618e-01 1.08109526e-01 3.37710351e-01 8.46716702e-01 -1.38637841e+00 -6.11285865e-01 -1.96652174e-01 9.15522501e-02 -1.80316061e-01 9.40098345e-01 -1.13534883e-01 5.25543571e-01 -1.38661277e+00 1.06880045e+00 1.59248364e+00 1.02223158e+00 2.71474630e-01 -1.18096459e+00 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6.18466616e-01 3.35484862e-01 -1.48776567e+00 -4.17832822e-01 -9.08755809e-02]	[7.486422061920166, -2.4709291458129883]
55958e8a-8ef4-41c9-b56c-f8a4b79ce351	how-deep-learning-sees-the-world-a-survey-on	2305.10862	null	https://arxiv.org/abs/2305.10862v1	https://arxiv.org/pdf/2305.10862v1.pdf	How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses	Deep Learning is currently used to perform multiple tasks, such as object recognition, face recognition, and natural language processing. However, Deep Neural Networks (DNNs) are vulnerable to perturbations that alter the network prediction (adversarial examples), raising concerns regarding its usage in critical areas, such as self-driving vehicles, malware detection, and healthcare. This paper compiles the most recent adversarial attacks, grouped by the attacker capacity, and modern defenses clustered by protection strategies. We also present the new advances regarding Vision Transformers, summarize the datasets and metrics used in the context of adversarial settings, and compare the state-of-the-art results under different attacks, finishing with the identification of open issues.	['Pedro R. M. Inácio', 'Hugo Proença', 'Tiago Roxo', 'Joana C. Costa']	2023-05-18	null	null	null	null	['face-recognition', 'object-recognition']	['computer-vision', 'computer-vision']	[ 2.31513157e-01 6.97250068e-02 6.73335344e-02 -1.63154587e-01 9.58739743e-02 -9.17136669e-01 8.38917255e-01 -2.52499372e-01 -6.00999117e-01 5.33430636e-01 4.31452692e-02 -5.01787722e-01 3.40740681e-02 -7.20310390e-01 -8.42226982e-01 -7.77239978e-01 -2.60475904e-01 5.06971292e-02 1.98674753e-01 -5.05861163e-01 2.14062914e-01 1.24954581e+00 -1.11720860e+00 1.46991909e-01 3.99670154e-01 9.51506436e-01 -7.61209548e-01 5.97559214e-01 1.98931068e-01 9.45224762e-01 -1.06465447e+00 -9.39056396e-01 5.17941117e-01 2.89429203e-02 -5.58576584e-01 -4.63597357e-01 6.59868360e-01 -6.72925234e-01 -1.15725124e+00 1.35964584e+00 5.93130469e-01 -5.69066033e-02 3.93488914e-01 -1.69911313e+00 -7.49672711e-01 3.98601562e-01 -1.51007876e-01 4.62124437e-01 -1.81026280e-01 5.75246751e-01 1.40499189e-01 -3.25042307e-01 3.78171802e-01 1.60101008e+00 6.51921034e-01 1.30970693e+00 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-2.96514213e-01 1.87328070e-01 8.25501859e-01 -5.92775643e-01 -7.31009722e-01 -3.38616312e-01]	[5.574195384979248, 7.8567423820495605]
cfecc962-03d8-4ab7-9049-17cd6078d7ed	a-comprehensive-assessment-of-dialog	2106.03706	null	https://arxiv.org/abs/2106.03706v4	https://arxiv.org/pdf/2106.03706v4.pdf	A Comprehensive Assessment of Dialog Evaluation Metrics	Automatic evaluation metrics are a crucial component of dialog systems research. Standard language evaluation metrics are known to be ineffective for evaluating dialog. As such, recent research has proposed a number of novel, dialog-specific metrics that correlate better with human judgements. Due to the fast pace of research, many of these metrics have been assessed on different datasets and there has as yet been no time for a systematic comparison between them. To this end, this paper provides a comprehensive assessment of recently proposed dialog evaluation metrics on a number of datasets. In this paper, 23 different automatic evaluation metrics are evaluated on 10 different datasets. Furthermore, the metrics are assessed in different settings, to better qualify their respective strengths and weaknesses. Metrics are assessed (1) on both the turn level and the dialog level, (2) for different dialog lengths, (3) for different dialog qualities (e.g., coherence, engaging), (4) for different types of response generation models (i.e., generative, retrieval, simple models and state-of-the-art models), (5) taking into account the similarity of different metrics and (6) exploring combinations of different metrics. This comprehensive assessment offers several takeaways pertaining to dialog evaluation metrics in general. It also suggests how to best assess evaluation metrics and indicates promising directions for future work.	['Shikib Mehri', 'Maxine Eskenazi', 'Yi-Ting Yeh']	2021-06-07	null	https://aclanthology.org/2021.eancs-1.3	https://aclanthology.org/2021.eancs-1.3.pdf	eancs-2021-11	['dialogue-evaluation']	['natural-language-processing']	[-2.99290746e-01 1.91782981e-01 -3.54951695e-02 -6.20333493e-01 -6.89650476e-01 -9.77699101e-01 1.11852717e+00 5.37689328e-01 -5.50239503e-01 8.44125867e-01 7.59279490e-01 -2.42482230e-01 -3.95758003e-01 -4.91307288e-01 5.59088290e-01 -2.66853064e-01 2.85146207e-01 9.33816791e-01 2.26120502e-01 -8.74168813e-01 6.20596707e-01 4.10910577e-01 -1.37022841e+00 2.71293908e-01 7.76319206e-01 6.15440369e-01 -8.69748555e-03 8.66214693e-01 -3.36286634e-01 8.35211396e-01 -1.17214370e+00 -6.82547808e-01 -3.35363507e-01 -7.92847872e-01 -1.20537925e+00 1.42511025e-01 1.51328146e-01 -4.25133407e-01 -8.05935413e-02 6.66189492e-01 6.12479210e-01 2.67504305e-01 6.41675889e-01 -1.19215071e+00 -5.54722190e-01 6.81608856e-01 4.47036237e-01 1.57037124e-01 9.63143647e-01 3.27648371e-01 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f2a2a8da-7e05-45d2-ae73-95df4e7b6169	solving-constraint-satisfaction-problems-with	1801.04515	null	http://arxiv.org/abs/1801.04515v1	http://arxiv.org/pdf/1801.04515v1.pdf	Solving constraint-satisfaction problems with distributed neocortical-like neuronal networks	Finding actions that satisfy the constraints imposed by both external inputs and internal representations is central to decision making. We demonstrate that some important classes of constraint satisfaction problems (CSPs) can be solved by networks composed of homogeneous cooperative-competitive modules that have connectivity similar to motifs observed in the superficial layers of neocortex. The winner-take-all modules are sparsely coupled by programming neurons that embed the constraints onto the otherwise homogeneous modular computational substrate. We show rules that embed any instance of the CSPs planar four-color graph coloring, maximum independent set, and Sudoku on this substrate, and provide mathematical proofs that guarantee these graph coloring problems will convergence to a solution. The network is composed of non-saturating linear threshold neurons. Their lack of right saturation allows the overall network to explore the problem space driven through the unstable dynamics generated by recurrent excitation. The direction of exploration is steered by the constraint neurons. While many problems can be solved using only linear inhibitory constraints, network performance on hard problems benefits significantly when these negative constraints are implemented by non-linear multiplicative inhibition. Overall, our results demonstrate the importance of instability rather than stability in network computation, and also offer insight into the computational role of dual inhibitory mechanisms in neural circuits.	[]	2018-01-14	null	null	null	null	['mathematical-proofs']	['miscellaneous']	[ 5.06294668e-01 5.45410216e-01 -1.31728753e-01 1.21482097e-01 4.34457123e-01 -1.07708108e+00 2.31344134e-01 -3.62671256e-01 -3.45445931e-01 6.12290382e-01 -2.34485134e-01 -1.44973844e-01 -7.59248674e-01 -5.89426816e-01 -6.56428814e-01 -1.00190377e+00 -6.44042313e-01 4.97308940e-01 2.49710634e-01 -3.64750832e-01 2.55026162e-01 3.80426437e-01 -1.37394428e+00 5.78692615e-01 5.75000167e-01 4.68337238e-01 3.42015684e-01 6.10825241e-01 1.36153832e-01 7.94473529e-01 -2.20748544e-01 2.34827191e-01 5.36272764e-01 -7.01097846e-01 -6.80400014e-01 7.71586895e-02 -1.60262417e-02 6.03911459e-01 -2.94257730e-01 1.08240330e+00 1.51164904e-02 -1.86536703e-02 5.48945844e-01 -1.57732570e+00 -7.82493234e-01 8.66149902e-01 -6.03294015e-01 3.21892709e-01 -4.14642766e-02 -7.98491601e-05 1.23071754e+00 -5.13613224e-01 1.02839458e+00 8.95175695e-01 1.98143929e-01 8.96876335e-01 -1.81703031e+00 -6.40689850e-01 9.56387222e-01 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8d01ec99-7b0f-4732-a11a-8831ae349bf4	spontaneous-facial-micro-expression-1	1904.0139	null	http://arxiv.org/abs/1904.01390v1	http://arxiv.org/pdf/1904.01390v1.pdf	Spontaneous Facial Micro-Expression Recognition using 3D Spatiotemporal Convolutional Neural Networks	Facial expression recognition in videos is an active area of research in computer vision. However, fake facial expressions are difficult to be recognized even by humans. On the other hand, facial micro-expressions generally represent the actual emotion of a person, as it is a spontaneous reaction expressed through human face. Despite of a few attempts made for recognizing micro-expressions, still the problem is far from being a solved problem, which is depicted by the poor rate of accuracy shown by the state-of-the-art methods. A few CNN based approaches are found in the literature to recognize micro-facial expressions from still images. Whereas, a spontaneous micro-expression video contains multiple frames that have to be processed together to encode both spatial and temporal information. This paper proposes two 3D-CNN methods: MicroExpSTCNN and MicroExpFuseNet, for spontaneous facial micro-expression recognition by exploiting the spatiotemporal information in CNN framework. The MicroExpSTCNN considers the full spatial information, whereas the MicroExpFuseNet is based on the 3D-CNN feature fusion of the eyes and mouth regions. The experiments are performed over CAS(ME)^2 and SMIC micro-expression databases. The proposed MicroExpSTCNN model outperforms the state-of-the-art methods.	['Shiv Ram Dubey', 'Snehasis Mukherjee', 'Sai Prasanna Teja Reddy', 'Surya Teja Karri']	2019-03-27	null	null	null	null	['micro-expression-recognition']	['computer-vision']	[-1.00015469e-01 -1.99908197e-01 -1.51648283e-01 -5.43988049e-01 -1.59827113e-01 -4.41052653e-02 6.59121811e-01 -4.20504093e-01 -4.51502860e-01 5.87068260e-01 -1.90887854e-01 3.85792106e-01 2.88681656e-01 -4.51592416e-01 -5.36170661e-01 -1.07203555e+00 6.75276816e-02 -1.84167832e-01 -2.35532060e-01 -4.01279420e-01 3.13219987e-02 7.94524014e-01 -1.92422760e+00 4.46468621e-01 2.96545625e-01 1.60445416e+00 -5.28769851e-01 3.23207736e-01 -4.18704331e-01 1.10801470e+00 -6.39815927e-01 -5.97178817e-01 -3.42668965e-02 -6.98939443e-01 -4.18790221e-01 1.39367580e-01 3.96152884e-01 -3.73498857e-01 -6.52692690e-02 1.27127838e+00 2.83985406e-01 -1.43058747e-01 5.21672428e-01 -1.44385016e+00 -6.19308114e-01 -3.90672565e-01 -7.87405670e-01 2.21933752e-01 5.01052618e-01 -2.30510131e-01 3.67282718e-01 -9.91629243e-01 8.25179219e-01 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db6dedac-28c2-41b3-af58-3426da5feb76	open-vocabulary-object-detection-using	2011.10678	null	https://arxiv.org/abs/2011.10678v2	https://arxiv.org/pdf/2011.10678v2.pdf	Open-Vocabulary Object Detection Using Captions	Despite the remarkable accuracy of deep neural networks in object detection, they are costly to train and scale due to supervision requirements. Particularly, learning more object categories typically requires proportionally more bounding box annotations. Weakly supervised and zero-shot learning techniques have been explored to scale object detectors to more categories with less supervision, but they have not been as successful and widely adopted as supervised models. In this paper, we put forth a novel formulation of the object detection problem, namely open-vocabulary object detection, which is more general, more practical, and more effective than weakly supervised and zero-shot approaches. We propose a new method to train object detectors using bounding box annotations for a limited set of object categories, as well as image-caption pairs that cover a larger variety of objects at a significantly lower cost. We show that the proposed method can detect and localize objects for which no bounding box annotation is provided during training, at a significantly higher accuracy than zero-shot approaches. Meanwhile, objects with bounding box annotation can be detected almost as accurately as supervised methods, which is significantly better than weakly supervised baselines. Accordingly, we establish a new state of the art for scalable object detection.	['Shih-Fu Chang', 'Derek Hao Hu', 'Kevin Dela Rosa', 'Alireza Zareian']	2020-11-20	null	http://openaccess.thecvf.com//content/CVPR2021/html/Zareian_Open-Vocabulary_Object_Detection_Using_Captions_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Zareian_Open-Vocabulary_Object_Detection_Using_Captions_CVPR_2021_paper.pdf	cvpr-2021-1	['open-vocabulary-object-detection', 'open-vocabulary-attribute-detection']	['computer-vision', 'computer-vision']	[ 6.01072796e-02 2.90166996e-02 -3.43412936e-01 -3.83286178e-01 -9.50678170e-01 -5.56339920e-01 4.34179723e-01 3.87168139e-01 -5.25426984e-01 3.25700969e-01 -2.69018173e-01 7.11412579e-02 2.44780853e-01 -7.69792616e-01 -8.42207074e-01 -5.17397761e-01 1.10084951e-01 5.78492224e-01 9.46323812e-01 -6.08631521e-02 -5.53798676e-02 3.10940295e-01 -1.86167550e+00 6.85476288e-02 3.22301447e-01 1.09011352e+00 2.86496639e-01 6.08709335e-01 -6.53015971e-02 6.11014307e-01 -4.03192520e-01 -2.76594013e-01 2.55024552e-01 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f9dd3ce2-a141-4fa1-a140-d10a179ba3ed	large-scale-bidirectional-training-for-zero	2211.06774	null	https://arxiv.org/abs/2211.06774v2	https://arxiv.org/pdf/2211.06774v2.pdf	Large-Scale Bidirectional Training for Zero-Shot Image Captioning	When trained on large-scale datasets, image captioning models can understand the content of images from a general domain but often fail to generate accurate, detailed captions. To improve performance, pretraining-and-finetuning has been a key strategy for image captioning. However, we find that large-scale bidirectional training between image and text enables zero-shot image captioning. In this paper, we introduce Bidirectional Image Text Training in largER Scale, BITTERS, an efficient training and inference framework for zero-shot image captioning. We also propose a new evaluation benchmark which comprises of high quality datasets and an extensive set of metrics to properly evaluate zero-shot captioning accuracy and societal bias. We additionally provide an efficient finetuning approach for keyword extraction. We show that careful selection of large-scale training set and model architecture is the key to achieving zero-shot image captioning.	['Seung Hwan Kim', 'Alessandra Sala', 'Sihaeng Lee', 'Sangyun Kim', 'Pyunghwan Ahn', 'Mark Marsden', 'TaeHoon Kim']	2022-11-13	null	null	null	null	['keyword-extraction']	['natural-language-processing']	[ 7.62487710e-01 2.37091735e-01 -7.07545459e-01 -4.75505739e-01 -1.44571328e+00 -4.72759008e-01 7.44175553e-01 -2.98592150e-01 -2.73674786e-01 7.08118618e-01 4.32017237e-01 -2.91954190e-01 4.16424036e-01 -5.51210940e-01 -1.24359524e+00 -3.08821112e-01 3.89966339e-01 4.29169357e-01 2.43507829e-02 -1.52893320e-01 5.11167310e-02 -2.59215415e-01 -1.53535044e+00 6.53809130e-01 7.13023126e-01 9.23146188e-01 4.79096860e-01 9.42560375e-01 -2.47164115e-01 7.61586249e-01 -5.01197875e-01 -7.31231093e-01 1.87183648e-01 -7.34419227e-01 -7.81142235e-01 2.15227664e-01 9.02328730e-01 -6.84202075e-01 -3.31987441e-01 9.24153805e-01 6.42052293e-01 -1.62233561e-01 6.71590865e-01 -1.49807382e+00 -1.33708787e+00 7.71780252e-01 -3.72899920e-01 1.01447277e-01 1.07126631e-01 6.17923498e-01 1.06862772e+00 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469813ae-eb55-4c2a-a8d0-7b79959bafbe	inout-diverse-image-outpainting-via-gan	null	null	http://openaccess.thecvf.com//content/CVPR2022/html/Cheng_InOut_Diverse_Image_Outpainting_via_GAN_Inversion_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Cheng_InOut_Diverse_Image_Outpainting_via_GAN_Inversion_CVPR_2022_paper.pdf	InOut: Diverse Image Outpainting via GAN Inversion	Image outpainting seeks for a semantically consistent extension of the input image beyond its available content. Compared to inpainting --- filling in missing pixels in a way coherent with the neighboring pixels --- outpainting can be achieved in more diverse ways since the problem is less constrained by the surrounding pixels. Existing image outpainting methods pose the problem as a conditional image-to-image translation task, often generating repetitive structures and textures by replicating the content available in the input image. In this work, we formulate the problem from the perspective of inverting generative adversarial networks. Our generator renders micro-patches conditioned on their joint latent code as well as their individual positions in the image. To outpaint an image, we seek for multiple latent codes not only recovering available patches but also synthesizing diverse outpainting by patch-based generation. This leads to richer structure and content in the outpainted regions. Furthermore, our formulation allows for outpainting conditioned on the categorical input, thereby enabling flexible user controls. Extensive experimental results demonstrate the proposed method performs favorably against existing in- and outpainting methods, featuring higher visual quality and diversity.	['Ming-Hsuan Yang', 'Sergey Tulyakov', 'Jian Ren', 'Hsin-Ying Lee', 'Chieh Hubert Lin', 'Yen-Chi Cheng']	2022-01-01	null	null	null	cvpr-2022-1	['image-outpainting']	['computer-vision']	[ 8.41682196e-01 4.09821779e-01 -1.64931417e-01 3.26729612e-04 -7.71660507e-01 -6.39320970e-01 3.23419511e-01 -4.97924715e-01 4.62306328e-02 1.12516594e+00 1.37758434e-01 1.66615233e-01 3.50528687e-01 -9.52586472e-01 -1.05303895e+00 -8.89669240e-01 4.72537845e-01 1.74890533e-01 -2.46765360e-01 -1.77813247e-01 1.37937143e-01 5.80604255e-01 -1.40657163e+00 3.92557293e-01 1.09369040e+00 5.73557079e-01 5.12298346e-01 5.43363810e-01 -1.32218257e-01 8.72542799e-01 -6.96964622e-01 -2.24900067e-01 6.80618823e-01 -1.01594234e+00 -3.51892829e-01 7.20463812e-01 7.66508460e-01 -3.67081434e-01 -4.57089841e-01 1.19076693e+00 3.31748351e-02 6.33239374e-03 4.98070687e-01 -1.16379559e+00 -1.19529617e+00 3.32155168e-01 -7.89836228e-01 -4.61639792e-01 4.34023380e-01 4.19799298e-01 7.08034098e-01 -6.95389986e-01 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d0ca35c6-07a1-4b47-bef8-79a2b1b8a07d	the-newsbridge-telecom-sudparis-voxceleb	2301.07491	null	https://arxiv.org/abs/2301.07491v1	https://arxiv.org/pdf/2301.07491v1.pdf	The Newsbridge -Telecom SudParis VoxCeleb Speaker Recognition Challenge 2022 System Description	We describe the system used by our team for the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC 2022) in the speaker diarization track. Our solution was designed around a new combination of voice activity detection algorithms that uses the strengths of several systems. We introduce a novel multi stream approach with a decision protocol based on classifiers entropy. We called this method a multi-stream voice activity detection and used it with standard baseline diarization embeddings, clustering and resegmentation. With this work, we successfully demonstrated that using a strong baseline and working only on voice activity detection, one can achieved close to state-of-theart results.	['Frédéric Petitpont', 'Jérôme Boudy', 'Yannis Tevissen']	2023-01-17	null	null	null	null	['activity-detection', 'speaker-recognition']	['computer-vision', 'speech']	[-2.16390546e-02 2.09336728e-01 1.62082911e-01 -2.06823140e-01 -1.03791738e+00 -5.76483011e-01 1.06545162e+00 -8.16658959e-02 -4.69249785e-01 1.92000553e-01 6.84090614e-01 -1.24355748e-01 1.97547987e-01 -4.38357182e-02 8.31276178e-02 -7.49706984e-01 -2.07882524e-01 3.96402925e-01 2.69575208e-01 -3.95475864e-01 -9.69710853e-03 5.51829040e-01 -1.88309491e+00 2.82047093e-01 3.05767238e-01 7.86072731e-01 -4.71382141e-01 1.33407021e+00 -1.34977952e-01 6.34070873e-01 -1.04447496e+00 1.32301671e-03 1.63783938e-01 -5.45737863e-01 -6.99048042e-01 -2.57214159e-01 5.85823536e-01 1.78591907e-02 -2.79569745e-01 4.85241920e-01 1.34545791e+00 2.05756932e-01 5.92578173e-01 -1.14607775e+00 1.01511329e-01 6.94557428e-01 3.56734917e-02 7.98485816e-01 7.75131524e-01 1.27155617e-01 8.91261041e-01 -8.86332095e-01 5.47802806e-01 1.37562227e+00 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21ef9a46-7276-4574-b22f-50b88c08524e	joint-channel-estimation-and-turbo	2305.09226	null	https://arxiv.org/abs/2305.09226v1	https://arxiv.org/pdf/2305.09226v1.pdf	Joint Channel Estimation and Turbo Equalization of Single-Carrier Systems over Time-Varying Channels	Block transmission systems have been proven successful over frequency-selective channels. For time-varying channel such as in high-speed mobile communication and underwater communication, existing equalizers assume that channels over different data frames are independent. However, the real-world channels over different data frames are correlated, thereby indicating potentials for performance improvement. In this paper, we propose a joint channel estimation and equalization/decoding algorithm for a single-carrier system that exploits temporal correlations of channel between transmitted data frames. Leveraging the concept of dynamic compressive sensing, our method can utilize the information of several data frames to achieve better performance. The information not only passes between the channel and symbol, but also the channels over different data frames. Numerical simulations using an extensively validated underwater acoustic model with a time-varying channel establish that the proposed algorithm outperforms the former bilinear generalized approximate message passing equalizer and classic minimum mean square error turbo equalizer in bit error rate and channel estimation normalized mean square error. The algorithm idea we present can also find applications in other bilinear multiple measurements vector compressive sensing problems.	['Yan Wei', 'Fengzhong Qu', 'Zhipeng Li', 'Xingbin Tu', 'Minhao Zhang', 'Yifan Wang']	2023-05-16	null	null	null	null	['compressive-sensing']	['computer-vision']	[ 6.86387777e-01 4.32900339e-02 1.18806869e-01 -6.69779480e-02 -7.10449576e-01 -1.92709938e-01 7.82785937e-02 -1.09972522e-01 -6.51763439e-01 8.35794985e-01 1.91396102e-01 -1.73520863e-01 -2.38588735e-01 -4.65680748e-01 -9.62484241e-01 -1.04382348e+00 -8.94811809e-01 -3.79190683e-01 -1.29385635e-01 -1.99892804e-01 4.25586909e-01 -7.31084645e-02 -1.28280735e+00 -1.63373485e-01 6.79325163e-01 9.59723115e-01 6.46477759e-01 1.11651981e+00 3.99841279e-01 3.95703286e-01 -4.79887813e-01 6.96842000e-02 4.46163028e-01 -6.58438802e-01 4.35236935e-03 1.13219731e-01 8.27532634e-02 -7.37321556e-01 -8.41528237e-01 1.23301375e+00 6.91348791e-01 -2.37434462e-01 1.83091834e-01 -8.21197569e-01 -7.11320192e-02 8.62334907e-01 -4.50623125e-01 1.41219810e-01 3.12309742e-01 -3.68560642e-01 4.59443808e-01 -1.12284505e+00 -1.29699018e-02 1.15412271e+00 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-1.11125326e+00 -4.72215623e-01 -1.04046309e+00 -1.12382257e+00 -2.19290391e-01 1.22840762e+00 -4.82395113e-01 -8.94804060e-01 2.30004400e-01]	[6.473203182220459, 1.3386143445968628]
36f67b94-6614-4b1e-9e82-3befe87762e2	eye-gaze-estimation-model-analysis	2207.14373	null	https://arxiv.org/abs/2207.14373v1	https://arxiv.org/pdf/2207.14373v1.pdf	Eye Gaze Estimation Model Analysis	We explore techniques for eye gaze estimation using machine learning. Eye gaze estimation is a common problem for various behavior analysis and human-computer interfaces. The purpose of this work is to discuss various model types for eye gaze estimation and present the results from predicting gaze direction using eye landmarks in unconstrained settings. In unconstrained real-world settings, feature-based and model-based methods are outperformed by recent appearance-based methods due to factors like illumination changes and other visual artifacts. We discuss a learning-based method for eye region landmark localization trained exclusively on synthetic data. We discuss how to use detected landmarks as input to iterative model-fitting and lightweight learning-based gaze estimation methods and how to use the model for person-independent and personalized gaze estimations.	['Ayush Kumar', 'Aveena Kottwani']	2022-07-28	null	null	null	null	['gaze-estimation']	['computer-vision']	[-5.65228611e-03 -1.00775383e-01 -1.29785314e-01 -6.32450163e-01 2.70645488e-02 -1.19357325e-01 1.01517871e-01 -3.19208890e-01 -4.40047413e-01 7.57203579e-01 -2.41136253e-01 -1.00491270e-01 9.78614390e-02 2.72355348e-01 -4.78231311e-01 -5.57830691e-01 1.01043634e-01 -1.86662719e-01 2.65857100e-01 3.44172902e-02 8.62237632e-01 4.32361960e-01 -2.38864732e+00 -3.40262920e-01 6.52984917e-01 8.49190295e-01 3.22133452e-02 1.00083375e+00 3.87875438e-02 4.52978671e-01 -5.29099107e-01 1.46114573e-01 5.69163896e-02 -4.59788412e-01 -4.75710243e-01 1.26948237e-01 1.20861244e+00 -2.74788260e-01 5.80863655e-01 7.87868440e-01 7.11144686e-01 2.03997061e-01 7.17217505e-01 -1.71400511e+00 -3.44449461e-01 -5.41776836e-01 -1.28807986e+00 3.47177207e-01 1.12150586e+00 1.69822127e-01 3.27904612e-01 -9.18692708e-01 5.68376482e-01 1.11664248e+00 7.29170561e-01 8.81618321e-01 -1.13402021e+00 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994d5778-d2a4-4dc9-80ee-9e852089fcca	a-label-aware-autoregressive-framework-for	null	null	https://aclanthology.org/2022.findings-naacl.171	https://aclanthology.org/2022.findings-naacl.171.pdf	A Label-Aware Autoregressive Framework for Cross-Domain NER	Cross-domain named entity recognition (NER) aims to borrow the entity information from the source domain to help the entity recognition in the target domain with limited labeled data. Despite the promising performance of existing approaches, most of them focus on reducing the discrepancy of token representation between source and target domains, while the transfer of the valuable label information is often not explicitly considered or even ignored. Therefore, we propose a novel autoregressive framework to advance cross-domain NER by first enhancing the relationship between labels and tokens and then further improving the transferability of label information. Specifically, we associate each label with an embedding vector, and for each token, we utilize a bidirectional LSTM (Bi-LSTM) to encode the labels of its previous tokens for modeling internal context information and label dependence. Afterward, we propose a Bi-Attention module that merges the token representation from a pre-trained model and the label features from the Bi-LSTM as the label-aware information, which is concatenated to the token representation to facilitate cross-domain NER. In doing so, label information contained in the embedding vectors can be effectively transferred to the target domain, and Bi-LSTM can further model the label relationship among different domains by pre-train and then fine-tune setting. Experimental results on several datasets confirm the effectiveness of our model, where our model achieves significant improvements over the state of the arts.	['Tsung-Hui Chang', 'Xiang Wan', 'Dan Guo', 'He Zhao', 'Jinpeng Hu']	null	null	null	null	findings-naacl-2022-7	['cross-domain-named-entity-recognition']	['natural-language-processing']	[ 1.42357824e-02 -1.13834582e-01 -2.84201056e-01 -6.37309611e-01 -7.54315376e-01 -6.38121963e-01 3.67737353e-01 9.53035057e-02 -6.43883049e-01 6.06402695e-01 3.57115269e-01 4.34341058e-02 2.43292242e-01 -7.81888664e-01 -4.63722020e-01 -6.04547858e-01 3.61681908e-01 1.94619119e-01 1.59316123e-01 1.54693297e-03 -1.28165215e-01 2.71315426e-01 -8.78464401e-01 1.66269690e-01 9.73781466e-01 1.13952768e+00 2.26111501e-01 -9.43086892e-02 -7.41105855e-01 9.30983543e-01 -5.17454326e-01 -2.57546991e-01 -5.87957501e-02 -2.45679229e-01 -9.36683893e-01 -1.11598000e-01 -1.64013088e-01 -2.66440958e-01 -4.07115161e-01 8.89534414e-01 5.50371468e-01 2.84542650e-01 6.07456744e-01 -9.86843884e-01 -1.01281714e+00 5.83999932e-01 -4.14319128e-01 -3.12311277e-02 -9.62540954e-02 -1.57064214e-01 9.42804277e-01 -1.08120918e+00 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ac8c2867-9283-4160-94e3-4b5b16c69c63	guiding-pretraining-in-reinforcement-learning	2302.06692	null	https://arxiv.org/abs/2302.06692v1	https://arxiv.org/pdf/2302.06692v1.pdf	Guiding Pretraining in Reinforcement Learning with Large Language Models	Reinforcement learning algorithms typically struggle in the absence of a dense, well-shaped reward function. Intrinsically motivated exploration methods address this limitation by rewarding agents for visiting novel states or transitions, but these methods offer limited benefits in large environments where most discovered novelty is irrelevant for downstream tasks. We describe a method that uses background knowledge from text corpora to shape exploration. This method, called ELLM (Exploring with LLMs) rewards an agent for achieving goals suggested by a language model prompted with a description of the agent's current state. By leveraging large-scale language model pretraining, ELLM guides agents toward human-meaningful and plausibly useful behaviors without requiring a human in the loop. We evaluate ELLM in the Crafter game environment and the Housekeep robotic simulator, showing that ELLM-trained agents have better coverage of common-sense behaviors during pretraining and usually match or improve performance on a range of downstream tasks.	['Jacob Andreas', 'Abhishek Gupta', 'Pieter Abbeel', 'Trevor Darrell', 'Cédric Colas', 'Zihan Wang', 'Olivia Watkins', 'Yuqing Du']	2023-02-13	null	null	null	null	['common-sense-reasoning']	['reasoning']	[-1.59494355e-01 4.96859908e-01 -4.23208565e-01 -5.54983318e-02 -6.69497013e-01 -7.56286919e-01 9.98347223e-01 3.26651067e-01 -9.11989093e-01 1.08344316e+00 4.21394557e-01 -3.58006865e-01 -1.04970478e-01 -4.54133451e-01 -6.93473399e-01 -4.55942631e-01 -4.50791210e-01 8.17937434e-01 2.44701847e-01 -6.03949785e-01 3.63351613e-01 3.72842282e-01 -1.55402601e+00 -9.63558033e-02 7.31178224e-01 1.40591502e-01 6.36069596e-01 5.76423168e-01 3.23742814e-02 1.01317668e+00 -4.68097180e-01 1.43987104e-01 4.59450930e-01 -6.90086544e-01 -9.06494617e-01 -1.18307881e-01 -3.95990849e-01 -4.18776363e-01 -3.29405069e-01 8.72301400e-01 1.76844880e-01 7.83122897e-01 4.31884408e-01 -1.25897074e+00 -2.70285457e-01 1.14773870e+00 -2.68905848e-01 2.32787356e-01 5.20992219e-01 9.18263435e-01 7.66099453e-01 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120f7bd9-68e2-420c-a9ec-292f65670c6d	heartbeat-classification-in-wearables-using	1908.06865	null	https://arxiv.org/abs/1908.06865v1	https://arxiv.org/pdf/1908.06865v1.pdf	Heartbeat Classification in Wearables Using Multi-layer Perceptron and Time-Frequency Joint Distribution of ECG	Heartbeat classification using electrocardiogram (ECG) data is a vital assistive technology for wearable health solutions. We propose heartbeat feature classification based on a novel sparse representation using time-frequency joint distribution of ECG. Fundamental to this is a multi-layer perceptron, which incorporates these signatures to detect cardiac arrhythmia. This approach is validated with ECG data from MIT-BIH arrhythmia database. Results show that our approach has an average 95.7% accuracy, an improvement of 22% over state-of-the-art approaches. Additionally, ECG sparse distributed representations generates only 3.7% false negatives, reduction of 89% with respect to existing ECG signal classification techniques.	['Siebren Schaafsma', 'Francky Catthoor', 'Anup Das']	2019-08-13	null	null	null	null	['heartbeat-classification']	['medical']	[ 5.70361257e-01 -1.97702814e-02 -1.07618272e-01 -3.42816263e-01 -6.18755221e-01 -4.05607074e-02 -2.43368655e-01 4.50445652e-01 -1.55067354e-01 7.99005389e-01 2.69781172e-01 -5.53914048e-02 -4.70011771e-01 -4.32908416e-01 5.57407252e-02 -6.09369755e-01 -5.50358236e-01 1.78650022e-01 -4.56235617e-01 2.06870824e-01 -5.40711395e-02 4.19922322e-01 -1.24524546e+00 6.26892328e-01 3.77492905e-01 1.30215108e+00 -5.61536968e-01 1.03406358e+00 5.10765910e-01 6.47017300e-01 -9.08154547e-01 2.06684873e-01 5.83092123e-02 -7.52216756e-01 -3.96971464e-01 -2.39159942e-01 3.33345309e-02 -5.04357032e-02 -1.93093181e-01 5.10844707e-01 1.24243164e+00 -3.84094089e-01 6.57602787e-01 -6.60652041e-01 -7.40585998e-02 6.47457182e-01 -6.16222262e-01 7.89675415e-01 4.63365674e-01 -3.07654649e-01 7.65442669e-01 -8.09520423e-01 2.31987089e-01 3.77320468e-01 1.40413582e+00 2.25519240e-01 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f3fe6c9f-f2ed-48cb-b21b-927a4776ff8e	self-attention-convlstm-for-spatiotemporal	null	null	https://ojs.aaai.org//index.php/AAAI/article/view/6819	https://ojs.aaai.org/index.php/AAAI/article/view/6819/6673	Self-Attention ConvLSTM for Spatiotemporal Prediction	Spatiotemporal prediction is challenging due to the complex dynamic motion and appearance changes. Existing work concentrates on embedding additional cells into the standard ConvLSTM to memorize spatial appearances during the prediction. These models always rely on the convolution layers to capture the spatial dependence, which are local and inefficient. However, long-range spatial dependencies are significant for spatial applications. To extract spatial features with both global and local dependencies, we introduce the self-attention mechanism into ConvLSTM. Specifically, a novel self-attention memory (SAM) is proposed to memorize features with long-range dependencies in terms of spatial and temporal domains. Based on the self-attention, SAM can produce features by aggregating features across all positions of both the input itself and memory features with pair-wise similarity scores. Moreover, the additional memory is updated by a gating mechanism on aggregated features and an established highway with the memory of the previous time step. Therefore, through SAM, we can extract features with long-range spatiotemporal dependencies. Furthermore, we embed the SAM into a standard ConvLSTM to construct a self-attention ConvLSTM (SA-ConvLSTM) for the spatiotemporal prediction. In experiments, we apply the SA-ConvLSTM to perform frame prediction on the MovingMNIST and KTH datasets and traffic flow prediction on the TexiBJ dataset. Our SA-ConvLSTM achieves state-of-the-art results on both datasets with fewer parameters and higher time efficiency than previous state-of-the-art method.	['Chun Yuan', 'Yangyang Cheng', 'Zhuobin Zheng', 'Maomao Li', 'Zhihui Lin']	2020-04-03	null	null	null	aaai-2020-4	['video-prediction']	['computer-vision']	[-1.08691953e-01 -6.82326019e-01 -2.52042562e-01 -5.75340867e-01 -3.06554586e-01 5.78454472e-02 4.60454494e-01 -1.20265409e-01 -5.53012729e-01 7.60872602e-01 2.84317821e-01 -7.13889822e-02 1.27142891e-02 -9.75248218e-01 -8.70156050e-01 -7.31959939e-01 -1.18149593e-01 -1.98993489e-01 9.87148583e-01 -7.49276429e-02 2.85903364e-01 5.88807642e-01 -1.54942167e+00 7.20096290e-01 8.26845706e-01 1.41794908e+00 5.94475865e-01 6.20672047e-01 -2.63886958e-01 1.12114775e+00 -3.37950200e-01 9.64965224e-02 -1.21104382e-01 -2.70833164e-01 -6.69507325e-01 -1.43612206e-01 5.04625142e-01 -3.03227663e-01 -9.37999129e-01 5.09278238e-01 2.61543542e-01 6.49212599e-01 3.51395398e-01 -1.02717698e+00 -9.40688074e-01 5.99344969e-02 -6.81802332e-01 1.01730084e+00 -8.67726654e-02 3.95561099e-01 8.70969057e-01 -1.06301272e+00 3.45932871e-01 1.19073391e+00 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-1.06659257e+00 2.90226042e-01 -8.70073318e-01 -2.71593302e-01 3.53644192e-01 4.47491884e-01 -7.59138584e-01 1.09562799e-01 -9.75115504e-03]	[8.844596862792969, 0.3916183114051819]
65fbc964-0f7b-4b4d-9928-9807e8c1872e	unleashing-infinite-length-input-capacity-for	2304.13343	null	https://arxiv.org/abs/2304.13343v1	https://arxiv.org/pdf/2304.13343v1.pdf	Unleashing Infinite-Length Input Capacity for Large-scale Language Models with Self-Controlled Memory System	Large-scale Language Models (LLMs) are constrained by their inability to process lengthy inputs. To address this limitation, we propose the Self-Controlled Memory (SCM) system to unleash infinite-length input capacity for large-scale language models. Our SCM system is composed of three key modules: the language model agent, the memory stream, and the memory controller. The language model agent iteratively processes ultra-long inputs and stores all historical information in the memory stream. The memory controller provides the agent with both long-term memory (archived memory) and short-term memory (flash memory) to generate precise and coherent responses. The controller determines which memories from archived memory should be activated and how to incorporate them into the model input. Our SCM system can be integrated with any LLMs to enable them to process ultra-long texts without any modification or fine-tuning. Experimental results show that our SCM system enables LLMs, which are not optimized for multi-turn dialogue, to achieve multi-turn dialogue capabilities that are comparable to ChatGPT, and to outperform ChatGPT in scenarios involving ultra-long document summarization or long-term conversations. Additionally, we will supply a test set, which covers common long-text input scenarios, for evaluating the abilities of LLMs in processing long documents.~\footnote{Working in progress.}\footnote{\url{https://github.com/wbbeyourself/SCM4LLMs}}	['Zhoujun Li', 'Zejun Ma', 'Lu Lu', 'Peihao Wu', 'Shuangzhi Wu', 'Hui Huang', 'Bing Wang', 'Xinnian Liang']	2023-04-26	null	null	null	null	['document-summarization']	['natural-language-processing']	[ 1.76926497e-02 3.06666911e-01 -1.85264558e-01 -2.21549034e-01 -1.23044920e+00 -7.40459383e-01 8.11217844e-01 3.59622017e-02 -4.17562217e-01 9.48567212e-01 5.39592624e-01 -4.92055625e-01 1.79889545e-01 -7.08871007e-01 -3.85742038e-01 -1.64821863e-01 1.40738845e-01 9.41038251e-01 4.39452738e-01 -3.93969774e-01 5.60539544e-01 6.06462546e-02 -1.27937818e+00 8.76033068e-01 1.00610173e+00 3.04704964e-01 7.46721685e-01 1.35292673e+00 -6.27806604e-01 1.05931818e+00 -1.06413376e+00 4.72645983e-02 -3.52856606e-01 -5.64967096e-01 -1.28048921e+00 -2.72963792e-01 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5905305e-d833-48a1-97b5-a40e1cc14c02	efficient-global-point-cloud-alignment-using	1603.04868	null	http://arxiv.org/abs/1603.04868v3	http://arxiv.org/pdf/1603.04868v3.pdf	Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures	Point cloud alignment is a common problem in computer vision and robotics, with applications ranging from 3D object recognition to reconstruction. We propose a novel approach to the alignment problem that utilizes Bayesian nonparametrics to describe the point cloud and surface normal densities, and branch and bound (BB) optimization to recover the relative transformation. BB uses a novel, refinable, near-uniform tessellation of rotation space using 4D tetrahedra, leading to more efficient optimization compared to the common axis-angle tessellation. We provide objective function bounds for pruning given the proposed tessellation, and prove that BB converges to the optimum of the cost function along with providing its computational complexity. Finally, we empirically demonstrate the efficiency of the proposed approach as well as its robustness to real-world conditions such as missing data and partial overlap.	['John W. Fisher III', 'Trevor Campbell', 'Julian Straub', 'Jonathan P. How']	2016-03-15	efficient-global-point-cloud-alignment-using-1	http://openaccess.thecvf.com/content_cvpr_2017/html/Straub_Efficient_Global_Point_CVPR_2017_paper.html	http://openaccess.thecvf.com/content_cvpr_2017/papers/Straub_Efficient_Global_Point_CVPR_2017_paper.pdf	cvpr-2017-7	['3d-object-recognition']	['computer-vision']	[-8.28618333e-02 -2.64883161e-01 -1.65003851e-01 -3.28270420e-02 -7.01484144e-01 -4.18094695e-01 4.97970909e-01 1.55336484e-01 -2.89676368e-01 4.63099569e-01 -4.36997026e-01 -2.62349397e-01 -2.87433177e-01 -4.81478840e-01 -1.14267039e+00 -7.53400743e-01 8.68350416e-02 1.10417283e+00 1.85698301e-01 3.97926092e-01 5.31441927e-01 1.17162645e+00 -1.46571767e+00 -7.55787730e-01 1.01209867e+00 1.08314562e+00 1.47487968e-01 3.70444059e-01 -5.11738397e-02 -3.40425760e-01 -5.04402876e-01 -1.56518027e-01 5.56050599e-01 4.19096828e-01 -4.01924193e-01 5.16142249e-01 3.70982468e-01 -1.85366109e-01 1.54614329e-01 1.13995969e+00 8.33327398e-02 1.90979362e-01 1.07735646e+00 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3b389ec5-2d31-4ac4-adcf-7a51f5ac9d6e	popdx-an-automated-framework-for-patient	2208.11223	null	https://arxiv.org/abs/2208.11223v2	https://arxiv.org/pdf/2208.11223v2.pdf	POPDx: An Automated Framework for Patient Phenotyping across 392,246 Individuals in the UK Biobank Study	Objective For the UK Biobank standardized phenotype codes are associated with patients who have been hospitalized but are missing for many patients who have been treated exclusively in an outpatient setting. We describe a method for phenotype recognition that imputes phenotype codes for all UK Biobank participants. Materials and Methods POPDx (Population-based Objective Phenotyping by Deep Extrapolation) is a bilinear machine learning framework for simultaneously estimating the probabilities of 1,538 phenotype codes. We extracted phenotypic and health-related information of 392,246 individuals from the UK Biobank for POPDx development and evaluation. A total of 12,803 ICD-10 diagnosis codes of the patients were converted to 1,538 Phecodes as gold standard labels. The POPDx framework was evaluated and compared to other available methods on automated multi-phenotype recognition. Results POPDx can predict phenotypes that are rare or even unobserved in training. We demonstrate substantial improvement of automated multi-phenotype recognition across 22 disease categories, and its application in identifying key epidemiological features associated with each phenotype. Conclusions POPDx helps provide well-defined cohorts for downstream studies. It is a general purpose method that can be applied to other biobanks with diverse but incomplete data.	['Russ B. Altman', 'Sheng Wang', 'Lu Yang']	2022-08-23	null	null	null	null	['patient-phenotyping']	['medical']	[ 2.89134920e-01 3.44417766e-02 -2.65340745e-01 -5.47246456e-01 -1.33847594e+00 -4.28968132e-01 -2.03442108e-02 9.07884479e-01 -1.34370282e-01 1.23136103e+00 5.26873827e-01 -8.39490294e-02 -3.45150590e-01 -6.31191909e-01 -4.56996024e-01 -6.55337214e-01 -3.54661494e-01 1.25366831e+00 -7.05426395e-01 5.21011055e-01 -5.36774814e-01 3.08865309e-01 -8.91547799e-01 6.78950906e-01 6.33911133e-01 7.50230193e-01 -1.38475507e-01 7.96617210e-01 4.58750486e-01 4.01284873e-01 -7.41443574e-01 -4.80761677e-01 -1.44778296e-01 -3.89968544e-01 -4.17895943e-01 -5.99677205e-01 4.44647342e-01 -1.30274832e-01 -1.13240048e-01 5.58038056e-01 1.03212857e+00 -6.73567951e-01 9.26650167e-01 -1.09288156e+00 -1.01102376e+00 5.26663125e-01 -3.87828767e-01 -2.51116063e-02 5.26371419e-01 1.87315732e-01 1.10843313e+00 -3.84651780e-01 8.06089520e-01 8.75562310e-01 1.53360355e+00 6.00692809e-01 -1.65798962e+00 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e2e1ab1a-0118-421b-ad93-016a090d93af	soda10m-towards-large-scale-object-detection	2106.11118	null	https://arxiv.org/abs/2106.11118v3	https://arxiv.org/pdf/2106.11118v3.pdf	SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving	Aiming at facilitating a real-world, ever-evolving and scalable autonomous driving system, we present a large-scale dataset for standardizing the evaluation of different self-supervised and semi-supervised approaches by learning from raw data, which is the first and largest dataset to date. Existing autonomous driving systems heavily rely on `perfect' visual perception models (i.e., detection) trained using extensive annotated data to ensure safety. However, it is unrealistic to elaborately label instances of all scenarios and circumstances (i.e., night, extreme weather, cities) when deploying a robust autonomous driving system. Motivated by recent advances of self-supervised and semi-supervised learning, a promising direction is to learn a robust detection model by collaboratively exploiting large-scale unlabeled data and few labeled data. Existing datasets either provide only a small amount of data or covers limited domains with full annotation, hindering the exploration of large-scale pre-trained models. Here, we release a Large-Scale 2D Self/semi-supervised Object Detection dataset for Autonomous driving, named as SODA10M, containing 10 million unlabeled images and 20K images labeled with 6 representative object categories. To improve diversity, the images are collected within 27833 driving hours under different weather conditions, periods and location scenes of 32 different cities. We provide extensive experiments and deep analyses of existing popular self/semi-supervised approaches, and give some interesting findings in autonomous driving scope. Experiments show that SODA10M can serve as a promising pre-training dataset for different self-supervised learning methods, which gives superior performance when fine-tuning with different downstream tasks (i.e., detection, semantic/instance segmentation) in autonomous driving domain. More information can refer to https://soda-2d.github.io.	['Jiageng Mao', 'Xiaodan Liang', 'Chunjing Xu', 'Zhenguo Li', 'Wei zhang', 'Chaoqiang Ye', 'Lanqing Hong', 'Kai Chen', 'Hang Xu', 'Xiwen Liang', 'Jianhua Han']	2021-06-21	null	null	null	null	['semi-supervised-object-detection']	['computer-vision']	[ 1.78101566e-02 -3.20344828e-02 -3.72992247e-01 -6.76930904e-01 -6.37231171e-01 -6.19902134e-01 7.01225281e-01 -2.07235888e-01 -4.73814040e-01 4.40909743e-01 -2.69775897e-01 -4.50121343e-01 2.43287086e-01 -6.78679943e-01 -8.20501864e-01 -7.09192455e-01 1.24756463e-01 5.03616869e-01 7.20219493e-01 -3.80473822e-01 2.69524585e-02 2.33751968e-01 -2.14658451e+00 -5.05255237e-02 9.19407129e-01 8.55082572e-01 5.92871070e-01 5.23572981e-01 5.04795425e-02 4.65225607e-01 -5.20568609e-01 -2.26122305e-01 6.16993904e-01 -9.03775729e-03 -4.69269544e-01 2.03345850e-01 4.74566132e-01 -2.51677394e-01 -4.03042674e-01 1.10415936e+00 3.76520008e-01 -9.10472423e-02 5.72391152e-01 -1.74326503e+00 -3.93288761e-01 2.88189799e-01 -4.66142982e-01 3.59934121e-01 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b9843a9d-caca-4b09-8a6c-362c1f9cddd9	towards-on-device-domain-adaptation-for-noise	null	null	https://ieeexplore.ieee.org/document/9869990	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9869990	Towards On-device Domain Adaptation for Noise-Robust Keyword Spotting	The accuracy of a keyword spotting model deployed on embedded devices often degrades when the system is exposed to real environments with significant noise. In this paper, we explore a methodology for tailoring a model to on-site noises through on-device domain adaptation, while accounting for the edge computing-associated costs. We show that accuracy improvements by up to 18 % can be obtained by specialising on difficult, previously unseen noise types, on embedded devices with a power budget in the Watt range, with a storage requirement of 1.1 GB. We also demonstrate an accuracy improvement of 1.43% on an ultra-low power platform consuming few-10mW, requiring only 1.47 MB of memory for the adaptation stage, at a one-time energy cost of 5.81 J.	['Luca Benini', 'Miguel de Prado', 'Manuele Rusci', 'Lukas Cavigelli', 'Cristian Cioflan']	2022-06-13	null	null	null	ieee-international-conference-on-artificial	['keyword-spotting']	['speech']	[ 3.70848000e-01 -2.32146502e-01 5.86572364e-02 -1.86210230e-01 -1.04815555e+00 -5.54174304e-01 9.46973488e-02 1.60363406e-01 -6.47248805e-01 5.96247137e-01 -3.15871090e-01 -4.23197299e-01 1.13728922e-02 -6.18939757e-01 -6.69127405e-01 -5.01620591e-01 1.75903440e-01 2.40203500e-01 4.60567504e-01 1.09379232e-01 -2.14496642e-01 2.88716614e-01 -1.38218129e+00 4.36710939e-02 5.72901964e-01 1.43049896e+00 2.93804258e-01 1.06568444e+00 2.05838531e-01 2.19946727e-01 -1.08237970e+00 -3.75177234e-01 1.06488638e-01 9.01883841e-02 -2.28185803e-01 -3.27355176e-01 9.91071463e-02 -5.73356748e-01 -5.91406047e-01 1.10274971e+00 1.04155040e+00 -3.11563075e-01 1.93090662e-01 -9.10586536e-01 -9.07880738e-02 6.19055867e-01 -2.09700122e-01 4.29116964e-01 1.71679705e-01 1.91969216e-01 5.09861648e-01 -6.80851758e-01 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5af53eb3-c8a4-494a-af56-f27911bcb69e	visualize-before-you-write-imagination-guided	2210.03765	null	https://arxiv.org/abs/2210.03765v4	https://arxiv.org/pdf/2210.03765v4.pdf	Visualize Before You Write: Imagination-Guided Open-Ended Text Generation	Recent advances in text-to-image synthesis make it possible to visualize machine imaginations for a given context. On the other hand, when generating text, human writers are gifted at creative visualization, which enhances their writings by forming imaginations as blueprints before putting down the stories in words. Inspired by such a cognitive process, we ask the natural question of whether we can endow machines with the same ability to utilize visual information and construct a general picture of the context to guide text generation. In this work, we propose iNLG that uses machine-generated images to guide language models in open-ended text generation. The experiments and analyses demonstrate the effectiveness of iNLG on open-ended text generation tasks, including text completion, story generation, and concept-to-text generation in both few-shot and full-data scenarios. Both automatic metrics and human evaluations verify that the text snippets generated by our iNLG are coherent and informative while displaying minor degeneration.	['William Yang Wang', 'Miguel Eckstein', 'Xin Eric Wang', 'Wenda Xu', 'Yujie Lu', 'An Yan', 'Wanrong Zhu']	2022-10-07	null	null	null	null	['story-generation', 'concept-to-text-generation']	['natural-language-processing', 'natural-language-processing']	[ 4.44143414e-01 3.15599889e-01 1.45287409e-01 -1.98393866e-01 -1.14587247e-01 -5.73217571e-01 1.23914289e+00 7.82286301e-02 2.28465840e-01 6.37764931e-01 9.23000574e-01 -1.48684829e-01 2.30449095e-01 -8.82123649e-01 -3.93052131e-01 -2.44714245e-01 5.67317426e-01 3.85464579e-01 -2.74743885e-01 -3.74697775e-01 7.28122652e-01 2.35507771e-01 -1.68048465e+00 7.57627070e-01 1.10022438e+00 3.68877560e-01 6.89612865e-01 7.60167480e-01 -8.56075227e-01 1.14714348e+00 -9.42215741e-01 -5.16706705e-01 -2.18186587e-01 -7.87992656e-01 -6.81548119e-01 4.11571264e-01 3.26546341e-01 -2.31808364e-01 -2.81346232e-01 7.70544231e-01 4.58124757e-01 -1.32251605e-02 6.97900295e-01 -1.15391493e+00 -1.24293864e+00 8.92931342e-01 -6.08921051e-01 -1.24683768e-01 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80d3bb52-c893-41f3-ba10-8aa7a0d4786c	unsupervised-domain-adaptation-for-plant	2009.01081	null	https://arxiv.org/abs/2009.01081v1	https://arxiv.org/pdf/2009.01081v1.pdf	Unsupervised Domain Adaptation For Plant Organ Counting	Supervised learning is often used to count objects in images, but for counting small, densely located objects, the required image annotations are burdensome to collect. Counting plant organs for image-based plant phenotyping falls within this category. Object counting in plant images is further challenged by having plant image datasets with significant domain shift due to different experimental conditions, e.g. applying an annotated dataset of indoor plant images for use on outdoor images, or on a different plant species. In this paper, we propose a domain-adversarial learning approach for domain adaptation of density map estimation for the purposes of object counting. The approach does not assume perfectly aligned distributions between the source and target datasets, which makes it more broadly applicable within general object counting and plant organ counting tasks. Evaluation on two diverse object counting tasks (wheat spikelets, leaves) demonstrates consistent performance on the target datasets across different classes of domain shift: from indoor-to-outdoor images and from species-to-species adaptation.	['Ian Stavness', 'Jordan Ubbens', 'Tewodros Ayalew']	2020-09-02	null	null	null	null	['plant-phenotyping', 'object-counting']	['computer-vision', 'computer-vision']	[ 6.15470588e-01 -3.93587708e-01 2.29459517e-02 -1.74461097e-01 -5.47802925e-01 -1.29980350e+00 3.76493961e-01 5.42685390e-01 -4.11678672e-01 7.21485913e-01 -6.51718378e-01 -3.99014890e-01 2.08409980e-01 -8.59358191e-01 -9.11237717e-01 -5.90668797e-01 1.87698796e-01 8.73999596e-01 2.99942702e-01 3.37298214e-01 9.10930559e-02 7.12167978e-01 -1.15392077e+00 -7.08905533e-02 6.40071869e-01 7.97463059e-01 7.30079591e-01 1.07457161e+00 -4.96316969e-01 6.05280161e-01 -9.63181496e-01 -3.03534657e-01 3.75727683e-01 -2.78820008e-01 -6.35878921e-01 4.47580993e-01 6.93128586e-01 -4.00297523e-01 1.86756909e-01 1.19188309e+00 3.07364613e-01 -2.77847797e-01 9.51056004e-01 -1.31499124e+00 -1.08469474e+00 2.69912541e-01 -8.97504866e-01 3.25068206e-01 1.39714070e-02 1.45720959e-01 4.59524930e-01 -5.89546382e-01 4.58900452e-01 9.46159065e-01 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a2f04094-f7f3-4218-9246-b437d979ce18	a-word-is-worth-a-thousand-dollars-1	null	null	https://openreview.net/forum?id=l_Wlug2cgDi	https://openreview.net/pdf?id=l_Wlug2cgDi	A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction	More and more investors and machine learning models rely on social media (e.g., Twitter and Reddit) to gather information and predict movements stock prices. Although text-based models are known to be vulnerable to adversarial attacks, whether stock prediction models have similar vulnerability given necessary constraints is underexplored. In this paper, we experiment with a variety of adversarial attack configurations to fool three stock prediction victim models. We address the task of adversarial generation by solving combinatorial optimization problems with semantics and budget constraints. Our results show that the proposed attack method can achieve consistent success rates and cause significant monetary loss in trading simulation by simply concatenating a perturbed but semantically similar tweet.	['Anonymous']	2022-01-16	null	null	null	acl-arr-january-2022-1	['stock-prediction']	['time-series']	[-3.54390353e-01 2.05348864e-01 -3.53663713e-02 -4.14142087e-02 -4.84738380e-01 -1.16022158e+00 9.14335191e-01 5.11343814e-02 -2.17688292e-01 9.67444718e-01 9.51511115e-02 -4.01354164e-01 3.48408312e-01 -1.33774543e+00 -7.04907060e-01 -1.80854559e-01 -4.59700346e-01 6.32568002e-01 3.56678933e-01 -6.58090711e-01 4.96997178e-01 4.66905743e-01 -9.70851481e-01 -2.02613957e-02 7.60100543e-01 1.00949156e+00 -6.76264942e-01 4.37102050e-01 -2.69002765e-01 1.22231865e+00 -1.10956812e+00 -1.23260713e+00 1.10861325e+00 -2.32571676e-01 -4.62529600e-01 -2.88241148e-01 2.77353555e-01 -6.81682050e-01 -3.15929890e-01 1.47964096e+00 5.20190299e-01 -1.51707843e-01 4.16963637e-01 -1.73249328e+00 -7.88101971e-01 1.32179177e+00 -3.46167803e-01 3.49808007e-01 1.93252102e-01 3.54088277e-01 1.10950899e+00 -4.42854732e-01 4.13647950e-01 1.09876060e+00 5.89761794e-01 3.44199449e-01 -1.15855920e+00 -1.27449071e+00 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60b56159-ca6e-46cb-bc4a-629b660057ad	towards-an-automatic-turing-test-learning-to	1708.07149	null	http://arxiv.org/abs/1708.07149v2	http://arxiv.org/pdf/1708.07149v2.pdf	Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses	Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automatic evaluation metrics are biased and correlate very poorly with human judgements of response quality. Yet having an accurate automatic evaluation procedure is crucial for dialogue research, as it allows rapid prototyping and testing of new models with fewer expensive human evaluations. In response to this challenge, we formulate automatic dialogue evaluation as a learning problem. We present an evaluation model (ADEM) that learns to predict human-like scores to input responses, using a new dataset of human response scores. We show that the ADEM model's predictions correlate significantly, and at a level much higher than word-overlap metrics such as BLEU, with human judgements at both the utterance and system-level. We also show that ADEM can generalize to evaluating dialogue models unseen during training, an important step for automatic dialogue evaluation.	['Nicolas Angelard-Gontier', 'Michael Noseworthy', 'Ryan Lowe', 'Yoshua Bengio', 'Joelle Pineau', 'Iulian V. Serban']	2017-08-23	towards-an-automatic-turing-test-learning-to-1	https://aclanthology.org/P17-1103	https://aclanthology.org/P17-1103.pdf	acl-2017-7	['dialogue-evaluation']	['natural-language-processing']	[ 2.41189420e-01 5.42324901e-01 1.51548445e-01 -8.63124073e-01 -1.11522222e+00 -8.20142031e-01 7.39910603e-01 4.72500026e-01 -6.00518763e-01 1.09303808e+00 6.22084320e-01 -3.12713891e-01 1.60816059e-01 -5.84702194e-01 9.56970528e-02 3.39499675e-02 2.70685732e-01 1.01243210e+00 1.78935498e-01 -7.84446836e-01 5.04785240e-01 2.42897519e-03 -1.07751667e+00 5.54623902e-01 8.40832531e-01 7.07163393e-01 -2.50302225e-01 1.43284667e+00 -1.04355931e-01 1.14895403e+00 -1.10493565e+00 -7.61329591e-01 -2.01478541e-01 -9.44418132e-01 -1.55235898e+00 -6.20946474e-02 1.28962636e-01 -4.82495487e-01 1.08981945e-01 7.53247678e-01 5.37422538e-01 4.32626128e-01 7.75296330e-01 -1.02649057e+00 -4.53805119e-01 5.42786419e-01 3.63563418e-01 -6.26614392e-02 1.01932991e+00 2.26952299e-01 1.33733690e+00 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3b6ca6c8-1386-4776-952e-1a9269b5c353	learning-word-embeddings-for-low-resource	null	null	https://aclanthology.org/N18-1093	https://aclanthology.org/N18-1093.pdf	Learning Word Embeddings for Low-Resource Languages by PU Learning	Word embedding is a key component in many downstream applications in processing natural languages. Existing approaches often assume the existence of a large collection of text for learning effective word embedding. However, such a corpus may not be available for some low-resource languages. In this paper, we study how to effectively learn a word embedding model on a corpus with only a few million tokens. In such a situation, the co-occurrence matrix is sparse as the co-occurrences of many word pairs are unobserved. In contrast to existing approaches often only sample a few unobserved word pairs as negative samples, we argue that the zero entries in the co-occurrence matrix also provide valuable information. We then design a Positive-Unlabeled Learning (PU-Learning) approach to factorize the co-occurrence matrix and validate the proposed approaches in four different languages.	['Kai-Wei Chang', 'Cho-Jui Hsieh', 'Hsiang-Fu Yu', 'Chao Jiang']	2018-06-01	null	null	null	naacl-2018-6	['learning-word-embeddings']	['methodology']	[-4.62344056e-03 1.52748972e-01 -6.52858198e-01 -2.36444652e-01 -8.56235385e-01 -5.42435288e-01 6.50882602e-01 5.03691137e-01 -8.36862624e-01 7.80734420e-01 5.06534517e-01 -6.06858015e-01 2.64454156e-01 -7.94310331e-01 -5.33502817e-01 -5.43348730e-01 -1.04370743e-01 3.49842846e-01 -6.97217211e-02 -1.98372722e-01 -1.43454811e-02 5.33505343e-03 -9.87302184e-01 5.29025681e-02 8.54801238e-01 1.77739397e-01 3.51598650e-01 4.34998572e-01 -7.89124370e-01 1.95983082e-01 -3.85913700e-01 -5.35201967e-01 5.06644309e-01 -2.06402406e-01 -4.18802440e-01 1.76013857e-02 3.96213867e-02 -1.38567880e-01 -1.82040676e-01 1.41816616e+00 4.14662480e-01 1.03591651e-01 7.53670037e-01 -1.08794856e+00 -7.58383334e-01 1.12437332e+00 -7.38233030e-01 4.18189019e-01 2.01019242e-01 4.25086431e-02 1.73817754e+00 -1.43659496e+00 6.17566168e-01 1.25072372e+00 3.52472574e-01 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ad403577-f001-4ef2-8786-955fae5ce3d4	kekulescope-improved-prediction-of-cancer	1811.09036	null	https://arxiv.org/abs/1811.09036v2	https://arxiv.org/pdf/1811.09036v2.pdf	KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images	The application of convolutional neural networks (ConvNets) to harness high-content screening images or 2D compound representations is gaining increasing attention in drug discovery. However, existing applications often require large data sets for training, or sophisticated pretraining schemes. Here, we show using 33 IC50 data sets from ChEMBL 23 that the in vitro activity of compounds on cancer cell lines and protein targets can be accurately predicted on a continuous scale from their Kekule structure representations alone by extending existing architectures, which were pretrained on unrelated image data sets. We show that the predictive power of the generated models is comparable to that of Random Forest (RF) models and fully-connected Deep Neural Networks trained on circular (Morgan) fingerprints. Notably, including additional fully-connected layers further increases the predictive power of the ConvNets by up to 10%. Analysis of the predictions generated by RF models and ConvNets shows that by simply averaging the output of the RF models and ConvNets we obtain significantly lower errors in prediction for multiple data sets, although the effect size is small, than those obtained with either model alone, indicating that the features extracted by the convolutional layers of the ConvNets provide complementary predictive signal to Morgan fingerprints. Lastly, we show that multi-task ConvNets trained on compound images permit to model COX isoform selectivity on a continuous scale with errors in prediction comparable to the uncertainty of the data. Overall, in this work we present a set of ConvNet architectures for the prediction of compound activity from their Kekule structure representations with state-of-the-art performance, that require no generation of compound descriptors or use of sophisticated image processing techniques.	['Isidro Cortes Ciriano', 'Andreas Bender']	2018-11-22	null	null	null	null	['prediction-of-cancer-cell-line-sensitivity']	['medical']	[ 7.89999783e-01 -2.95028742e-02 -3.84408444e-01 -1.99212208e-01 -9.17485952e-01 -8.38582397e-01 8.04959953e-01 3.74697536e-01 -6.20702922e-01 1.12400675e+00 -2.36654997e-01 -5.61688840e-01 -2.76938170e-01 -6.91679299e-01 -1.00238657e+00 -8.91216457e-01 -2.50531048e-01 4.67055053e-01 3.36254954e-01 -1.02456547e-01 1.99943364e-01 8.74764323e-01 -1.08667946e+00 7.35064328e-01 4.06294137e-01 1.32862234e+00 1.01285890e-01 6.15286827e-01 8.36449713e-02 6.31291926e-01 -5.19681692e-01 -1.50356531e-01 1.67052299e-01 -1.28107980e-01 -7.03576326e-01 -6.58549786e-01 7.38024950e-01 -2.10519373e-01 -1.18964359e-01 5.34221947e-01 7.14900434e-01 -2.63935655e-01 1.06871045e+00 -4.80039537e-01 -6.02585793e-01 2.46510684e-01 -2.48223513e-01 1.39165744e-01 3.84417385e-01 2.33092934e-01 8.77925396e-01 -8.89009595e-01 6.60688996e-01 8.12852263e-01 1.06592000e+00 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11a609df-acf8-4f47-bb46-8232ed31bcf7	bird-species-categorization-using-pose	1406.2952	null	http://arxiv.org/abs/1406.2952v1	http://arxiv.org/pdf/1406.2952v1.pdf	Bird Species Categorization Using Pose Normalized Deep Convolutional Nets	We propose an architecture for fine-grained visual categorization that approaches expert human performance in the classification of bird species. Our architecture first computes an estimate of the object's pose; this is used to compute local image features which are, in turn, used for classification. The features are computed by applying deep convolutional nets to image patches that are located and normalized by the pose. We perform an empirical study of a number of pose normalization schemes, including an investigation of higher order geometric warping functions. We propose a novel graph-based clustering algorithm for learning a compact pose normalization space. We perform a detailed investigation of state-of-the-art deep convolutional feature implementations and fine-tuning feature learning for fine-grained classification. We observe that a model that integrates lower-level feature layers with pose-normalized extraction routines and higher-level feature layers with unaligned image features works best. Our experiments advance state-of-the-art performance on bird species recognition, with a large improvement of correct classification rates over previous methods (75% vs. 55-65%).	['Grant van Horn', 'Serge Belongie', 'Steve Branson', 'Pietro Perona']	2014-06-11	null	null	null	null	['fine-grained-visual-categorization']	['computer-vision']	[-1.40126079e-01 -5.81583738e-01 -1.13844229e-02 -6.84206903e-01 -8.51869285e-02 -1.12667775e+00 7.70641267e-01 5.07782400e-01 -9.30719435e-01 -7.86147416e-02 2.10567072e-01 1.31149039e-01 -3.61545593e-01 -8.35295320e-01 -6.09323859e-01 -5.41632950e-01 -4.34716433e-01 3.84415537e-01 3.27410907e-01 -3.56357217e-01 5.13401628e-01 1.10452020e+00 -1.99020624e+00 3.05841386e-01 2.54497588e-01 1.19100547e+00 -3.13818187e-01 1.08613026e+00 2.02068403e-01 5.10625541e-01 -7.47871220e-01 -1.54136032e-01 5.03716588e-01 1.66260257e-01 -1.07232904e+00 -5.34395501e-02 1.21896267e+00 -3.96219879e-01 -1.84584837e-02 9.57352102e-01 2.02783003e-01 4.02991444e-01 1.10425961e+00 -1.15657389e+00 -6.23852134e-01 3.96745801e-01 -4.98410583e-01 5.90432584e-01 -7.42096603e-02 1.39544383e-01 1.07478774e+00 -5.25438070e-01 3.14129055e-01 1.17783189e+00 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67fceedf-f9dd-4583-a662-f67bea6c2a4e	multimodal-analysis-of-the-predictability-of	2108.05762	null	https://arxiv.org/abs/2108.05762v3	https://arxiv.org/pdf/2108.05762v3.pdf	Multimodal analysis of the predictability of hand-gesture properties	Embodied conversational agents benefit from being able to accompany their speech with gestures. Although many data-driven approaches to gesture generation have been proposed in recent years, it is still unclear whether such systems can consistently generate gestures that convey meaning. We investigate which gesture properties (phase, category, and semantics) can be predicted from speech text and/or audio using contemporary deep learning. In extensive experiments, we show that gesture properties related to gesture meaning (semantics and category) are predictable from text features (time-aligned FastText embeddings) alone, but not from prosodic audio features, while rhythm-related gesture properties (phase) on the other hand can be predicted from audio features better than from text. These results are encouraging as they indicate that it is possible to equip an embodied agent with content-wise meaningful co-speech gestures using a machine-learning model.	['Gustav Eje Henter', 'Hedvig Kjellström', 'Michael Neff', 'Rajmund Nagy', 'Taras Kucherenko']	2021-08-12	null	null	null	null	['gesture-generation']	['robots']	[ 3.68261307e-01 2.97675818e-01 -1.44456804e-01 -6.90363050e-01 -6.50314927e-01 -6.11588120e-01 1.36943758e+00 -1.10565342e-01 -4.61474597e-01 6.00901365e-01 1.09432149e+00 2.90373545e-02 4.64407913e-02 -4.36434090e-01 -3.10518861e-01 -8.10424805e-01 -4.70188022e-01 5.23260772e-01 -1.26108631e-01 -4.80785847e-01 1.90000892e-01 4.24048454e-01 -1.70123649e+00 4.76575762e-01 2.10839793e-01 6.44290924e-01 1.69160962e-01 1.11172509e+00 -4.08776343e-01 8.08562875e-01 -7.13468194e-01 2.57490337e-01 -5.28588705e-02 -9.61457372e-01 -9.37777638e-01 -2.03837790e-02 -4.79325987e-02 -5.77021658e-01 -1.89269230e-01 3.81147087e-01 3.79608959e-01 3.34415466e-01 1.06885326e+00 -1.32880771e+00 -5.01301169e-01 9.06391799e-01 1.28505260e-01 -3.83702934e-01 8.27294827e-01 3.28125745e-01 1.38047159e+00 -5.57715535e-01 9.19666469e-01 1.49579072e+00 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b5407982-1750-4c45-93fe-58304ee6bf49	a-categorized-reflection-removal-dataset-with	2108.0338	null	https://arxiv.org/abs/2108.03380v1	https://arxiv.org/pdf/2108.03380v1.pdf	A Categorized Reflection Removal Dataset with Diverse Real-world Scenes	Due to the lack of a large-scale reflection removal dataset with diverse real-world scenes, many existing reflection removal methods are trained on synthetic data plus a small amount of real-world data, which makes it difficult to evaluate the strengths or weaknesses of different reflection removal methods thoroughly. Furthermore, existing real-world benchmarks and datasets do not categorize image data based on the types and appearances of reflection (e.g., smoothness, intensity), making it hard to analyze reflection removal methods. Hence, we construct a new reflection removal dataset that is categorized, diverse, and real-world (CDR). A pipeline based on RAW data is used to capture perfectly aligned input images and transmission images. The dataset is constructed using diverse glass types under various environments to ensure diversity. By analyzing several reflection removal methods and conducting extensive experiments on our dataset, we show that state-of-the-art reflection removal methods generally perform well on blurry reflection but fail in obtaining satisfying performance on other types of real-world reflection. We believe our dataset can help develop novel methods to remove real-world reflection better. Our dataset is available at https://alexzhao-hugga.github.io/Real-World-Reflection-Removal/.	['Qifeng Chen', 'Qiong Yan', 'Wenxiu Sun', 'Yankun Zhao', 'Chenyang Qi', 'Xuhua Huang', 'Chenyang Lei']	2021-08-07	null	null	null	null	['reflection-removal']	['computer-vision']	[ 4.17715758e-01 -5.59399784e-01 4.88171279e-01 -1.03610598e-01 -6.39841199e-01 -2.85615861e-01 4.68658537e-01 -6.32914841e-01 7.08016679e-02 4.34751630e-01 5.36828279e-01 -2.09231645e-01 1.88543975e-01 -7.78551698e-01 -5.42035043e-01 -9.03823495e-01 2.37729326e-01 -3.00476998e-01 4.26967412e-01 -5.35179496e-01 3.65646392e-01 2.93359429e-01 -1.54881370e+00 4.79590386e-01 1.08459592e+00 5.83833456e-01 2.60860503e-01 5.84984303e-01 3.22857201e-01 6.28567874e-01 -8.23545933e-01 -6.99187666e-02 8.59869003e-01 -6.82120979e-01 -1.96572155e-01 4.10010032e-02 7.11929500e-01 -5.19546449e-01 -5.43704987e-01 1.05420554e+00 7.31397748e-01 4.62555587e-02 4.83295381e-01 -8.33819926e-01 -1.10518670e+00 1.87780876e-02 -8.82630408e-01 7.87933692e-02 5.94970107e-01 6.03325784e-01 5.97039580e-01 -7.12098956e-01 4.58643407e-01 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2143956c-3c37-4831-beef-107f5b8c5da2	deep-aggregation-of-regional-convolutional	1909.0942	null	https://arxiv.org/abs/1909.09420v2	https://arxiv.org/pdf/1909.09420v2.pdf	Deep Aggregation of Regional Convolutional Activations for Content Based Image Retrieval	One of the key challenges of deep learning based image retrieval remains in aggregating convolutional activations into one highly representative feature vector. Ideally, this descriptor should encode semantic, spatial and low level information. Even though off-the-shelf pre-trained neural networks can already produce good representations in combination with aggregation methods, appropriate fine tuning for the task of image retrieval has shown to significantly boost retrieval performance. In this paper, we present a simple yet effective supervised aggregation method built on top of existing regional pooling approaches. In addition to the maximum activation of a given region, we calculate regional average activations of extracted feature maps. Subsequently, weights for each of the pooled feature vectors are learned to perform a weighted aggregation to a single feature vector. Furthermore, we apply our newly proposed NRA loss function for deep metric learning to fine tune the backbone neural network and to learn the aggregation weights. Our method achieves state-of-the-art results for the INRIA Holidays data set and competitive results for the Oxford Buildings and Paris data sets while reducing the training time significantly.	['Konstantin Schall', 'Nico Hezel', 'Kai Uwe Barthel', 'Klaus Jung']	2019-09-20	null	null	null	null	['content-based-image-retrieval']	['computer-vision']	[-5.13405427e-02 -4.28943515e-01 -2.78682075e-02 -6.31336033e-01 -1.46890712e+00 -3.58306915e-01 8.17618370e-01 5.09890616e-01 -7.91844785e-01 5.23734510e-01 1.30811691e-01 3.71836483e-01 -3.51683646e-01 -9.26450193e-01 -7.13611722e-01 -7.56345212e-01 -1.92791641e-01 2.82501519e-01 1.79969355e-01 -2.98959047e-01 2.13277534e-01 9.12607014e-01 -1.72723699e+00 6.16714120e-01 6.09995365e-01 1.65449905e+00 9.19082165e-02 5.29353380e-01 4.09858376e-02 6.97497725e-01 -7.68290102e-01 -1.10553280e-01 3.34496766e-01 -1.50930122e-01 -7.77572274e-01 -1.32193133e-01 7.18802869e-01 -4.40818489e-01 -3.51028144e-01 5.96122324e-01 6.55222774e-01 4.58310485e-01 6.81574881e-01 -8.88440192e-01 -7.42188811e-01 1.75070107e-01 -3.12344968e-01 1.63782716e-01 5.80473393e-02 -2.16986492e-01 1.40800667e+00 -1.06753004e+00 6.40510023e-01 9.24194336e-01 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9be43d4a-66bb-481f-a70b-0e28b282904b	meta-modeling-game-for-deriving-theoretical	1810.10535	null	http://arxiv.org/abs/1810.10535v1	http://arxiv.org/pdf/1810.10535v1.pdf	Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learning	This paper presents a new meta-modeling framework to employ deep reinforcement learning (DRL) to generate mechanical constitutive models for interfaces. The constitutive models are conceptualized as information flow in directed graphs. The process of writing constitutive models are simplified as a sequence of forming graph edges with the goal of maximizing the model score (a function of accuracy, robustness and forward prediction quality). Thus meta-modeling can be formulated as a Markov decision process with well-defined states, actions, rules, objective functions, and rewards. By using neural networks to estimate policies and state values, the computer agent is able to efficiently self-improve the constitutive model it generated through self-playing, in the same way AlphaGo Zero (the algorithm that outplayed the world champion in the game of Go)improves its gameplay. Our numerical examples show that this automated meta-modeling framework not only produces models which outperform existing cohesive models on benchmark traction-separation data but is also capable of detecting hidden mechanisms among micro-structural features and incorporating them in constitutive models to improve the forward prediction accuracy, which are difficult tasks to do manually.	['WaiChing Sun', 'Kun Wang']	2018-10-24	null	null	null	null	['game-of-go']	['playing-games']	[ 1.03339382e-01 6.05513692e-01 -4.72748168e-02 2.73943275e-01 -4.71263319e-01 -5.02026640e-02 4.74760085e-01 1.52699694e-01 -9.09283385e-02 1.04096949e+00 -2.47633398e-01 -1.69578210e-01 -5.58983028e-01 -1.18282807e+00 -9.53845799e-01 -8.28572094e-01 -2.28698120e-01 8.52090657e-01 3.11097145e-01 -6.75007045e-01 4.39083040e-01 3.45000535e-01 -1.54549682e+00 9.57653895e-02 8.94383430e-01 6.40811205e-01 1.00367203e-01 8.59982789e-01 4.37798917e-01 1.13251591e+00 -2.49053806e-01 -2.42754787e-01 -1.54485077e-01 -5.10952413e-01 -9.96783257e-01 -3.98366079e-02 -3.12050402e-01 5.30701987e-02 -2.13737145e-01 7.95722544e-01 2.58893281e-01 1.53865382e-01 9.16262746e-01 -1.24282539e+00 -4.01212633e-01 7.35723019e-01 -2.31759906e-01 -2.70701885e-01 3.57438564e-01 3.70976985e-01 1.07822585e+00 -3.25297654e-01 8.85191023e-01 1.06393600e+00 5.75943947e-01 6.34022236e-01 -1.37741673e+00 -2.18792573e-01 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a1691b32-bcce-4681-ace5-3c21ff840d39	maskfusion-real-time-recognition-tracking-and	1804.09194	null	http://arxiv.org/abs/1804.09194v2	http://arxiv.org/pdf/1804.09194v2.pdf	MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects	We present MaskFusion, a real-time, object-aware, semantic and dynamic RGB-D SLAM system that goes beyond traditional systems which output a purely geometric map of a static scene. MaskFusion recognizes, segments and assigns semantic class labels to different objects in the scene, while tracking and reconstructing them even when they move independently from the camera. As an RGB-D camera scans a cluttered scene, image-based instance-level semantic segmentation creates semantic object masks that enable real-time object recognition and the creation of an object-level representation for the world map. Unlike previous recognition-based SLAM systems, MaskFusion does not require known models of the objects it can recognize, and can deal with multiple independent motions. MaskFusion takes full advantage of using instance-level semantic segmentation to enable semantic labels to be fused into an object-aware map, unlike recent semantics enabled SLAM systems that perform voxel-level semantic segmentation. We show augmented-reality applications that demonstrate the unique features of the map output by MaskFusion: instance-aware, semantic and dynamic.	['Martin Rünz', 'Maud Buffier', 'Lourdes Agapito']	2018-04-24	null	null	null	null	['object-slam', 'semantic-slam']	['computer-vision', 'computer-vision']	[ 4.23204213e-01 2.74620801e-01 -9.52632225e-04 -6.30192280e-01 -6.61144197e-01 -8.32489729e-01 4.89945650e-01 3.43281105e-02 -2.20829666e-01 2.87904650e-01 -1.67676106e-01 -4.86660749e-02 4.64312918e-02 -7.99510479e-01 -8.61703098e-01 -2.21341774e-01 4.56566960e-02 1.29880250e+00 1.03577673e+00 2.18479671e-02 1.51526749e-01 8.24599028e-01 -1.83396804e+00 1.32272750e-01 3.37177753e-01 1.03865099e+00 7.49350011e-01 8.31800163e-01 -5.10344028e-01 5.82090199e-01 -4.49354887e-01 3.43626887e-01 5.27733147e-01 -1.84543028e-01 -8.74378026e-01 4.57589984e-01 7.74681449e-01 -1.16182424e-01 -3.54489177e-01 1.11163318e+00 -2.34335065e-01 2.47014582e-01 6.46350980e-02 -1.54443419e+00 -1.04959488e-01 -3.31795961e-02 -1.52057424e-01 -2.06623375e-01 8.78371835e-01 2.25373715e-01 4.78930205e-01 -6.99598491e-01 1.07805169e+00 1.34997404e+00 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e9355ae4-4f41-4398-ab3e-195daab246ac	activity-recognition-using-st-gcn-with-3d	null	null	https://doi.org/10.1145/3341162.3345581	http://delivery.acm.org/10.1145/3350000/3345581/p689-cao.pdf	Activity recognition using ST-GCN with 3D motion data	For the Nurse Care Activity Recognition Challenge, an activity recognition algorithm was developed by Team TDU-DSML. A spatial-temporal graph convolutional network (ST-GCN) was applied to process 3D motion capture data included in the challenge dataset. Time-series data was divided into 20-second segments with a 10-second overlap. The recognition model with a tree-structure graph was then created. The prediction result was set to one-minute segments on the basis of a majority decision from each segment output. Our model was evaluated by using leave-one-subject-out cross-validation methods. An average accuracy of 57% for all six subjects was achieved.	['Xin Cao', 'Masaki Shuzo', 'Wataru Kudo', 'Chihiro Ito', 'Eisaku Maeda']	2019-09-13	null	null	null	ubicompiswc-19-adjunct-2019-9	['multimodal-activity-recognition']	['computer-vision']	[ 4.67517942e-01 3.68760437e-01 -2.81658679e-01 -5.04913807e-01 -7.66807437e-01 -6.93601444e-02 3.02907117e-02 3.68728667e-01 -3.94965678e-01 4.41256195e-01 5.20641804e-01 -3.87428880e-01 -2.95943081e-01 -4.31309521e-01 -3.67070407e-01 -3.08798075e-01 -6.24655366e-01 1.92001104e-01 9.15334672e-02 2.67246306e-01 -1.96556181e-01 5.72094321e-01 -9.65075672e-01 7.65550256e-01 4.77322400e-01 1.07736194e+00 -6.89184666e-02 1.05739820e+00 2.79179603e-01 1.40961254e+00 -4.57575798e-01 3.79759997e-01 3.28234702e-01 -6.54797852e-01 -6.40304208e-01 2.05375627e-01 1.99184835e-01 -1.62840053e-01 -6.89945638e-01 1.31846458e-01 7.27680385e-01 3.79421234e-01 3.15977335e-01 -1.02968502e+00 1.32754326e-01 2.28471398e-01 1.00954644e-01 6.15199983e-01 6.58733606e-01 4.08787608e-01 4.20635849e-01 -7.85625160e-01 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1.10406645e-01 -1.26300323e+00 -2.91335821e-01 3.30326229e-01 3.15625340e-01 -2.07078263e-01 -6.47833347e-02 1.82433292e-01]	[14.046417236328125, -3.377955436706543]
3cb546b8-72c9-4677-8cd1-23c1c7d4b043	using-web-co-occurrence-statistics-for	1312.5697	null	http://arxiv.org/abs/1312.5697v2	http://arxiv.org/pdf/1312.5697v2.pdf	Using Web Co-occurrence Statistics for Improving Image Categorization	Object recognition and localization are important tasks in computer vision. The focus of this work is the incorporation of contextual information in order to improve object recognition and localization. For instance, it is natural to expect not to see an elephant to appear in the middle of an ocean. We consider a simple approach to encapsulate such common sense knowledge using co-occurrence statistics from web documents. By merely counting the number of times nouns (such as elephants, sharks, oceans, etc.) co-occur in web documents, we obtain a good estimate of expected co-occurrences in visual data. We then cast the problem of combining textual co-occurrence statistics with the predictions of image-based classifiers as an optimization problem. The resulting optimization problem serves as a surrogate for our inference procedure. Albeit the simplicity of the resulting optimization problem, it is effective in improving both recognition and localization accuracy. Concretely, we observe significant improvements in recognition and localization rates for both ImageNet Detection 2012 and Sun 2012 datasets.	['Dumitru Erhan', 'Samy Bengio', 'Jonathon Shlens', 'Eugene Ie', 'Yoram Singer', 'Quoc Le', 'Jeff Dean', 'Andrew Rabinovich']	2013-12-19	null	null	null	null	['image-categorization']	['computer-vision']	[ 2.04664871e-01 -3.63177210e-01 -3.85684706e-03 -3.88336599e-01 -7.44365513e-01 -7.80574441e-01 1.06846964e+00 4.34986740e-01 -8.09527397e-01 4.90778565e-01 2.57915020e-01 -4.02944535e-01 1.20621547e-01 -7.44451046e-01 -1.13396049e+00 -6.85014606e-01 1.55567884e-01 3.20259690e-01 7.88662955e-02 1.95447996e-01 4.27964091e-01 5.20326674e-01 -1.66675794e+00 3.85364920e-01 4.83239979e-01 1.10149515e+00 5.47683835e-01 7.11330175e-01 -2.87642814e-02 6.67334616e-01 -5.56128204e-01 -4.25800622e-01 1.03500269e-01 -1.43756941e-01 -7.62869060e-01 5.71344435e-01 9.04367208e-01 -2.19832547e-02 -7.07386732e-02 1.18308854e+00 1.31956518e-01 7.86226466e-02 1.05866742e+00 -8.63517404e-01 -5.12338102e-01 5.99492848e-01 -7.98130572e-01 2.97942132e-01 1.93276867e-01 2.13380326e-02 1.43142068e+00 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a56e4b07-3b2a-4400-90a8-33815f2715d0	learning-expressive-prompting-with-residuals	2303.15591	null	https://arxiv.org/abs/2303.15591v1	https://arxiv.org/pdf/2303.15591v1.pdf	Learning Expressive Prompting With Residuals for Vision Transformers	Prompt learning is an efficient approach to adapt transformers by inserting learnable set of parameters into the input and intermediate representations of a pre-trained model. In this work, we present Expressive Prompts with Residuals (EXPRES) which modifies the prompt learning paradigm specifically for effective adaptation of vision transformers (ViT). Out method constructs downstream representations via learnable ``output'' tokens, that are akin to the learned class tokens of the ViT. Further for better steering of the downstream representation processed by the frozen transformer, we introduce residual learnable tokens that are added to the output of various computations. We apply EXPRES for image classification, few shot learning, and semantic segmentation, and show our method is capable of achieving state of the art prompt tuning on 3/3 categories of the VTAB benchmark. In addition to strong performance, we observe that our approach is an order of magnitude more prompt efficient than existing visual prompting baselines. We analytically show the computational benefits of our approach over weight space adaptation techniques like finetuning. Lastly we systematically corroborate the architectural design of our method via a series of ablation experiments.	['Ashwin Swaminathan', 'Avinash Ravichandran', 'Yonatan Dukler', 'Rajshekhar Das']	2023-03-27	null	http://openaccess.thecvf.com//content/CVPR2023/html/Das_Learning_Expressive_Prompting_With_Residuals_for_Vision_Transformers_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Das_Learning_Expressive_Prompting_With_Residuals_for_Vision_Transformers_CVPR_2023_paper.pdf	cvpr-2023-1	['visual-prompting']	['computer-vision']	[ 4.41516489e-01 4.18864906e-01 3.37817520e-02 -2.58777022e-01 -7.76139796e-01 -7.18530774e-01 9.56749260e-01 -1.31655991e-01 -5.04007399e-01 3.03188711e-01 5.22804797e-01 -3.06316942e-01 8.76994357e-02 -5.14230728e-01 -1.08133912e+00 -7.19270349e-01 3.36849302e-01 2.25462288e-01 6.43360078e-01 -2.80310690e-01 2.89648950e-01 3.66337925e-01 -1.56280315e+00 4.89602029e-01 5.15321136e-01 1.28260255e+00 1.33060366e-01 8.12386394e-01 -1.56621620e-01 1.14402187e+00 -4.08067226e-01 -3.33396614e-01 3.37523401e-01 -2.38543689e-01 -9.85370874e-01 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