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4b5e8f67-75e0-4e4d-a9cc-c0451110a9ed	learn2augment-learning-to-composite-videos	2206.04790	null	https://arxiv.org/abs/2206.04790v2	https://arxiv.org/pdf/2206.04790v2.pdf	Learn2Augment: Learning to Composite Videos for Data Augmentation in Action Recognition	We address the problem of data augmentation for video action recognition. Standard augmentation strategies in video are hand-designed and sample the space of possible augmented data points either at random, without knowing which augmented points will be better, or through heuristics. We propose to learn what makes a good video for action recognition and select only high-quality samples for augmentation. In particular, we choose video compositing of a foreground and a background video as the data augmentation process, which results in diverse and realistic new samples. We learn which pairs of videos to augment without having to actually composite them. This reduces the space of possible augmentations, which has two advantages: it saves computational cost and increases the accuracy of the final trained classifier, as the augmented pairs are of higher quality than average. We present experimental results on the entire spectrum of training settings: few-shot, semi-supervised and fully supervised. We observe consistent improvements across all of them over prior work and baselines on Kinetics, UCF101, HMDB51, and achieve a new state-of-the-art on settings with limited data. We see improvements of up to 8.6% in the semi-supervised setting.	['Laura Sevilla-Lara', 'Frank Keller', 'Marcus Rohrbach', 'Shreyank N Gowda']	2022-06-09	null	null	null	null	['few-shot-action-recognition']	['computer-vision']	[ 5.4149556e-01 -1.7467637e-01 -6.8537390e-01 -1.7081904e-01 -9.8809123e-01 -4.3996689e-01 6.0459429e-01 -2.4880260e-01 -6.7478567e-01 7.3457146e-01 4.7351927e-01 -1.8838020e-02 2.7290317e-01 -4.1934505e-01 -9.4000077e-01 -7.3894757e-01 -6.3031107e-02 6.6914946e-01 3.0385110e-01 5.9861176e-02 -5.2533474e-02 1.9502267e-01 -1.7382801e+00 7.0796919e-01 5.3818381e-01 1.0255917e+00 -2.2892360e-02 8.2751274e-01 1.2427254e-01 1.0742204e+00 -4.4416565e-01 -3.8641679e-01 6.8365061e-01 -4.4931218e-01 -9.7676259e-01 9.9864489e-01 7.2901326e-01 -8.4335041e-01 -6.3761908e-01 6.8250775e-01 7.9665408e-02 3.1433985e-01 5.1377219e-01 -1.4957714e+00 -3.7571552e-01 5.3041118e-01 -7.3027593e-01 4.0684631e-01 3.7368718e-01 5.0205213e-01 9.4470727e-01 -9.1563225e-01 7.5706685e-01 1.1112223e+00 2.7703428e-01 8.9902616e-01 -1.1533421e+00 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330ca883-297d-42fc-9f99-20c69050cea6	hand-based-person-identification-using-global	2101.05260	null	https://arxiv.org/abs/2101.05260v8	https://arxiv.org/pdf/2101.05260v8.pdf	Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learning	In cases of serious crime, including sexual abuse, often the only available information with demonstrated potential for identification is images of the hands. Since this evidence is captured in uncontrolled situations, it is difficult to analyse. As global approaches to feature comparison are limited in this case, it is important to extend to consider local information. In this work, we propose hand-based person identification by learning both global and local deep feature representations. Our proposed method, Global and Part-Aware Network (GPA-Net), creates global and local branches on the conv-layer for learning robust discriminative global and part-level features. For learning the local (part-level) features, we perform uniform partitioning on the conv-layer in both horizontal and vertical directions. We retrieve the parts by conducting a soft partition without explicitly partitioning the images or requiring external cues such as pose estimation. We make extensive evaluations on two large multi-ethnic and publicly available hand datasets, demonstrating that our proposed method significantly outperforms competing approaches.	['Plamen Angelov', 'Bryan Williams', 'Sue Black', 'Hossein Rahmani', 'Nathanael L. Baisa']	2021-01-13	null	null	null	null	['person-identification']	['computer-vision']	[ 1.17952287e-01 -2.69183904e-01 -2.37822562e-01 -6.32453799e-01 -8.81235242e-01 -6.87765121e-01 4.55617785e-01 1.88512295e-01 -6.60069466e-01 7.09126830e-01 2.38244295e-01 1.50629389e-03 -4.29436624e-01 -6.23253047e-01 -3.74729842e-01 -7.86154449e-01 -8.76884833e-02 7.31479347e-01 -1.71021327e-01 2.15918094e-01 3.77412111e-01 1.05199313e+00 -1.36328816e+00 2.03167960e-01 4.92156804e-01 9.38979268e-01 -2.88092077e-01 5.57367146e-01 3.57485592e-01 2.02536196e-01 -6.02863550e-01 -6.98203444e-01 5.58026135e-01 -1.47478089e-01 -1.02542436e+00 1.62769571e-01 8.09711218e-01 -9.41195071e-01 -1.28772646e-01 7.91618288e-01 1.06927073e+00 -1.10738818e-02 7.73993731e-01 -9.68545437e-01 -2.29401529e-01 3.72070938e-01 -9.54184234e-01 2.15185881e-01 4.77682978e-01 2.03258649e-01 8.32274616e-01 -7.44897604e-01 7.76764989e-01 1.38878846e+00 9.88515556e-01 3.75709802e-01 -1.36422944e+00 -7.81564355e-01 9.70335454e-02 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-9.55918074e-01 -2.99605243e-02 -1.98563278e-01]	[13.672229766845703, 0.9077821969985962]
6619372a-3d0a-4ef9-8cdc-56dfcf39e077	rapid-object-detection-using-a-boosted	null	null	https://ieeexplore.ieee.org/document/990517	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=990517	Rapid Object Detection using a Boosted Cascade of Simple Features	This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the “Integral linage” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely eflcient class@ers[5]. The third contribution is a method for combining increasingly more complex classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specijic focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest, In the domain of face detection the system yields detection rates comparable to the best previous sys- tems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differenc- ing or skin color detection.	['Michael Jones', 'Paul Viola']	2003-04-15	null	null	null	cvpr-2003-4	['face-detection']	['computer-vision']	[ 1.00006707e-01 -1.04056425e-01 -1.26519218e-01 -2.88962811e-01 -5.48354030e-01 -3.75260651e-01 4.38984394e-01 2.60067642e-01 -6.38116479e-01 2.79904366e-01 -5.06157279e-01 -1.80060431e-01 3.72958243e-01 -5.27564883e-01 -3.38845700e-01 -6.86692536e-01 -1.31831273e-01 3.26388240e-01 9.29377556e-01 -1.31834626e-01 3.32549989e-01 1.11191821e+00 -1.93232679e+00 5.83524764e-01 3.85874033e-01 1.10321760e+00 3.81651781e-02 1.14756215e+00 -1.39044255e-01 7.93381870e-01 -6.76391065e-01 -1.25223324e-01 3.32582653e-01 -5.28383315e-01 -7.26407290e-01 5.71031451e-01 8.17506850e-01 -4.27678823e-01 1.70863986e-01 1.06112742e+00 5.65967858e-01 -9.71428081e-02 8.37472916e-01 -1.18811965e+00 2.73118708e-02 -2.12970942e-01 -7.62659132e-01 7.78706908e-01 5.10556519e-01 9.15549621e-02 6.87080920e-01 -1.16229284e+00 4.78547931e-01 1.55570638e+00 5.87239981e-01 7.42091060e-01 -1.41091013e+00 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796647b7-444c-49f0-aa63-3c6ed0478c6a	image-captioners-are-scalable-vision-learners	2306.07915	null	https://arxiv.org/abs/2306.07915v1	https://arxiv.org/pdf/2306.07915v1.pdf	Image Captioners Are Scalable Vision Learners Too	Contrastive pretraining on image-text pairs from the web is one of the most popular large-scale pretraining strategies for vision backbones, especially in the context of large multimodal models. At the same time, image captioning on this type of data is commonly considered an inferior pretraining strategy. In this paper, we perform a fair comparison of these two pretraining strategies, carefully matching training data, compute, and model capacity. Using a standard encoder-decoder transformer, we find that captioning alone is surprisingly effective: on classification tasks, captioning produces vision encoders competitive with contrastively pretrained encoders, while surpassing them on vision & language tasks. We further analyze the effect of the model architecture and scale, as well as the pretraining data on the representation quality, and find that captioning exhibits the same or better scaling behavior along these axes. Overall our results show that plain image captioning is a more powerful pretraining strategy than was previously believed.	['Lucas Beyer', 'Neil Houlsby', 'Xiaohua Zhai', 'Andreas Steiner', 'Manoj Kumar', 'Michael Tschannen']	2023-06-13	null	null	null	null	['image-captioning']	['computer-vision']	[ 4.23735738e-01 3.01005810e-01 -1.60543516e-01 -3.43875200e-01 -1.03892624e+00 -6.10094130e-01 9.76510167e-01 1.07305348e-01 -7.37719834e-01 5.50012529e-01 4.01464611e-01 -2.89536983e-01 3.24508816e-01 -3.99330854e-01 -1.16998804e+00 -4.31682289e-01 3.60623717e-01 6.65353596e-01 5.71133532e-02 -3.21633428e-01 1.57673642e-01 7.68681169e-02 -1.60244334e+00 5.46332598e-01 6.73092961e-01 9.72621083e-01 4.36705351e-01 9.07675862e-01 -1.41138241e-01 8.15156102e-01 -3.41736466e-01 -1.03416216e+00 6.62025660e-02 -4.15244967e-01 -8.72821867e-01 1.41452283e-01 1.14999902e+00 -3.98790181e-01 -4.43413973e-01 8.75408530e-01 4.42349702e-01 -3.90970767e-01 7.05079257e-01 -1.16535997e+00 -1.07538152e+00 7.33496428e-01 -5.66656172e-01 1.39080718e-01 2.73596048e-01 3.02215159e-01 1.20328271e+00 -9.22915518e-01 6.33206308e-01 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-8.36363971e-01 -6.41435504e-01 -9.43856716e-01 -2.60103177e-02 6.06955409e-01 2.80415714e-01 -4.07985598e-01 -1.99960172e-01 3.30564082e-01]	[10.907065391540527, 1.520050287246704]
7ba851a3-09ff-491d-b7f8-6012e0fdb86f	190503692	1905.03692	null	https://arxiv.org/abs/1905.03692v2	https://arxiv.org/pdf/1905.03692v2.pdf	Improving Image-Based Localization with Deep Learning: The Impact of the Loss Function	This work investigates the impact of the loss function on the performance of Neural Networks, in the context of a monocular, RGB-only, image localization task. A common technique used when regressing a camera's pose from an image is to formulate the loss as a linear combination of positional and rotational mean squared error (using tuned hyperparameters as coefficients). In this work we observe that changes to rotation and position mutually affect the captured image, and in order to improve performance, a pose regression network's loss function should include a term which combines the error of both of these coupled quantities. Based on task specific observations and experimental tuning, we present said loss term, and create a new model by appending this loss term to the loss function of the pre-existing pose regression network `PoseNet'. We achieve improvements in the localization accuracy of the network for indoor scenes; with decreases of up to 26.7% and 24.0% in the median positional and rotational error respectively, when compared to the default PoseNet.	['Mohammed Bennamoun', 'M. A. Asim K. Jalwana', 'Isaac Ronald Ward']	2019-04-28	null	null	null	null	['image-based-localization']	['computer-vision']	[ 2.73716658e-01 -2.01695524e-02 2.03539282e-01 -6.51418686e-01 -6.74706876e-01 -5.71941257e-01 6.05932474e-01 -2.46008276e-03 -9.78566885e-01 6.42772615e-01 -4.81569879e-02 -6.59411326e-02 -3.15715335e-02 -3.71827275e-01 -1.15708542e+00 -7.33820438e-01 5.42278700e-02 -3.84675525e-02 1.50128573e-01 7.86982104e-02 9.06031653e-02 7.09503293e-01 -1.19852412e+00 -3.29286993e-01 3.57418656e-01 1.34372473e+00 9.56481844e-02 6.17765903e-01 5.58395982e-01 7.41686225e-01 -6.71517134e-01 -1.50564343e-01 3.25207442e-01 4.63525653e-02 -4.79204059e-01 -1.06067009e-01 8.61611247e-01 -1.24308228e-01 -3.62121135e-01 7.85863936e-01 6.63195133e-01 3.13932240e-01 4.31433201e-01 -9.32685375e-01 -1.28412887e-01 2.31305823e-01 -3.65093470e-01 1.01812966e-01 4.15785789e-01 -4.98368740e-02 7.57873416e-01 -7.56828785e-01 6.36064589e-01 9.24899578e-01 1.13379216e+00 1.26508698e-01 -1.50685632e+00 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2.21532643e-01 5.47157049e-01 -6.72265291e-01 -4.36114669e-01 -2.17804983e-01]	[7.67065954208374, -2.1096909046173096]
d677efd0-7a76-472a-8540-7cdcc86a8e9c	unsupervised-speech-enhancement-with-deep	2306.07820	null	https://arxiv.org/abs/2306.07820v1	https://arxiv.org/pdf/2306.07820v1.pdf	Unsupervised speech enhancement with deep dynamical generative speech and noise models	This work builds on a previous work on unsupervised speech enhancement using a dynamical variational autoencoder (DVAE) as the clean speech model and non-negative matrix factorization (NMF) as the noise model. We propose to replace the NMF noise model with a deep dynamical generative model (DDGM) depending either on the DVAE latent variables, or on the noisy observations, or on both. This DDGM can be trained in three configurations: noise-agnostic, noise-dependent and noise adaptation after noise-dependent training. Experimental results show that the proposed method achieves competitive performance compared to state-of-the-art unsupervised speech enhancement methods, while the noise-dependent training configuration yields a much more time-efficient inference process.	['Xavier Alameda-Pineda', 'Laurent Girin', 'Simon Leglaive', 'Xiaoyu Lin']	2023-06-13	null	null	null	null	['speech-enhancement']	['speech']	[ 3.13412882e-02 -7.03780726e-02 4.08224583e-01 -4.63440530e-02 -4.97655690e-01 -1.49254426e-01 8.49974155e-01 -4.92018342e-01 -5.70777357e-01 4.91101980e-01 6.56056404e-01 -3.52360427e-01 -7.90691748e-02 -7.37431169e-01 -4.47883755e-01 -1.22027647e+00 5.18975496e-01 2.17589572e-01 1.15618326e-01 -4.85149592e-01 -3.79458219e-01 3.09113473e-01 -1.69573760e+00 -5.75747415e-02 8.09250414e-01 5.26783645e-01 6.00979507e-01 1.05054140e+00 -1.86498076e-01 7.59491086e-01 -6.07774138e-01 -3.51995856e-01 1.18199274e-01 -5.34828305e-01 -3.91806960e-01 3.49177957e-01 -8.39145929e-02 -9.72380415e-02 -8.18869650e-01 1.30860257e+00 8.77499521e-01 8.70094180e-01 5.68140805e-01 -7.75497496e-01 -9.33417976e-01 5.54925144e-01 -1.47953257e-01 3.63652587e-01 -3.55527103e-01 -1.20834783e-02 4.60746169e-01 -9.89424109e-01 6.70585275e-01 1.21744776e+00 6.68853641e-01 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-1.34617543e+00 -6.23333871e-01 3.37767988e-01 5.03208101e-01 -6.85960531e-01 -7.40553200e-01 1.73478007e-01]	[15.028271675109863, 6.041990756988525]
613569a3-3278-415f-9ad4-07f2d8073b5f	inductive-visual-localisation-factorised	1807.08179	null	http://arxiv.org/abs/1807.08179v1	http://arxiv.org/pdf/1807.08179v1.pdf	Inductive Visual Localisation: Factorised Training for Superior Generalisation	End-to-end trained Recurrent Neural Networks (RNNs) have been successfully applied to numerous problems that require processing sequences, such as image captioning, machine translation, and text recognition. However, RNNs often struggle to generalise to sequences longer than the ones encountered during training. In this work, we propose to optimise neural networks explicitly for induction. The idea is to first decompose the problem in a sequence of inductive steps and then to explicitly train the RNN to reproduce such steps. Generalisation is achieved as the RNN is not allowed to learn an arbitrary internal state; instead, it is tasked with mimicking the evolution of a valid state. In particular, the state is restricted to a spatial memory map that tracks parts of the input image which have been accounted for in previous steps. The RNN is trained for single inductive steps, where it produces updates to the memory in addition to the desired output. We evaluate our method on two different visual recognition problems involving visual sequences: (1) text spotting, i.e. joint localisation and reading of text in images containing multiple lines (or a block) of text, and (2) sequential counting of objects in aerial images. We show that inductive training of recurrent models enhances their generalisation ability on challenging image datasets.	['Andrew Zisserman', 'Andrea Vedaldi', 'Ankush Gupta']	2018-07-21	null	null	null	null	['text-spotting']	['computer-vision']	[ 1.27010489e+00 4.55187410e-02 1.11769356e-01 -2.24709988e-01 -2.36216113e-01 -4.99288917e-01 1.02063465e+00 -1.49175361e-01 -6.37036085e-01 6.17817521e-01 -8.27796757e-02 -4.93250817e-01 2.68623978e-01 -4.57450837e-01 -1.12014043e+00 -8.97911072e-01 2.22672537e-01 5.18736959e-01 1.13319837e-01 4.50139027e-03 3.04439843e-01 4.70667452e-01 -1.44810462e+00 4.22425956e-01 3.98705482e-01 6.89400434e-01 6.33925378e-01 1.26391125e+00 -1.16083071e-01 1.40331459e+00 -7.45896161e-01 1.15835287e-01 -2.36434072e-01 -1.02488506e+00 -1.07550907e+00 5.26722312e-01 2.08556533e-01 -1.95764944e-01 -3.91259849e-01 9.23408270e-01 2.12759897e-01 3.40582520e-01 7.25431502e-01 -7.71374404e-01 -5.40930510e-01 4.87279743e-01 -4.33263868e-01 1.96856156e-01 1.34019911e-01 1.89794496e-01 6.57401979e-01 -7.76539743e-01 6.64094746e-01 8.36507499e-01 2.95378953e-01 8.98600161e-01 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-1.02982235e+00 -2.17346147e-01 2.19456866e-01 7.85634935e-01 -1.23037286e-01 -3.85160267e-01 6.66626096e-01]	[10.862950325012207, 0.8693172335624695]
322b9b71-34a8-4b45-89c3-d8e19c837c73	facelet-bank-for-fast-portrait-manipulation	1803.05576	null	http://arxiv.org/abs/1803.05576v3	http://arxiv.org/pdf/1803.05576v3.pdf	Facelet-Bank for Fast Portrait Manipulation	Digital face manipulation has become a popular and fascinating way to touch images with the prevalence of smartphones and social networks. With a wide variety of user preferences, facial expressions, and accessories, a general and flexible model is necessary to accommodate different types of facial editing. In this paper, we propose a model to achieve this goal based on an end-to-end convolutional neural network that supports fast inference, edit-effect control, and quick partial-model update. In addition, this model learns from unpaired image sets with different attributes. Experimental results show that our framework can handle a wide range of expressions, accessories, and makeup effects. It produces high-resolution and high-quality results in fast speed.	['Ying-Cong Chen', 'Ruiyu Li', 'Xin Tao', 'Xiaoyong Shen', 'Jiaya Jia', 'Huaijia Lin', 'Yangang Ye', 'Michelle Shu']	2018-03-15	facelet-bank-for-fast-portrait-manipulation-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Chen_Facelet-Bank_for_Fast_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Facelet-Bank_for_Fast_CVPR_2018_paper.pdf	cvpr-2018-6	['facial-editing']	['computer-vision']	[ 5.31332418e-02 -3.63003939e-01 -2.46370941e-01 -7.68910110e-01 -1.88458398e-01 -4.16350275e-01 4.17611331e-01 -7.86202550e-01 -2.62005329e-01 4.53135341e-01 -7.09760860e-02 1.99140221e-01 2.05134809e-01 -6.60627902e-01 -5.61164856e-01 -1.95826024e-01 4.07366723e-01 2.50117004e-01 -1.59570143e-01 -2.01406211e-01 8.22590590e-02 8.40634882e-01 -1.69052064e+00 2.29682788e-01 5.79627395e-01 1.25269151e+00 1.02454826e-01 4.23917741e-01 -1.56081900e-01 3.63539934e-01 -2.38722116e-01 -9.95169520e-01 2.86572754e-01 5.56103177e-02 -3.56027842e-01 1.40797600e-01 8.85802269e-01 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4b71eeec-2243-46fc-afcf-08bd7b32670e	smart-contract-vulnerability-detection-from	2106.09282	null	https://arxiv.org/abs/2106.09282v1	https://arxiv.org/pdf/2106.09282v1.pdf	Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion	Smart contracts hold digital coins worth billions of dollars, their security issues have drawn extensive attention in the past years. Towards smart contract vulnerability detection, conventional methods heavily rely on fixed expert rules, leading to low accuracy and poor scalability. Recent deep learning approaches alleviate this issue but fail to encode useful expert knowledge. In this paper, we explore combining deep learning with expert patterns in an explainable fashion. Specifically, we develop automatic tools to extract expert patterns from the source code. We then cast the code into a semantic graph to extract deep graph features. Thereafter, the global graph feature and local expert patterns are fused to cooperate and approach the final prediction, while yielding their interpretable weights. Experiments are conducted on all available smart contracts with source code in two platforms, Ethereum and VNT Chain. Empirically, our system significantly outperforms state-of-the-art methods. Our code is released.	['Shouling Ji', 'Qinming He', 'Lei Zhu', 'Xiang Wang', 'Peng Qian', 'Zhenguang Liu']	2021-06-17	null	null	null	null	['vulnerability-detection']	['miscellaneous']	[-2.92762429e-01 4.31516528e-01 -5.63777685e-01 -3.42896402e-01 -5.90153396e-01 -8.69558632e-01 3.11065197e-01 9.57041010e-02 2.05917776e-01 2.85807073e-01 2.91926533e-01 -7.79727578e-01 -2.02279627e-01 -8.12271595e-01 -4.23156112e-01 -1.73726946e-01 -1.35276109e-01 3.19847077e-01 1.40826270e-01 -9.07802507e-02 3.77031267e-01 1.69750631e-01 -8.25309336e-01 3.85561377e-01 1.02011609e+00 1.07691574e+00 -2.90279925e-01 1.43016025e-01 -1.34489924e-01 1.43962383e+00 -4.39728022e-01 -1.17310882e+00 4.34285671e-01 -7.73155019e-02 -6.54857874e-01 -3.25399727e-01 -1.39438540e-01 -3.41165125e-01 -5.96688032e-01 1.41998899e+00 3.04230541e-01 -5.47627449e-01 2.12100431e-01 -1.44357872e+00 -8.20453346e-01 1.26883256e+00 -5.91977477e-01 -5.96404858e-02 1.63342297e-01 2.62386918e-01 1.65790176e+00 -5.89623213e-01 6.18461132e-01 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99043dd2-e92b-4393-9811-be37a0f4d22a	image-declipping-with-deep-networks	1811.06277	null	http://arxiv.org/abs/1811.06277v1	http://arxiv.org/pdf/1811.06277v1.pdf	Image declipping with deep networks	We present a deep network to recover pixel values lost to clipping. The clipped area of the image is typically a uniform area of minimum or maximum brightness, losing image detail and color fidelity. The degree to which the clipping is visually noticeable depends on the amount by which values were clipped, and the extent of the clipped area. Clipping may occur in any (or all) of the pixel's color channels. Although clipped pixels are common and occur to some degree in almost every image we tested, current automatic solutions have only partial success in repairing clipped pixels and work only in limited cases such as only with overexposure (not under-exposure) and when some of the color channels are not clipped. Using neural networks and their ability to model natural images allows our neural network, DeclipNet, to reconstruct data in clipped regions producing state of the art results.	['Shachar Honig', 'Michael Werman']	2018-11-15	null	null	null	null	['image-declipping']	['computer-vision']	[ 5.65794826e-01 2.03431360e-02 1.62753835e-01 -2.40016207e-01 -4.89795953e-01 -8.29206467e-01 1.35599747e-01 -1.68443024e-01 -3.77361476e-01 7.53879726e-01 4.15793024e-02 -2.30261460e-01 4.03235018e-01 -8.21021736e-01 -1.11952174e+00 -4.44592744e-01 -2.67546028e-02 -1.30643412e-01 4.61599290e-01 -2.49490783e-01 1.43200293e-01 6.48408413e-01 -1.48911512e+00 5.02179325e-01 6.79899395e-01 6.57037854e-01 1.23495102e-01 8.76508534e-01 -3.31798196e-02 7.51407146e-01 -8.53793263e-01 -2.76372701e-01 5.55845797e-01 -5.18047452e-01 -5.76444387e-01 1.58885062e-01 8.69562030e-01 -7.58456051e-01 -4.81434464e-01 1.12571645e+00 1.36888802e-01 -2.42561549e-01 1.41089588e-01 -9.69954193e-01 -7.93371797e-01 2.72002310e-01 -6.77566051e-01 6.18986897e-02 4.30193424e-01 2.19104499e-01 2.37997711e-01 -8.50987673e-01 9.25368369e-01 7.59910405e-01 8.94264340e-01 2.91752934e-01 -1.52840233e+00 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c8e20e84-4fc6-45a2-9aa3-f0637ab9b015	3d-video-object-detection-with-learnable	2303.15416	null	https://arxiv.org/abs/2303.15416v1	https://arxiv.org/pdf/2303.15416v1.pdf	3D Video Object Detection with Learnable Object-Centric Global Optimization	We explore long-term temporal visual correspondence-based optimization for 3D video object detection in this work. Visual correspondence refers to one-to-one mappings for pixels across multiple images. Correspondence-based optimization is the cornerstone for 3D scene reconstruction but is less studied in 3D video object detection, because moving objects violate multi-view geometry constraints and are treated as outliers during scene reconstruction. We address this issue by treating objects as first-class citizens during correspondence-based optimization. In this work, we propose BA-Det, an end-to-end optimizable object detector with object-centric temporal correspondence learning and featuremetric object bundle adjustment. Empirically, we verify the effectiveness and efficiency of BA-Det for multiple baseline 3D detectors under various setups. Our BA-Det achieves SOTA performance on the large-scale Waymo Open Dataset (WOD) with only marginal computation cost. Our code is available at https://github.com/jiaweihe1996/BA-Det.	['Zhaoxiang Zhang', 'Naiyan Wang', 'Yuntao Chen', 'JiaWei He']	2023-03-27	null	http://openaccess.thecvf.com//content/CVPR2023/html/He_3D_Video_Object_Detection_With_Learnable_Object-Centric_Global_Optimization_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/He_3D_Video_Object_Detection_With_Learnable_Object-Centric_Global_Optimization_CVPR_2023_paper.pdf	cvpr-2023-1	['3d-scene-reconstruction', 'video-object-detection']	['computer-vision', 'computer-vision']	[-3.19679439e-01 -4.96699303e-01 -2.51177549e-01 -2.34984696e-01 -7.94096172e-01 -6.06299758e-01 3.31162810e-01 -1.11241296e-01 -4.12733108e-01 -1.07908532e-01 1.48077458e-01 -4.94210934e-03 1.87811568e-01 -3.44339818e-01 -9.75301981e-01 -2.56132901e-01 5.80556057e-02 5.74592471e-01 6.06925845e-01 1.28642648e-01 3.07342708e-02 7.30564415e-01 -1.28454101e+00 2.15621233e-01 3.11006486e-01 7.82740831e-01 2.84648627e-01 7.87490726e-01 1.82956219e-01 3.89803082e-01 1.96688250e-02 -3.92231882e-01 8.51310968e-01 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f9665cb1-6c50-41bf-99eb-a88c1ba17e40	track-targets-by-dense-spatio-temporal	2210.09455	null	https://arxiv.org/abs/2210.09455v1	https://arxiv.org/pdf/2210.09455v1.pdf	Track Targets by Dense Spatio-Temporal Position Encoding	In this work, we propose a novel paradigm to encode the position of targets for target tracking in videos using transformers. The proposed paradigm, Dense Spatio-Temporal (DST) position encoding, encodes spatio-temporal position information in a pixel-wise dense fashion. The provided position encoding provides location information to associate targets across frames beyond appearance matching by comparing objects in two bounding boxes. Compared to the typical transformer positional encoding, our proposed encoding is applied to the 2D CNN features instead of the projected feature vectors to avoid losing positional information. Moreover, the designed DST encoding can represent the location of a single-frame object and the evolution of the location of the trajectory among frames uniformly. Integrated with the DST encoding, we build a transformer-based multi-object tracking model. The model takes a video clip as input and conducts the target association in the clip. It can also perform online inference by associating existing trajectories with objects from the new-coming frames. Experiments on video multi-object tracking (MOT) and multi-object tracking and segmentation (MOTS) datasets demonstrate the effectiveness of the proposed DST position encoding.	['Kris Kitani', 'Hao Wu', 'Jinkun Cao']	2022-10-17	null	null	null	null	['multi-object-tracking-and-segmentation']	['computer-vision']	[ 9.84794721e-02 -3.26577127e-01 -2.68553168e-01 -3.71521056e-01 -6.68225229e-01 -5.58214366e-01 5.36767602e-01 2.94837475e-01 -4.39898431e-01 3.26692522e-01 -4.40337099e-02 1.50032505e-01 -3.22744608e-01 -6.97590590e-01 -1.13109601e+00 -7.70803750e-01 -1.63768351e-01 4.22606438e-01 1.04880071e+00 3.43243510e-01 -9.26538557e-03 7.11341202e-01 -1.52053463e+00 2.81693578e-01 2.98753709e-01 1.43336117e+00 4.43544209e-01 6.00774407e-01 -4.52263057e-02 8.41407776e-01 -3.72590244e-01 -2.75220037e-01 3.39212775e-01 -8.48729685e-02 -2.99257219e-01 1.33314490e-01 7.91914701e-01 -4.21773970e-01 -6.50472701e-01 1.04784858e+00 8.54049027e-02 2.61274874e-01 2.89442062e-01 -1.44899440e+00 -4.84598666e-01 4.94393736e-01 -7.32632041e-01 7.05523431e-01 8.97797346e-02 8.06726441e-02 7.44287729e-01 -4.46204394e-01 6.52886987e-01 1.39765477e+00 8.36260319e-01 3.27075034e-01 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e6b4614d-c528-4396-be66-1f0674941210	fine-tuning-offline-reinforcement-learning	null	null	https://openreview.net/forum?id=wiSgdeJ29ee	https://openreview.net/pdf?id=wiSgdeJ29ee	Fine-Tuning Offline Reinforcement Learning with Model-Based Policy Optimization	In offline reinforcement learning (RL), we attempt to learn a control policy from a fixed dataset of environment interactions. This setting has the potential benefit of allowing us to learn effective policies without needing to collect additional interactive data, which can be expensive or dangerous in real-world systems. However, traditional off-policy RL methods tend to perform poorly in this setting due to the distributional shift between the fixed data set and the learned policy. In particular, they tend to extrapolate optimistically and overestimate the action-values outside of the dataset distribution. Recently, two major avenues have been explored to address this issue. First, behavior-regularized methods that penalize actions that deviate from the demonstrated action distribution. Second, uncertainty-aware model-based (MB) methods that discourage state-actions where the dynamics are uncertain. In this work, we propose an algorithmic framework that consists of two stages. In the first stage, we train a policy using behavior-regularized model-free RL on the offline dataset. Then, we can optionally enter a second stage where we fine-tune the policy using our novel Model-Based Behavior-Regularized Policy Optimization (MB2PO) algorithm. We demonstrate that for certain tasks and dataset distributions our conservative model-based fine tuning can greatly increase performance and allow the agent to generalize and outperform the demonstrated behavior. We evaluate our method on a variety of the Gym-MuJoCo tasks in the D4RL benchmark and demonstrate that our method is competitive and in some cases superior to the state of the art for most of the evaluated tasks.	['Jeff Schneider', 'John Dolan', 'Adam Villaflor']	2021-01-01	null	null	null	null	['d4rl']	['robots']	[-4.27460410e-02 4.50494848e-02 -4.37926769e-01 -1.97359815e-01 -7.10565090e-01 -6.26187027e-01 6.00818455e-01 2.46383145e-01 -8.19492996e-01 1.21132112e+00 -1.94102317e-01 -3.74499142e-01 -2.30913401e-01 -6.18210435e-01 -9.17949140e-01 -8.33424211e-01 -3.90716136e-01 7.22695053e-01 2.63928860e-01 -3.24427187e-01 1.43153653e-01 3.44380826e-01 -1.46973300e+00 -1.55939341e-01 9.93047059e-01 9.28271711e-01 2.28872091e-01 5.66828966e-01 2.21458793e-01 7.63358355e-01 -6.82964861e-01 2.16482714e-01 4.14770693e-01 -3.09291810e-01 -4.33020145e-01 -3.89322713e-02 -1.98099628e-01 -6.12606764e-01 3.50996479e-03 9.30874765e-01 5.23235023e-01 5.96731305e-01 2.65466571e-01 -1.21120977e+00 9.17136520e-02 7.49862790e-01 -6.73172891e-01 -7.93227255e-02 1.87186465e-01 6.45282984e-01 5.63434541e-01 -2.11556047e-01 4.43967164e-01 1.41204143e+00 2.21258923e-01 6.74075663e-01 -1.53750408e+00 -5.84131360e-01 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fc57f7bb-b11d-436a-b678-a4a5a12af87d	deep-transfer-learning-for-intelligent	2306.15110	null	https://arxiv.org/abs/2306.15110v1	https://arxiv.org/pdf/2306.15110v1.pdf	Deep Transfer Learning for Intelligent Vehicle Perception: a Survey	Deep learning-based intelligent vehicle perception has been developing prominently in recent years to provide a reliable source for motion planning and decision making in autonomous driving. A large number of powerful deep learning-based methods can achieve excellent performance in solving various perception problems of autonomous driving. However, these deep learning methods still have several limitations, for example, the assumption that lab-training (source domain) and real-testing (target domain) data follow the same feature distribution may not be practical in the real world. There is often a dramatic domain gap between them in many real-world cases. As a solution to this challenge, deep transfer learning can handle situations excellently by transferring the knowledge from one domain to another. Deep transfer learning aims to improve task performance in a new domain by leveraging the knowledge of similar tasks learned in another domain before. Nevertheless, there are currently no survey papers on the topic of deep transfer learning for intelligent vehicle perception. To the best of our knowledge, this paper represents the first comprehensive survey on the topic of the deep transfer learning for intelligent vehicle perception. This paper discusses the domain gaps related to the differences of sensor, data, and model for the intelligent vehicle perception. The recent applications, challenges, future researches in intelligent vehicle perception are also explored.	['Hongkai Yu', 'Tianyun Zhang', 'Zhigang Xu', 'Huiming Sun', 'Jin Ma', 'Jinlong Li', 'Xinyu Liu']	2023-06-26	null	null	null	null	['transfer-learning', 'decision-making', 'motion-planning']	['miscellaneous', 'reasoning', 'robots']	[-6.67849630e-02 -1.16511479e-01 -1.95861638e-01 -6.53145611e-01 -5.95612347e-01 -7.02959672e-02 5.60666323e-01 -2.56511420e-01 -4.36210126e-01 7.62874246e-01 -3.59904975e-01 -4.78630334e-01 3.90808471e-02 -1.10788727e+00 -8.97178590e-01 -7.64856398e-01 2.02238321e-01 5.05294323e-01 5.68358660e-01 -6.81122363e-01 3.33765626e-01 3.10349554e-01 -1.90296507e+00 3.78542066e-01 9.37355399e-01 1.08282137e+00 8.18193138e-01 4.97182459e-01 -1.60068884e-01 6.99570656e-01 -7.39100099e-01 6.58478215e-02 8.20485055e-02 -3.82887609e-02 -5.52091539e-01 -1.03728652e-01 3.30511600e-01 -4.14269060e-01 -5.01164436e-01 9.93463695e-01 5.47716916e-01 1.25476226e-01 8.05201054e-01 -1.76041913e+00 -7.19036400e-01 -1.66496575e-01 -3.69343191e-01 2.83792824e-01 -3.11601851e-02 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b5368b8c-bf7d-428d-bf57-8810d3342393	fvp-fourier-visual-prompting-for-source-free	2304.13672	null	https://arxiv.org/abs/2304.13672v1	https://arxiv.org/pdf/2304.13672v1.pdf	FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentation	Medical image segmentation methods normally perform poorly when there is a domain shift between training and testing data. Unsupervised Domain Adaptation (UDA) addresses the domain shift problem by training the model using both labeled data from the source domain and unlabeled data from the target domain. Source-Free UDA (SFUDA) was recently proposed for UDA without requiring the source data during the adaptation, due to data privacy or data transmission issues, which normally adapts the pre-trained deep model in the testing stage. However, in real clinical scenarios of medical image segmentation, the trained model is normally frozen in the testing stage. In this paper, we propose Fourier Visual Prompting (FVP) for SFUDA of medical image segmentation. Inspired by prompting learning in natural language processing, FVP steers the frozen pre-trained model to perform well in the target domain by adding a visual prompt to the input target data. In FVP, the visual prompt is parameterized using only a small amount of low-frequency learnable parameters in the input frequency space, and is learned by minimizing the segmentation loss between the predicted segmentation of the prompted target image and reliable pseudo segmentation label of the target image under the frozen model. To our knowledge, FVP is the first work to apply visual prompts to SFUDA for medical image segmentation. The proposed FVP is validated using three public datasets, and experiments demonstrate that FVP yields better segmentation results, compared with various existing methods.	['Haogang Zhu', 'Tao Liu', 'Zhenzhou Wu', 'Lanyun Zhu', 'Shuai Shao', 'Yixin Chen', 'Jian Cheng', 'Yan Wang']	2023-04-26	null	null	null	null	['visual-prompting']	['computer-vision']	[ 6.32511735e-01 5.33506930e-01 -4.87821877e-01 -5.47038317e-01 -8.24131191e-01 -4.71489698e-01 1.67944446e-01 1.79823786e-01 -4.73545402e-01 3.68291527e-01 -2.79377755e-02 -2.59052813e-01 1.14672661e-01 -7.19815135e-01 -7.92839408e-01 -7.06617475e-01 1.63794458e-01 7.15244114e-01 3.44875067e-01 5.36358356e-02 -1.71719432e-01 8.05095881e-02 -8.69719863e-01 4.17416602e-01 1.07983673e+00 1.01540160e+00 4.01640892e-01 5.32690287e-01 -2.81279802e-01 5.21509171e-01 -6.92104578e-01 9.80128124e-02 3.84399563e-01 -8.04282427e-01 -1.32166338e+00 3.70716214e-01 8.63578692e-02 -3.66276473e-01 -9.83197987e-02 9.71445918e-01 5.81614614e-01 1.47009924e-01 6.36318207e-01 -1.04507256e+00 -6.38310552e-01 5.33632576e-01 -6.28035069e-01 2.67218798e-01 2.72221535e-01 3.37456584e-01 3.43581110e-01 -5.22120535e-01 1.07948554e+00 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c70952b6-95a3-4d63-9a7e-a1b746553d38	layerdiffusion-layered-controlled-image	2305.18676	null	https://arxiv.org/abs/2305.18676v1	https://arxiv.org/pdf/2305.18676v1.pdf	LayerDiffusion: Layered Controlled Image Editing with Diffusion Models	Text-guided image editing has recently experienced rapid development. However, simultaneously performing multiple editing actions on a single image, such as background replacement and specific subject attribute changes, while maintaining consistency between the subject and the background remains challenging. In this paper, we propose LayerDiffusion, a semantic-based layered controlled image editing method. Our method enables non-rigid editing and attribute modification of specific subjects while preserving their unique characteristics and seamlessly integrating them into new backgrounds. We leverage a large-scale text-to-image model and employ a layered controlled optimization strategy combined with layered diffusion training. During the diffusion process, an iterative guidance strategy is used to generate a final image that aligns with the textual description. Experimental results demonstrate the effectiveness of our method in generating highly coherent images that closely align with the given textual description. The edited images maintain a high similarity to the features of the input image and surpass the performance of current leading image editing methods. LayerDiffusion opens up new possibilities for controllable image editing.	['Zhiheng Li', 'Yikang Ding', 'QInxuan Huang', 'Pengzhi Li']	2023-05-30	null	null	null	null	['text-guided-image-editing']	['computer-vision']	[ 8.39438438e-01 -3.82713750e-02 9.70236212e-02 -3.49647552e-01 -5.17968237e-01 -6.19476497e-01 7.02804804e-01 4.80911061e-02 -2.98468858e-01 5.52190900e-01 1.54773384e-01 1.59643263e-01 1.49421543e-01 -4.16603386e-01 -6.80224359e-01 -6.59703970e-01 5.21401882e-01 2.58929819e-01 4.05554563e-01 -9.05366987e-02 3.02025676e-01 4.70618784e-01 -1.50205362e+00 2.99422413e-01 1.12844634e+00 5.95271647e-01 7.13405311e-01 6.66921258e-01 -3.60123605e-01 8.34081829e-01 -5.01801729e-01 -3.87779742e-01 2.95241565e-01 -5.18991649e-01 -4.07556146e-01 9.19484437e-01 1.00474834e+00 -3.58489066e-01 -1.00979574e-01 1.02306199e+00 4.93025213e-01 2.81529069e-01 4.39728230e-01 -9.97543275e-01 -9.81323004e-01 1.84810162e-01 -9.62978244e-01 -1.98949710e-01 4.33186531e-01 1.56427339e-01 5.48518181e-01 -9.61636364e-01 1.02478504e+00 1.06160128e+00 5.04958749e-01 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2.55802665e-02 -3.55657786e-01 -4.30377752e-01 7.10796297e-01 4.79796141e-01 -4.51557100e-01 -9.00414512e-02 7.97804058e-01]	[11.38927936553955, -0.5487061142921448]
1ab77d1a-a738-4ee0-a8da-d823abd2526e	transfer-learning-without-knowing	2007.08714	null	https://arxiv.org/abs/2007.08714v2	https://arxiv.org/pdf/2007.08714v2.pdf	Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources	Current transfer learning methods are mainly based on finetuning a pretrained model with target-domain data. Motivated by the techniques from adversarial machine learning (ML) that are capable of manipulating the model prediction via data perturbations, in this paper we propose a novel approach, black-box adversarial reprogramming (BAR), that repurposes a well-trained black-box ML model (e.g., a prediction API or a proprietary software) for solving different ML tasks, especially in the scenario with scarce data and constrained resources. The rationale lies in exploiting high-performance but unknown ML models to gain learning capability for transfer learning. Using zeroth order optimization and multi-label mapping techniques, BAR can reprogram a black-box ML model solely based on its input-output responses without knowing the model architecture or changing any parameter. More importantly, in the limited medical data setting, on autism spectrum disorder classification, diabetic retinopathy detection, and melanoma detection tasks, BAR outperforms state-of-the-art methods and yields comparable performance to the vanilla adversarial reprogramming method requiring complete knowledge of the target ML model. BAR also outperforms baseline transfer learning approaches by a significant margin, demonstrating cost-effective means and new insights for transfer learning.	['Tsung-Yi Ho', 'Pin-Yu Chen', 'Yun-Yun Tsai']	2020-07-17	null	https://proceedings.icml.cc/static/paper_files/icml/2020/3642-Paper.pdf	https://proceedings.icml.cc/static/paper_files/icml/2020/3642-Paper.pdf	icml-2020-1	['diabetic-retinopathy-detection']	['medical']	[ 7.02683568e-01 6.19208515e-01 -1.94075018e-01 -6.15660325e-02 -7.71670103e-01 -9.48408842e-01 4.77081180e-01 -2.88872421e-01 -3.99241686e-01 9.13880467e-01 -3.65922302e-01 -3.26114058e-01 8.30823332e-02 -5.51785946e-01 -1.33536780e+00 -5.79128683e-01 2.77272463e-01 5.49851835e-01 -3.20667624e-02 -3.28950375e-01 -4.92533632e-02 3.92678648e-01 -1.00945020e+00 4.63991791e-01 1.05419409e+00 6.63506746e-01 -1.59312218e-01 6.18565321e-01 1.95870876e-01 7.47931004e-01 -7.54663050e-01 -4.99600768e-01 3.82798523e-01 -4.58103180e-01 -7.74652123e-01 -3.20940316e-01 4.32878405e-01 -3.03345263e-01 -1.55327722e-01 1.02573001e+00 5.42091548e-01 -2.82171130e-01 6.02325976e-01 -1.45543075e+00 -9.28392947e-01 4.81451243e-01 -1.36477381e-01 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78466608-9367-4081-82df-4d48b0125f09	proving-equivalence-between-complex	2106.02452	null	https://arxiv.org/abs/2106.02452v2	https://arxiv.org/pdf/2106.02452v2.pdf	Proving Equivalence Between Complex Expressions Using Graph-to-Sequence Neural Models	We target the problem of provably computing the equivalence between two complex expression trees. To this end, we formalize the problem of equivalence between two such programs as finding a set of semantics-preserving rewrite rules from one into the other, such that after the rewrite the two programs are structurally identical, and therefore trivially equivalent.We then develop a graph-to-sequence neural network system for program equivalence, trained to produce such rewrite sequences from a carefully crafted automatic example generation algorithm. We extensively evaluate our system on a rich multi-type linear algebra expression language, using arbitrary combinations of 100+ graph-rewriting axioms of equivalence. Our machine learning system guarantees correctness for all true negatives, and ensures 0 false positive by design. It outputs via inference a valid proof of equivalence for 93% of the 10,000 equivalent expression pairs isolated for testing, using up to 50-term expressions. In all cases, the validity of the sequence produced and therefore the provable assertion of program equivalence is always computable, in negligible time.	['Louis-Noël Pouchet', 'Théo Barollet', 'Steve Kommrusch']	2021-06-01	null	null	null	null	['graph-to-sequence']	['natural-language-processing']	[ 5.55952847e-01 5.13815165e-01 -2.22125188e-01 -3.75958085e-01 -9.55244362e-01 -1.05737543e+00 2.22087339e-01 3.38468522e-01 1.42180458e-01 6.34590507e-01 -2.04246283e-01 -1.45134401e+00 2.62091875e-01 -1.41070414e+00 -1.40627015e+00 -1.11934235e-02 -3.40600222e-01 4.90655631e-01 2.23724514e-01 -3.28077734e-01 4.68089357e-02 3.62892747e-01 -1.79805481e+00 4.47741538e-01 7.49829710e-01 5.97078204e-01 -5.28510332e-01 1.54884851e+00 -8.83289799e-02 1.06484056e+00 -4.21993613e-01 -9.26800013e-01 3.42801929e-01 -4.99722004e-01 -1.22377181e+00 -4.50957447e-01 7.77202189e-01 -4.24396485e-01 5.51076867e-02 1.51902699e+00 -6.23619407e-02 -2.39352807e-01 3.86362821e-01 -1.67663276e+00 -6.28955781e-01 7.66290843e-01 -1.08820297e-01 -1.09625481e-01 8.81958246e-01 3.31248134e-01 1.59463811e+00 -2.31182531e-01 6.21404767e-01 1.19395304e+00 7.54862368e-01 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-1.03892779e+00 -1.27709672e-01 -2.01842651e-01 3.20535898e-01 2.57866353e-01 2.89644599e-01 -2.87217051e-01 3.49488646e-01]	[8.783308029174805, 7.186458587646484]
3ba6823f-2237-492d-a98f-d029684cfec0	learning-spatial-features-from-audio-visual	2307.04760	null	https://arxiv.org/abs/2307.04760v1	https://arxiv.org/pdf/2307.04760v1.pdf	Learning Spatial Features from Audio-Visual Correspondence in Egocentric Videos	We propose a self-supervised method for learning representations based on spatial audio-visual correspondences in egocentric videos. In particular, our method leverages a masked auto-encoding framework to synthesize masked binaural audio through the synergy of audio and vision, thereby learning useful spatial relationships between the two modalities. We use our pretrained features to tackle two downstream video tasks requiring spatial understanding in social scenarios: active speaker detection and spatial audio denoising. We show through extensive experiments that our features are generic enough to improve over multiple state-of-the-art baselines on two public challenging egocentric video datasets, EgoCom and EasyCom. Project: http://vision.cs.utexas.edu/projects/ego_av_corr.	['Kristen Grauman', 'Ziad Al-Halah', 'Sagnik Majumder']	2023-07-10	null	null	null	null	['audio-denoising', 'denoising']	['audio', 'computer-vision']	[-8.12261477e-02 9.51213948e-03 7.21901879e-02 -4.29595858e-01 -1.27351582e+00 -6.32439673e-01 8.62491012e-01 -4.77460951e-01 -9.58159119e-02 3.54295075e-01 1.14888251e+00 3.80529910e-01 5.30419089e-02 -3.04379910e-01 -1.13352203e+00 -4.72656101e-01 -3.13403219e-01 -2.14818999e-01 -1.47936448e-01 2.68563535e-02 1.94599062e-01 -6.00666041e-03 -1.92893314e+00 8.03941131e-01 3.01804930e-01 8.36765587e-01 1.06894998e-02 1.03214037e+00 6.12660408e-01 1.09643257e+00 8.13196786e-03 -5.65621480e-02 2.68373579e-01 -3.40023041e-01 -9.15033519e-01 1.38189346e-01 1.04904580e+00 -6.79618657e-01 -1.13089466e+00 8.20178688e-01 7.03734815e-01 2.91206360e-01 6.99368417e-01 -1.36601400e+00 -7.07184970e-01 8.11778247e-01 -6.25753105e-01 6.01077616e-01 8.38908017e-01 3.37227434e-01 1.04812467e+00 -1.06869769e+00 6.59259200e-01 1.37517142e+00 6.23700619e-01 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6cd0aac3-c685-4d21-a836-f520263eaea8	wind-turbine-gearbox-fault-detection-based-on	2303.03496	null	https://arxiv.org/abs/2303.03496v1	https://arxiv.org/pdf/2303.03496v1.pdf	Wind Turbine Gearbox Fault Detection Based on Sparse Filtering and Graph Neural Networks	The wind energy industry has been experiencing tremendous growth and confronting the failures of wind turbine components. Wind turbine gearbox malfunctions are particularly prevalent and lead to the most prolonged downtime and highest cost. This paper presents a data-driven gearbox fault detection algorithm base on high frequency vibration data using graph neural network (GNN) models and sparse filtering (SF). The approach can take advantage of the comprehensive data sources and the complicated sensing networks. The GNN models, including basic graph neural networks, gated graph neural networks, and gated graph sequential neural networks, are used to detect gearbox condition from knowledge-based graphs formed using wind turbine information. Sparse filtering is used as an unsupervised feature learning method to accelerate the training of the GNN models. The effectiveness of the proposed method was verified on practical experimental data.	['Kenneth A. Loparo', 'Jinsong Wang']	2023-03-06	null	null	null	null	['fault-detection']	['miscellaneous']	[-2.43943855e-02 -3.46029341e-01 2.44206280e-01 1.42963082e-01 4.88856941e-01 9.99351442e-02 -1.58941045e-01 -4.32805717e-01 1.34861350e-01 6.35537565e-01 4.36064936e-02 2.39422098e-02 -7.88384199e-01 -1.19888914e+00 -1.95575744e-01 -6.66023254e-01 -4.09949213e-01 3.03694665e-01 2.07427174e-01 -4.52355295e-01 1.43485352e-01 6.82911634e-01 -2.06325626e+00 -2.82711297e-01 8.51022720e-01 8.37230623e-01 4.72235888e-01 5.95794022e-01 1.38573021e-01 6.66848123e-01 -7.67815590e-01 3.78534555e-01 2.99295366e-01 -4.03722882e-01 -2.19693035e-01 3.75019252e-01 -9.02839974e-02 -3.18629712e-01 -6.37073457e-01 1.25278950e+00 7.24730194e-01 8.87953043e-01 3.78179342e-01 -1.08404922e+00 -4.58166748e-01 6.27638042e-01 -1.39070556e-01 3.73581022e-01 -8.77991095e-02 -5.55259697e-02 5.60427189e-01 -1.23270357e+00 3.51719230e-01 1.01839066e+00 9.59609985e-01 2.25102648e-01 -5.51686645e-01 -4.46226478e-01 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-1.17283356e+00 -9.99821723e-03 2.81060636e-02 -1.32552040e+00 -7.03904688e-01 -9.01535433e-03]	[6.666131019592285, 2.370184898376465]
e35b1cf7-04ac-462d-8f92-0b656abf940d	task-driven-semantic-coding-via-reinforcement	2106.03511	null	https://arxiv.org/abs/2106.03511v1	https://arxiv.org/pdf/2106.03511v1.pdf	Task-driven Semantic Coding via Reinforcement Learning	Task-driven semantic video/image coding has drawn considerable attention with the development of intelligent media applications, such as license plate detection, face detection, and medical diagnosis, which focuses on maintaining the semantic information of videos/images. Deep neural network (DNN)-based codecs have been studied for this purpose due to their inherent end-to-end optimization mechanism. However, the traditional hybrid coding framework cannot be optimized in an end-to-end manner, which makes task-driven semantic fidelity metric unable to be automatically integrated into the rate-distortion optimization process. Therefore, it is still attractive and challenging to implement task-driven semantic coding with the traditional hybrid coding framework, which should still be widely used in practical industry for a long time. To solve this challenge, we design semantic maps for different tasks to extract the pixelwise semantic fidelity for videos/images. Instead of directly integrating the semantic fidelity metric into traditional hybrid coding framework, we implement task-driven semantic coding by implementing semantic bit allocation based on reinforcement learning (RL). We formulate the semantic bit allocation problem as a Markov decision process (MDP) and utilize one RL agent to automatically determine the quantization parameters (QPs) for different coding units (CUs) according to the task-driven semantic fidelity metric. Extensive experiments on different tasks, such as classification, detection and segmentation, have demonstrated the superior performance of our approach by achieving an average bitrate saving of 34.39% to 52.62% over the High Efficiency Video Coding (H.265/HEVC) anchor under equivalent task-related semantic fidelity.	['Zhibo Chen', 'Jun Shi', 'Xin Li']	2021-06-07	null	null	null	null	['license-plate-detection']	['computer-vision']	[ 4.36590493e-01 -2.71042198e-01 -2.10555702e-01 -3.98448765e-01 -6.34197831e-01 -2.76574045e-02 2.53899336e-01 -2.82693923e-01 -4.71334636e-01 5.20600200e-01 -9.49713122e-03 -1.52075201e-01 -1.20389774e-01 -6.63540006e-01 -5.20138741e-01 -8.45731735e-01 1.93796784e-01 6.60678893e-02 3.17016900e-01 5.51558891e-03 6.12313092e-01 5.33836186e-02 -1.47386754e+00 6.12907037e-02 1.09512508e+00 1.32443929e+00 7.58258879e-01 6.26151085e-01 -2.30976790e-01 7.18860507e-01 -5.03995001e-01 -6.30853772e-01 2.45107248e-01 -6.43963039e-01 -6.21525645e-01 3.70591700e-01 -8.90289173e-02 -5.63398898e-01 -3.59113038e-01 1.61736369e+00 5.00522971e-01 1.67481616e-01 4.80666727e-01 -1.34771228e+00 -5.63369334e-01 6.71352167e-03 -5.66331863e-01 2.04596952e-01 -6.49181008e-03 1.97733313e-01 7.13837743e-01 -5.57894707e-01 4.50163603e-01 1.23198259e+00 2.08428338e-01 7.46395648e-01 -7.85235703e-01 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3.63914251e-01 2.16585964e-01 -8.15780044e-01 -2.16573477e-03 3.36854160e-01]	[11.285341262817383, -1.6432334184646606]
863e69e5-f7dc-45be-aabe-4dc8fa3ca07b	commonsense-knowledge-augmented-pretrained-1	2112.08615	null	https://arxiv.org/abs/2112.08615v3	https://arxiv.org/pdf/2112.08615v3.pdf	Knowledge-Augmented Language Models for Cause-Effect Relation Classification	Previous studies have shown the efficacy of knowledge augmentation methods in pretrained language models. However, these methods behave differently across domains and downstream tasks. In this work, we investigate the augmentation of pretrained language models with commonsense knowledge in the cause-effect relation classification and commonsense causal reasoning tasks. After automatically verbalizing ATOMIC2020, a wide coverage commonsense reasoning knowledge graph, and GLUCOSE, a dataset of implicit commonsense causal knowledge, we continually pretrain BERT and RoBERTa with the verbalized data. Then we evaluate the resulting models on cause-effect pair classification and answering commonsense causal reasoning questions. Our results show that continually pretrained language models augmented with commonsense knowledge outperform our baselines on two commonsense causal reasoning benchmarks, COPA and BCOPA-CE, and the Temporal and Causal Reasoning (TCR) dataset, without additional improvement in model architecture or using quality-enhanced data for fine-tuning.	['Mona Diab', 'David A. Broniatowski', 'Pedram Hosseini']	2021-12-16	null	https://aclanthology.org/2022.csrr-1.6	https://aclanthology.org/2022.csrr-1.6.pdf	csrr-acl-2022-5	['commonsense-causal-reasoning', 'relation-classification', 'cause-effect-relation-classification']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[ 3.97917211e-01 4.55814153e-01 -4.65072334e-01 -3.94819200e-01 -4.30676341e-01 -5.58538914e-01 1.06694555e+00 3.43828171e-01 -3.77695054e-01 9.93607461e-01 7.13912964e-01 -4.96533126e-01 -2.94237763e-01 -1.03375435e+00 -7.57033050e-01 6.78849593e-02 1.43155515e-01 8.22311997e-01 2.44435012e-01 -6.54171169e-01 1.97488248e-01 4.57780026e-02 -9.56925452e-01 6.59423053e-01 1.17534685e+00 6.88430607e-01 -2.32189760e-01 5.29588282e-01 -2.57364780e-01 2.14262414e+00 -5.00982046e-01 -1.11094594e+00 -3.58093768e-01 -5.74159086e-01 -1.53165400e+00 -7.21599221e-01 5.52347541e-01 -2.10790187e-01 -3.83464724e-01 8.65035117e-01 1.03991352e-01 2.63714075e-01 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38bfc511-f46e-4488-aa05-331a013e25dc	simpler-but-more-accurate-semantic-dependency	1807.01396	null	http://arxiv.org/abs/1807.01396v1	http://arxiv.org/pdf/1807.01396v1.pdf	Simpler but More Accurate Semantic Dependency Parsing	While syntactic dependency annotations concentrate on the surface or functional structure of a sentence, semantic dependency annotations aim to capture between-word relationships that are more closely related to the meaning of a sentence, using graph-structured representations. We extend the LSTM-based syntactic parser of Dozat and Manning (2017) to train on and generate these graph structures. The resulting system on its own achieves state-of-the-art performance, beating the previous, substantially more complex state-of-the-art system by 0.6% labeled F1. Adding linguistically richer input representations pushes the margin even higher, allowing us to beat it by 1.9% labeled F1.	['Timothy Dozat', 'Christopher D. Manning']	2018-07-03	simpler-but-more-accurate-semantic-dependency-1	https://aclanthology.org/P18-2077	https://aclanthology.org/P18-2077.pdf	acl-2018-7	['semantic-dependency-parsing']	['natural-language-processing']	[ 2.01649934e-01 9.92075264e-01 -3.21805000e-01 -6.32369995e-01 -5.80264270e-01 -6.42052770e-01 6.96445465e-01 6.62921011e-01 -4.33414102e-01 7.14534760e-01 6.07913196e-01 -5.66763699e-01 1.27625726e-02 -9.62272942e-01 -6.94061279e-01 -1.41927063e-01 -2.33494610e-01 2.35301465e-01 2.72791386e-01 -3.33960950e-01 5.22677302e-02 1.40730470e-01 -1.06009102e+00 6.29621923e-01 3.98621589e-01 6.24226511e-01 7.98143968e-02 5.93290389e-01 -5.18697858e-01 1.06467450e+00 -6.23107374e-01 -5.91938078e-01 -3.01119119e-01 -4.03336585e-01 -1.17138088e+00 -2.26842791e-01 4.72976238e-01 3.38525325e-01 -3.78047973e-01 9.61977065e-01 -2.54971720e-02 5.37562929e-02 4.50040519e-01 -6.23888910e-01 -1.02828336e+00 1.21813679e+00 -2.08566144e-01 2.99387246e-01 3.60925555e-01 -2.96864390e-01 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1.25753582e+00 3.29745442e-01 8.01410437e-01 2.05622673e-01 -6.30452394e-01 -1.57061130e-01 -1.10611248e+00 -1.39518753e-01 5.11723638e-01 1.07213907e-01 -3.04626912e-01 -1.19276412e-01 -7.30210170e-02]	[10.404014587402344, 9.335216522216797]
827d6973-cc72-4039-8e93-ddc2ab82574d	prod-prompting-to-disentangle-domain	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Ma_ProD_Prompting-To-Disentangle_Domain_Knowledge_for_Cross-Domain_Few-Shot_Image_Classification_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Ma_ProD_Prompting-To-Disentangle_Domain_Knowledge_for_Cross-Domain_Few-Shot_Image_Classification_CVPR_2023_paper.pdf	ProD: Prompting-To-Disentangle Domain Knowledge for Cross-Domain Few-Shot Image Classification	This paper considers few-shot image classification under the cross-domain scenario, where the train-to-test domain gap compromises classification accuracy. To mitigate the domain gap, we propose a prompting-to-disentangle (ProD) method through a novel exploration with the prompting mechanism. ProD adopts the popular multi-domain training scheme and extracts the backbone feature with a standard Convolutional Neural Network. Based on these two common practices, the key point of ProD is using the prompting mechanism in the transformer to disentangle the domain-general (DG) and domain-specific (DS) knowledge from the backbone feature. Specifically, ProD concatenates a DG and a DS prompt to the backbone feature and feeds them into a lightweight transformer. The DG prompt is learnable and shared by all the training domains, while the DS prompt is generated from the domain-of-interest on the fly. As a result, the transformer outputs DG and DS features in parallel with the two prompts, yielding the disentangling effect. We show that: 1) Simply sharing a single DG prompt for all the training domains already improves generalization towards the novel test domain. 2) The cross-domain generalization can be further reinforced by making the DG prompt neutral towards the training domains. 3) When inference, the DS prompt is generated from the support samples and can capture test domain knowledge through the prompting mechanism. Combining all three benefits, ProD significantly improves cross-domain few-shot classification. For instance, on CUB, ProD improves the 5-way 5-shot accuracy from 73.56% (baseline) to 79.19%, setting a new state of the art.	['Yi Yang', 'Zongxin Yang', 'Yifan Sun', 'Tianyi Ma']	2023-01-01	null	null	null	cvpr-2023-1	['cross-domain-few-shot', 'few-shot-image-classification']	['computer-vision', 'computer-vision']	[ 2.48299956e-01 5.96344098e-02 -5.25419056e-01 -3.70046288e-01 -9.25836325e-01 -8.33214939e-01 7.13329732e-01 2.56294049e-02 -3.23783994e-01 7.72025287e-01 1.82396155e-02 -2.29218051e-01 -1.29352540e-01 -8.73720646e-01 -7.35459507e-01 -7.82926500e-01 1.95288181e-01 2.70107180e-01 4.91799474e-01 -2.00482145e-01 -2.76956353e-02 2.17659831e-01 -1.69978595e+00 4.65926886e-01 1.05278206e+00 1.42231071e+00 2.13592917e-01 3.93346012e-01 -1.56340107e-01 5.55765331e-01 -6.72190249e-01 -3.01719278e-01 5.09325266e-01 -3.77534896e-01 -5.96130431e-01 -9.36440472e-03 2.00890973e-01 -7.09159493e-01 -1.83782876e-01 8.59363437e-01 6.06872082e-01 1.36857167e-01 5.73611021e-01 -1.54471397e+00 -7.20370054e-01 3.75426024e-01 -5.51117897e-01 9.28910598e-02 1.90045893e-01 3.75186682e-01 1.12354183e+00 -7.67260253e-01 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d5a94e3d-20f2-4ad6-8f75-cd4d5384d078	deep-representations-for-iris-face-and	1410.1980	null	https://arxiv.org/abs/1410.1980v3	https://arxiv.org/pdf/1410.1980v3.pdf	Deep Representations for Iris, Face, and Fingerprint Spoofing Detection	Biometrics systems have significantly improved person identification and authentication, playing an important role in personal, national, and global security. However, these systems might be deceived (or "spoofed") and, despite the recent advances in spoofing detection, current solutions often rely on domain knowledge, specific biometric reading systems, and attack types. We assume a very limited knowledge about biometric spoofing at the sensor to derive outstanding spoofing detection systems for iris, face, and fingerprint modalities based on two deep learning approaches. The first approach consists of learning suitable convolutional network architectures for each domain, while the second approach focuses on learning the weights of the network via back-propagation. We consider nine biometric spoofing benchmarks --- each one containing real and fake samples of a given biometric modality and attack type --- and learn deep representations for each benchmark by combining and contrasting the two learning approaches. This strategy not only provides better comprehension of how these approaches interplay, but also creates systems that exceed the best known results in eight out of the nine benchmarks. The results strongly indicate that spoofing detection systems based on convolutional networks can be robust to attacks already known and possibly adapted, with little effort, to image-based attacks that are yet to come.	['Alexandre Xavier Falcao', 'Helio Pedrini', 'Giovani Chiachia', 'Anderson Rocha', 'Allan Pinto', 'William Robson Schwartz', 'David Menotti']	2014-10-08	null	null	null	null	['person-identification']	['computer-vision']	[ 5.75397611e-01 -1.24538675e-01 -6.90312684e-03 -3.87633115e-01 1.95961725e-02 -6.45637453e-01 7.52309799e-01 -7.98092186e-02 -3.61352175e-01 5.13044775e-01 -1.80139810e-01 -5.35044789e-01 -7.90304840e-02 -8.12545180e-01 -5.22651911e-01 -6.89081967e-01 2.97256783e-02 1.80160433e-01 -1.00718871e-01 -4.38539445e-01 2.32644647e-01 8.58076096e-01 -1.34139252e+00 3.75207752e-01 5.54223955e-01 1.25174022e+00 -8.61161709e-01 8.58768582e-01 1.10157892e-01 1.95579469e-01 -8.56712520e-01 -1.07677972e+00 3.98683369e-01 -2.40156606e-01 -6.82006478e-01 -4.18468952e-01 8.83084357e-01 -4.19581413e-01 -5.18176913e-01 1.24105525e+00 6.83695197e-01 -5.54238558e-01 4.54745591e-01 -1.29183435e+00 -6.72867715e-01 4.45038110e-01 -2.52239138e-01 -5.80575839e-02 6.39876664e-01 4.70057100e-01 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e47439b5-37de-4d6d-b015-14a1e8b2832c	cesi-canonicalizing-open-knowledge-bases	1902.00172	null	http://arxiv.org/abs/1902.00172v1	http://arxiv.org/pdf/1902.00172v1.pdf	CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information	Open Information Extraction (OpenIE) methods extract (noun phrase, relation phrase, noun phrase) triples from text, resulting in the construction of large Open Knowledge Bases (Open KBs). The noun phrases (NPs) and relation phrases in such Open KBs are not canonicalized, leading to the storage of redundant and ambiguous facts. Recent research has posed canonicalization of Open KBs as clustering over manuallydefined feature spaces. Manual feature engineering is expensive and often sub-optimal. In order to overcome this challenge, we propose Canonicalization using Embeddings and Side Information (CESI) - a novel approach which performs canonicalization over learned embeddings of Open KBs. CESI extends recent advances in KB embedding by incorporating relevant NP and relation phrase side information in a principled manner. Through extensive experiments on multiple real-world datasets, we demonstrate CESI's effectiveness.	['Shikhar Vashishth', 'Prince Jain', 'Partha Talukdar']	2019-02-01	null	null	null	null	['open-knowledge-graph-canonicalization']	['knowledge-base']	[-3.20082307e-01 5.23896575e-01 -4.26712424e-01 -8.64050537e-02 -7.66366482e-01 -9.17375982e-01 2.46106133e-01 4.00610089e-01 -5.33473849e-01 1.23144293e+00 6.43466771e-01 -2.10485578e-01 -2.86916256e-01 -9.45615232e-01 -6.79328799e-01 -3.99428457e-01 -1.29598841e-01 6.55336142e-01 1.12009756e-01 -2.05265179e-01 -2.04613000e-01 1.92578390e-01 -1.02663517e+00 1.47345021e-01 8.74023974e-01 7.19319224e-01 -9.73022655e-02 1.52356789e-01 -5.34059286e-01 5.43904491e-02 -1.93286851e-01 -9.06195223e-01 3.80714625e-01 4.59129065e-01 -1.16834545e+00 -4.72853482e-01 8.35217908e-02 6.79856837e-02 -3.87663215e-01 9.35412884e-01 2.83004224e-01 1.28091961e-01 8.07548940e-01 -1.12213576e+00 -1.06355405e+00 7.45802522e-01 -2.29810968e-01 1.03841290e-01 5.08848727e-01 -4.61563885e-01 2.00762129e+00 -1.30056226e+00 8.65420640e-01 1.02898932e+00 6.31568789e-01 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37660397-63ad-44f7-a174-ce83d93d4ac6	readability-controllable-biomedical-document	2210.04705	null	https://arxiv.org/abs/2210.04705v3	https://arxiv.org/pdf/2210.04705v3.pdf	Readability Controllable Biomedical Document Summarization	Different from general documents, it is recognised that the ease with which people can understand a biomedical text is eminently varied, owing to the highly technical nature of biomedical documents and the variance of readers' domain knowledge. However, existing biomedical document summarization systems have paid little attention to readability control, leaving users with summaries that are incompatible with their levels of expertise. In recognition of this urgent demand, we introduce a new task of readability controllable summarization for biomedical documents, which aims to recognise users' readability demands and generate summaries that better suit their needs: technical summaries for experts and plain language summaries (PLS) for laymen. To establish this task, we construct a corpus consisting of biomedical papers with technical summaries and PLSs written by the authors, and benchmark multiple advanced controllable abstractive and extractive summarization models based on pre-trained language models (PLMs) with prevalent controlling and generation techniques. Moreover, we propose a novel masked language model (MLM) based metric and its variant to effectively evaluate the readability discrepancy between lay and technical summaries. Experimental results from automated and human evaluations show that though current control techniques allow for a certain degree of readability adjustment during generation, the performance of existing controllable summarization methods is far from desirable in this task.	['Sophia Ananiadou', 'Qianqian Xie', 'Zheheng Luo']	2022-10-10	null	null	null	null	['extractive-summarization']	['natural-language-processing']	[ 7.07032502e-01 4.49264556e-01 -2.00396314e-01 -2.69056380e-01 -1.16276085e+00 -4.34867352e-01 7.65320241e-01 8.43111038e-01 -3.34876627e-01 9.95235920e-01 9.13614333e-01 -6.58731163e-02 -3.24930757e-01 -3.52117568e-01 -6.86154515e-02 -3.75354618e-01 4.22502428e-01 7.32333243e-01 -7.44118020e-02 -2.43064836e-01 7.19173610e-01 3.58586878e-01 -1.20041311e+00 5.17405570e-01 1.69123554e+00 3.38359952e-01 3.14183831e-01 1.11684668e+00 -1.93958715e-01 6.33732438e-01 -1.22237074e+00 -4.58286196e-01 -4.66136932e-01 -7.77048647e-01 -1.04336751e+00 1.17961422e-01 5.41886270e-01 2.44796313e-02 -6.12379238e-02 8.47644269e-01 1.06588018e+00 -6.68664202e-02 1.04631495e+00 -7.16202319e-01 -9.02455628e-01 6.10737741e-01 -2.43332595e-01 3.38258445e-01 8.19081724e-01 -7.03382194e-02 8.60957146e-01 -2.93807298e-01 5.38493097e-01 1.15934849e+00 4.82827038e-01 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e7fa2245-a3f1-4429-ae90-5c8823ec26fd	controllable-image-to-video-translation-a	1808.02992	null	http://arxiv.org/abs/1808.02992v1	http://arxiv.org/pdf/1808.02992v1.pdf	Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation	The recent advances in deep learning have made it possible to generate photo-realistic images by using neural networks and even to extrapolate video frames from an input video clip. In this paper, for the sake of both furthering this exploration and our own interest in a realistic application, we study image-to-video translation and particularly focus on the videos of facial expressions. This problem challenges the deep neural networks by another temporal dimension comparing to the image-to-image translation. Moreover, its single input image fails most existing video generation methods that rely on recurrent models. We propose a user-controllable approach so as to generate video clips of various lengths from a single face image. The lengths and types of the expressions are controlled by users. To this end, we design a novel neural network architecture that can incorporate the user input into its skip connections and propose several improvements to the adversarial training method for the neural network. Experiments and user studies verify the effectiveness of our approach. Especially, we would like to highlight that even for the face images in the wild (downloaded from the Web and the authors' own photos), our model can generate high-quality facial expression videos of which about 50\% are labeled as real by Amazon Mechanical Turk workers.	['Boqing Gong', 'Chuang Gan', 'Lijie Fan', 'Junzhou Huang', 'Wenbing Huang']	2018-08-09	null	null	null	null	['facial-expression-generation']	['computer-vision']	[ 3.54136586e-01 2.78360397e-01 6.37779608e-02 -3.87056589e-01 -4.51775074e-01 -5.41601300e-01 4.98020142e-01 -8.31457555e-01 -3.01118702e-01 7.60317981e-01 -4.10972945e-02 -1.32978648e-01 4.57788289e-01 -6.19495749e-01 -1.06421947e+00 -5.69346070e-01 1.47552684e-01 1.88591313e-02 -1.77230626e-01 -3.28103632e-01 -1.75004125e-01 6.02310658e-01 -1.54817259e+00 4.41953391e-01 4.47273791e-01 1.06434548e+00 -3.61805186e-02 6.47397697e-01 1.78572923e-01 9.95572984e-01 -7.90069103e-01 -8.05713236e-01 4.23092008e-01 -7.09210098e-01 -5.39540708e-01 2.93164551e-01 5.73700666e-01 -7.98922837e-01 -4.73922133e-01 1.05616033e+00 6.34846807e-01 -7.88592249e-02 3.79451603e-01 -1.52450264e+00 -8.95176589e-01 4.34145391e-01 -3.97171021e-01 -2.03822657e-01 5.66811144e-01 3.23790371e-01 3.96901965e-01 -8.43374848e-01 9.57846165e-01 1.18770623e+00 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b1ae6f9a-3ae9-4162-88c9-fe7b46d93ac5	document-level-relation-extraction-with-3	2204.12679	null	https://arxiv.org/abs/2204.12679v1	https://arxiv.org/pdf/2204.12679v1.pdf	Document-Level Relation Extraction with Sentences Importance Estimation and Focusing	Document-level relation extraction (DocRE) aims to determine the relation between two entities from a document of multiple sentences. Recent studies typically represent the entire document by sequence- or graph-based models to predict the relations of all entity pairs. However, we find that such a model is not robust and exhibits bizarre behaviors: it predicts correctly when an entire test document is fed as input, but errs when non-evidence sentences are removed. To this end, we propose a Sentence Importance Estimation and Focusing (SIEF) framework for DocRE, where we design a sentence importance score and a sentence focusing loss, encouraging DocRE models to focus on evidence sentences. Experimental results on two domains show that our SIEF not only improves overall performance, but also makes DocRE models more robust. Moreover, SIEF is a general framework, shown to be effective when combined with a variety of base DocRE models.	['Tiejun Zhao', 'Lili Mou', 'Kehai Chen', 'Wang Xu']	2022-04-27	null	https://aclanthology.org/2022.naacl-main.212	https://aclanthology.org/2022.naacl-main.212.pdf	naacl-2022-7	['dialog-relation-extraction', 'document-level-relation-extraction']	['natural-language-processing', 'natural-language-processing']	[ 2.85705775e-01 3.89938235e-01 -3.13373297e-01 -3.87011200e-01 -7.17912555e-01 -5.34242034e-01 7.99199939e-01 6.00940406e-01 -3.08997929e-01 9.62290943e-01 4.12681907e-01 -3.29893649e-01 -4.07715917e-01 -8.10661077e-01 -6.11514449e-01 -2.11065318e-02 -1.57546848e-01 6.36034906e-01 5.95233381e-01 -4.57362264e-01 3.03083211e-01 4.52526122e-01 -1.16869462e+00 4.39817786e-01 9.57776546e-01 6.01665854e-01 -1.62236709e-02 9.90781546e-01 -1.28507271e-01 1.20260239e+00 -7.38920331e-01 -9.75004077e-01 -1.48186624e-01 -3.79415691e-01 -1.22662890e+00 -5.32604083e-02 4.18647259e-01 -1.26388773e-01 -2.68565118e-01 9.53752100e-01 2.60252088e-01 -1.12913243e-01 8.47155154e-01 -1.06471848e+00 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b78188ca-365b-4323-b31f-0fcc49f9cd96	biosentvec-creating-sentence-embeddings-for	1810.09302	null	https://arxiv.org/abs/1810.09302v6	https://arxiv.org/pdf/1810.09302v6.pdf	BioSentVec: creating sentence embeddings for biomedical texts	Sentence embeddings have become an essential part of today's natural language processing (NLP) systems, especially together advanced deep learning methods. Although pre-trained sentence encoders are available in the general domain, none exists for biomedical texts to date. In this work, we introduce BioSentVec: the first open set of sentence embeddings trained with over 30 million documents from both scholarly articles in PubMed and clinical notes in the MIMIC-III Clinical Database. We evaluate BioSentVec embeddings in two sentence pair similarity tasks in different text genres. Our benchmarking results demonstrate that the BioSentVec embeddings can better capture sentence semantics compared to the other competitive alternatives and achieve state-of-the-art performance in both tasks. We expect BioSentVec to facilitate the research and development in biomedical text mining and to complement the existing resources in biomedical word embeddings. BioSentVec is publicly available at https://github.com/ncbi-nlp/BioSentVec	['Qingyu Chen', 'Yifan Peng', 'Zhiyong Lu']	2018-10-22	null	null	null	null	['sentence-embeddings-for-biomedical-texts', 'sentence-embeddings-for-biomedical-texts']	['methodology', 'natural-language-processing']	[-4.97211665e-02 -7.13084713e-02 -3.36423904e-01 -2.16288447e-01 -7.71999836e-01 -6.15740642e-02 3.91480833e-01 1.05493450e+00 -8.54712129e-01 7.67540574e-01 9.30781543e-01 -3.30575645e-01 2.43285578e-02 -4.64216471e-01 -2.59936094e-01 -4.21006292e-01 -4.29591164e-02 5.40503919e-01 -1.57440200e-01 -3.15398574e-01 2.64326125e-01 2.13298351e-01 -8.71688783e-01 4.77903068e-01 8.04380417e-01 7.11649716e-01 2.78818101e-01 7.84989119e-01 -4.18020695e-01 3.09378147e-01 -4.01138812e-01 -5.13349533e-01 -4.50697392e-01 -1.39319628e-01 -1.01321244e+00 -5.33499956e-01 1.43615246e-01 1.83893189e-01 -6.70265079e-01 1.06986094e+00 1.15722048e+00 -1.71955600e-01 6.95039272e-01 -6.82516575e-01 -1.32877731e+00 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30bf47ec-49a8-4bc9-9c5a-fc7eee3e239f	an-energy-efficient-reconfigurable	2301.07050	null	https://arxiv.org/abs/2301.07050v1	https://arxiv.org/pdf/2301.07050v1.pdf	An Energy-Efficient Reconfigurable Autoencoder Implementation on FPGA	Autoencoders are unsupervised neural networks that are used to process and compress input data and then reconstruct the data back to the original data size. This allows autoencoders to be used for different processing applications such as data compression, image classification, image noise reduction, and image coloring. Hardware-wise, re-configurable architectures like Field Programmable Gate Arrays (FPGAs) have been used for accelerating computations from several domains because of their unique combination of flexibility, performance, and power efficiency. In this paper, we look at the different autoencoders available and use the convolutional autoencoder in both FPGA and GPU-based implementations to process noisy static MNIST images. We compare the different results achieved with the FPGA and GPU-based implementations and then discuss the pros and cons of each implementation. The evaluation of the proposed design achieved 80%accuracy and our experimental results show that the proposed accelerator achieves a throughput of 21.12 Giga-Operations Per Second (GOP/s) with a 5.93 W on-chip power consumption at 100 MHz. The comparison results with off-the-shelf devices and recent state-of-the-art implementations illustrate that the proposed accelerator has obvious advantages in terms of energy efficiency and design flexibility. We also discuss future work that can be done with the use of our proposed accelerator.	['Lifeng Zhou', 'Matthew Oldland', 'Murat Isik']	2023-01-17	null	null	null	null	['data-compression']	['time-series']	[ 5.30057028e-02 -2.93680549e-01 2.28922352e-01 -3.91754448e-01 5.35347819e-01 -5.32640442e-02 2.77967930e-01 2.34834298e-01 -7.71154165e-01 2.88860470e-01 1.65051952e-01 -5.47060251e-01 -4.59004147e-03 -1.10114741e+00 -5.44539690e-01 -7.70046771e-01 -8.50535780e-02 1.84057355e-02 3.24950784e-01 -2.49028578e-01 1.22667179e-01 6.92650497e-01 -2.14116240e+00 5.01323700e-01 3.82144213e-01 1.11605024e+00 5.80180585e-01 8.63230348e-01 6.20505176e-02 7.75469244e-01 -5.70383966e-01 -2.67060399e-01 4.80571836e-01 -2.87557375e-02 -2.64237463e-01 -1.94912016e-01 1.29246652e-01 -4.49172050e-01 -6.88085318e-01 9.10546958e-01 4.87170964e-01 1.42542481e-01 2.72646338e-01 -1.03074372e+00 -2.83965290e-01 4.95150328e-01 -4.53697711e-01 6.49433136e-01 -1.61757365e-01 -5.96159995e-02 2.24620759e-01 -4.38263506e-01 1.22317426e-01 9.87854719e-01 6.58013701e-01 2.27658913e-01 -6.38435364e-01 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6346fb68-4858-4248-9ae7-f3586c8f2421	font-representation-learning-via-paired-glyph	2211.10967	null	https://arxiv.org/abs/2211.10967v1	https://arxiv.org/pdf/2211.10967v1.pdf	Font Representation Learning via Paired-glyph Matching	Fonts can convey profound meanings of words in various forms of glyphs. Without typography knowledge, manually selecting an appropriate font or designing a new font is a tedious and painful task. To allow users to explore vast font styles and create new font styles, font retrieval and font style transfer methods have been proposed. These tasks increase the need for learning high-quality font representations. Therefore, we propose a novel font representation learning scheme to embed font styles into the latent space. For the discriminative representation of a font from others, we propose a paired-glyph matching-based font representation learning model that attracts the representations of glyphs in the same font to one another, but pushes away those of other fonts. Through evaluations on font retrieval with query glyphs on new fonts, we show our font representation learning scheme achieves better generalization performance than the existing font representation learning techniques. Finally on the downstream font style transfer and generation tasks, we confirm the benefits of transfer learning with the proposed method. The source code is available at https://github.com/junhocho/paired-glyph-matching.	['Jin Young Choi', 'Kyuewang Lee', 'Junho Cho']	2022-11-20	null	null	null	null	['font-style-transfer']	['computer-vision']	[ 2.40489483e-01 -2.82255381e-01 -8.51254463e-02 -5.10414004e-01 -5.84353387e-01 -9.72062528e-01 5.00532150e-01 -5.35651334e-02 2.46354192e-02 4.01699424e-01 4.51918900e-01 -3.08771282e-01 2.42030159e-01 -7.15643346e-01 -5.19520462e-01 -6.81345880e-01 5.45501649e-01 2.06928462e-01 -1.57639638e-01 -5.29203117e-01 4.20268834e-01 4.24204171e-01 -1.49022651e+00 8.59389544e-01 1.05581605e+00 7.69758642e-01 5.43592632e-01 4.83293414e-01 -7.29495049e-01 2.12893188e-01 -8.78626227e-01 -2.25082964e-01 5.76660872e-01 -2.88960606e-01 -4.05052453e-01 1.42791450e-01 8.90370786e-01 -2.34925181e-01 -2.46358156e-01 8.63817275e-01 5.22021115e-01 6.55704439e-02 1.04129422e+00 -8.61608505e-01 -1.77392840e+00 5.64926028e-01 -5.50827324e-01 -1.60287157e-01 3.30743492e-01 1.63903132e-01 1.00114071e+00 -1.26840949e+00 5.63106298e-01 1.39611757e+00 2.97403067e-01 9.07908678e-01 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6ce10019-42dd-403e-858f-0bda197b2ba4	robust-multi-agent-pickup-and-delivery-with	2303.17422	null	https://arxiv.org/abs/2303.17422v1	https://arxiv.org/pdf/2303.17422v1.pdf	Robust Multi-Agent Pickup and Delivery with Delays	Multi-Agent Pickup and Delivery (MAPD) is the problem of computing collision-free paths for a group of agents such that they can safely reach delivery locations from pickup ones. These locations are provided at runtime, making MAPD a combination between classical Multi-Agent Path Finding (MAPF) and online task assignment. Current algorithms for MAPD do not consider many of the practical issues encountered in real applications: real agents often do not follow the planned paths perfectly, and may be subject to delays and failures. In this paper, we study the problem of MAPD with delays, and we present two solution approaches that provide robustness guarantees by planning paths that limit the effects of imperfect execution. In particular, we introduce two algorithms, k-TP and p-TP, both based on a decentralized algorithm typically used to solve MAPD, Token Passing (TP), which offer deterministic and probabilistic guarantees, respectively. Experimentally, we compare our algorithms against a version of TP enriched with online replanning. k-TP and p-TP provide robust solutions, significantly reducing the number of replans caused by delays, with little or no increase in solution cost and running time.	['Francesco Amigoni', 'Nicola Basilico', 'Giacomo Lodigiani']	2023-03-30	null	null	null	null	['multi-agent-path-finding']	['playing-games']	[-1.63956329e-01 2.41383910e-01 -2.39650644e-02 -2.56455261e-02 -6.83356583e-01 -7.71629453e-01 5.21515965e-01 9.08807814e-01 -6.61621988e-01 1.13364565e+00 -2.27434993e-01 -7.10822120e-02 -9.31721449e-01 -9.17902350e-01 -6.84554458e-01 -5.96764028e-01 -1.01096761e+00 1.33088362e+00 1.02751207e+00 -3.65489185e-01 1.78591162e-01 5.24519622e-01 -1.20788467e+00 -2.89636821e-01 6.35453463e-01 6.77530468e-01 5.11371136e-01 7.37810671e-01 3.95622216e-02 5.17227352e-01 -8.25534761e-01 1.68945611e-01 3.28797072e-01 1.17577940e-01 -1.13527405e+00 -9.39889327e-02 -7.62292147e-01 -6.13801479e-01 -2.18095139e-01 5.86484432e-01 4.25296038e-01 3.21903110e-01 3.70702386e-01 -2.34055662e+00 1.59822032e-01 9.84705508e-01 -7.19899476e-01 2.63390541e-02 7.79329062e-01 -1.25308912e-02 4.95940953e-01 -9.33889970e-02 9.01453316e-01 1.09013283e+00 6.09130085e-01 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ee3a2a2c-6517-4b9b-99d2-30694df7007c	does-speech-enhancement-of-publicly-available	1910.13488	null	https://arxiv.org/abs/1910.13488v2	https://arxiv.org/pdf/1910.13488v2.pdf	Does Speech enhancement of publicly available data help build robust Speech Recognition Systems?	Automatic speech recognition (ASR) systems play a key role in many commercial products including voice assistants. Typically, they require large amounts of clean speech data for training which gives an undue advantage to large organizations which have tons of private data. In this paper, we have first curated a fairly big dataset using publicly available data sources. Thereafter, we tried to investigate if we can use publicly available noisy data to train robust ASR systems. We have used speech enhancement to clean the noisy data first and then used it together with its cleaned version to train ASR systems. We have found that using speech enhancement gives 9.5\% better word error rate than training on just noisy data and 9\% better than training on just clean data. It's performance is also comparable to the ideal case scenario when trained on noisy and its clean version.	['Klaus Mueller', 'Buvana Ramanan', 'Bhavya Ghai']	2019-10-29	null	null	null	null	['robust-speech-recognition']	['speech']	[ 1.68780923e-01 -7.82055501e-03 3.58981520e-01 -4.67840821e-01 -1.34485614e+00 -4.32817012e-01 5.24439692e-01 -1.29454061e-01 -7.20134795e-01 7.56683171e-01 4.69975144e-01 -6.60599828e-01 1.05258077e-01 -3.43625277e-01 -3.55751306e-01 -8.09651554e-01 2.10191742e-01 3.28269303e-01 -1.63013637e-02 -6.26015306e-01 -2.19946112e-02 4.42352504e-01 -1.56182873e+00 2.34046295e-01 7.77000070e-01 6.80960476e-01 5.34164429e-01 9.19256449e-01 -1.40734717e-01 5.41714609e-01 -1.10037053e+00 -1.84021235e-01 4.42980468e-01 -1.55539066e-01 -7.26206422e-01 4.73552234e-02 2.94219032e-02 -3.39782648e-02 -6.23365819e-01 1.14697313e+00 7.85248339e-01 5.12583554e-01 -2.65035802e-03 -6.58595800e-01 -4.74893689e-01 7.62064576e-01 -1.63393766e-01 4.21922982e-01 4.14995164e-01 2.20602080e-01 6.66668534e-01 -7.82594919e-01 5.07198334e-01 1.00951862e+00 4.19086009e-01 8.38448107e-01 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718196a7-d494-4aea-a62b-fc898e068591	level-set-binocular-stereo-with-occlusions	2109.03464	null	https://arxiv.org/abs/2109.03464v1	https://arxiv.org/pdf/2109.03464v1.pdf	Level Set Binocular Stereo with Occlusions	Localizing stereo boundaries and predicting nearby disparities are difficult because stereo boundaries induce occluded regions where matching cues are absent. Most modern computer vision algorithms treat occlusions secondarily (e.g., via left-right consistency checks after matching) or rely on high-level cues to improve nearby disparities (e.g., via deep networks and large training sets). They ignore the geometry of stereo occlusions, which dictates that the spatial extent of occlusion must equal the amplitude of the disparity jump that causes it. This paper introduces an energy and level-set optimizer that improves boundaries by encoding occlusion geometry. Our model applies to two-layer, figure-ground scenes, and it can be implemented cooperatively using messages that pass predominantly between parents and children in an undecimated hierarchy of multi-scale image patches. In a small collection of figure-ground scenes curated from Middlebury and Falling Things stereo datasets, our model provides more accurate boundaries than previous occlusion-handling stereo techniques. This suggests new directions for creating cooperative stereo systems that incorporate occlusion cues in a human-like manner.	['Todd Zickler', 'Jialiang Wang']	2021-09-08	null	null	null	null	['occlusion-handling']	['computer-vision']	[ 3.06751668e-01 2.19913498e-01 -1.41948551e-01 -7.22150981e-01 -6.03168309e-01 -5.65884650e-01 3.27127963e-01 3.00572813e-01 -3.02590787e-01 6.17565989e-01 6.00332022e-01 -1.76025942e-01 1.85854882e-01 -9.42017317e-01 -9.40919459e-01 -5.07675707e-01 -2.67343689e-02 3.21125835e-01 6.84217751e-01 -4.19726849e-01 5.32186389e-01 4.03729945e-01 -2.15201020e+00 6.45587087e-01 9.50124204e-01 9.06274557e-01 1.17582686e-01 8.22473049e-01 7.39693195e-02 7.49044478e-01 -3.24557334e-01 -1.08076617e-01 6.90615058e-01 -3.23720515e-01 -6.44342065e-01 1.66219532e-01 1.47428536e+00 -6.69041514e-01 -2.01968342e-01 8.27963829e-01 5.18894970e-01 -3.20526920e-02 3.33270192e-01 -1.09071469e+00 -3.47783059e-01 9.72155929e-02 -8.78550828e-01 3.11891824e-01 4.62105393e-01 1.14195898e-01 1.05919540e+00 -5.11152864e-01 9.01105165e-01 1.46459663e+00 7.40871370e-01 3.98491293e-01 -1.73980260e+00 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-2.57778764e-01 5.75067222e-01 5.63328922e-01 -4.62440640e-01 -5.39347649e-01 2.20217362e-01]	[8.896578788757324, -2.442319393157959]
9e2e9dee-4568-4690-b467-82fd1f459ee5	sicknl-a-dataset-for-dutch-natural-language	2101.05716	null	https://arxiv.org/abs/2101.05716v1	https://arxiv.org/pdf/2101.05716v1.pdf	SICKNL: A Dataset for Dutch Natural Language Inference	We present SICK-NL (read: signal), a dataset targeting Natural Language Inference in Dutch. SICK-NL is obtained by translating the SICK dataset of Marelli et al. (2014)from English into Dutch. Having a parallel inference dataset allows us to compare both monolingual and multilingual NLP models for English and Dutch on the two tasks. In the paper, we motivate and detail the translation process, perform a baseline evaluation on both the original SICK dataset and its Dutch incarnation SICK-NL, taking inspiration from Dutch skipgram embeddings and contextualised embedding models. In addition, we encapsulate two phenomena encountered in the translation to formulate stress tests and verify how well the Dutch models capture syntactic restructurings that do not affect semantics. Our main finding is all models perform worse on SICK-NL than on SICK, indicating that the Dutch dataset is more challenging than the English original. Results on the stress tests show that models don't fully capture word order freedom in Dutch, warranting future systematic studies.	['Michael Moortgat', 'Gijs Wijnholds']	2021-01-14	null	null	null	null	['multilingual-nlp']	['natural-language-processing']	[ 9.36996564e-02 3.48730087e-01 -3.59924167e-01 -5.48575997e-01 -7.15179145e-01 -1.04002535e+00 7.11361349e-01 2.24399880e-01 -8.31243873e-01 8.75828445e-01 1.02838516e+00 -4.73612189e-01 2.11861387e-01 -5.68050504e-01 -7.09556758e-01 -1.82454720e-01 4.86169726e-01 8.40390265e-01 -6.23674169e-02 -4.72536594e-01 -1.07837215e-01 1.68092698e-01 -9.47421730e-01 2.75453418e-01 9.16689396e-01 1.37541518e-01 1.70515597e-01 5.24279714e-01 1.99993816e-03 4.41657096e-01 -4.40391809e-01 -1.12253678e+00 1.24931969e-01 -3.12987834e-01 -8.04179728e-01 -5.07919550e-01 6.28094614e-01 -1.81458071e-01 -2.60339379e-01 7.33930349e-01 8.79458547e-01 -1.89768672e-01 7.97906220e-01 -9.72005248e-01 -1.02306533e+00 1.21600783e+00 1.80958107e-01 5.53771257e-01 8.55976522e-01 2.84696698e-01 1.52882195e+00 -1.31646466e+00 1.25036693e+00 1.49304461e+00 7.32194901e-01 6.39355898e-01 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-2.22798914e-01 3.86792690e-01 1.60297856e-01 -2.12381124e-01 -7.10806489e-01 -8.27186480e-02]	[10.859232902526855, 9.804659843444824]
a83c38e5-4546-458b-a12e-9ee17483d0cc	do-sentence-interactions-matter-leveraging	1910.12203	null	https://arxiv.org/abs/1910.12203v1	https://arxiv.org/pdf/1910.12203v1.pdf	Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification	The rising growth of fake news and misleading information through online media outlets demands an automatic method for detecting such news articles. Of the few limited works which differentiate between trusted vs other types of news article (satire, propaganda, hoax), none of them model sentence interactions within a document. We observe an interesting pattern in the way sentences interact with each other across different kind of news articles. To capture this kind of information for long news articles, we propose a graph neural network-based model which does away with the need of feature engineering for fine grained fake news classification. Through experiments, we show that our proposed method beats strong neural baselines and achieves state-of-the-art accuracy on existing datasets. Moreover, we establish the generalizability of our model by evaluating its performance in out-of-domain scenarios. Code is available at https://github.com/MysteryVaibhav/fake_news_semantics	['Vaibhav Vaibhav', 'Raghuram Mandyam Annasamy', 'Eduard Hovy']	2019-10-27	do-sentence-interactions-matter-leveraging-1	https://aclanthology.org/D19-5316	https://aclanthology.org/D19-5316.pdf	ws-2019-11	['news-classification']	['natural-language-processing']	[-3.34522218e-01 1.76510826e-01 -5.78704298e-01 -3.18600029e-01 -5.26929080e-01 -6.62763834e-01 1.15537429e+00 3.62997502e-01 7.61757642e-02 5.68875372e-01 8.51336002e-01 -5.52281141e-01 2.61146814e-01 -8.75493050e-01 -1.04774570e+00 -9.25463885e-02 1.20556377e-01 4.28256899e-01 4.22344476e-01 -9.06029105e-01 4.62599784e-01 -2.06349660e-02 -8.27484429e-01 8.42809200e-01 5.93619585e-01 6.88971937e-01 -6.39775753e-01 2.61584014e-01 -2.39673436e-01 1.35565293e+00 -9.51687992e-01 -9.71331835e-01 -2.80448217e-02 -4.76770878e-01 -8.63498747e-01 -9.73751768e-02 5.88315248e-01 -4.40483242e-01 -8.82146060e-01 1.26124668e+00 1.25741348e-01 -5.02577186e-01 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2764bfdb-e8aa-49b8-8402-4ef60804386b	visual-aware-hierarchy-based-food-recognition	2012.03368	null	https://arxiv.org/abs/2012.03368v1	https://arxiv.org/pdf/2012.03368v1.pdf	Visual Aware Hierarchy Based Food Recognition	Food recognition is one of the most important components in image-based dietary assessment. However, due to the different complexity level of food images and inter-class similarity of food categories, it is challenging for an image-based food recognition system to achieve high accuracy for a variety of publicly available datasets. In this work, we propose a new two-step food recognition system that includes food localization and hierarchical food classification using Convolutional Neural Networks (CNNs) as the backbone architecture. The food localization step is based on an implementation of the Faster R-CNN method to identify food regions. In the food classification step, visually similar food categories can be clustered together automatically to generate a hierarchical structure that represents the semantic visual relations among food categories, then a multi-task CNN model is proposed to perform the classification task based on the visual aware hierarchical structure. Since the size and quality of dataset is a key component of data driven methods, we introduce a new food image dataset, VIPER-FoodNet (VFN) dataset, consists of 82 food categories with 15k images based on the most commonly consumed foods in the United States. A semi-automatic crowdsourcing tool is used to provide the ground-truth information for this dataset including food object bounding boxes and food object labels. Experimental results demonstrate that our system can significantly improve both classification and recognition performance on 4 publicly available datasets and the new VFN dataset.	['Fengqing Zhu', 'Sri Kalyan Yarlagadda', 'Zeman Shao', 'Jiangpeng He', 'Runyu Mao']	2020-12-06	null	null	null	null	['food-recognition']	['computer-vision']	[ 1.03906095e-02 -3.83557677e-01 -4.03635293e-01 -5.99718273e-01 -4.21312183e-01 -7.02625215e-01 4.48556319e-02 8.98951769e-01 -2.47501791e-01 9.50241014e-02 4.52256441e-01 1.01658210e-01 1.57953069e-01 -1.15253901e+00 -9.11738157e-01 -5.50905943e-01 -2.21042588e-01 8.19125473e-02 8.00973326e-02 -1.02719143e-01 -7.14944154e-02 -1.17832143e-03 -1.76395190e+00 8.06716263e-01 7.69006610e-01 1.20615399e+00 3.29066634e-01 6.73253536e-01 -3.99292648e-01 7.15654016e-01 -1.53330192e-01 1.19825669e-01 4.72045302e-01 -4.61054802e-01 -5.99457145e-01 2.70114124e-01 3.60816777e-01 -3.48446786e-01 1.25790223e-01 1.42099357e+00 3.02174151e-01 -5.01783229e-02 6.59534037e-01 -1.21936989e+00 -1.31419790e+00 8.89090300e-01 -3.07514012e-01 8.22551828e-03 4.97680008e-01 2.06318840e-01 6.12243116e-01 -7.30046630e-01 1.06228709e-01 1.29822040e+00 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a0b2d197-2791-48d1-a79d-3781b46de906	link-prediction-for-egocentrically-sampled	1803.04084	null	http://arxiv.org/abs/1803.04084v1	http://arxiv.org/pdf/1803.04084v1.pdf	Link prediction for egocentrically sampled networks	Link prediction in networks is typically accomplished by estimating or ranking the probabilities of edges for all pairs of nodes. In practice, especially for social networks, the data are often collected by egocentric sampling, which means selecting a subset of nodes and recording all of their edges. This sampling mechanism requires different prediction tools than the typical assumption of links missing at random. We propose a new computationally efficient link prediction algorithm for egocentrically sampled networks, which estimates the underlying probability matrix by estimating its row space. For networks created by sampling rows, our method outperforms many popular link prediction and graphon estimation techniques.	['Yun-Jhong Wu', 'Ji Zhu', 'Elizaveta Levina']	2018-03-12	null	null	null	null	['graphon-estimation']	['graphs']	[-1.50851220e-01 6.33082211e-01 -7.17499435e-01 -1.76139921e-01 8.21297467e-02 -4.13980097e-01 5.47818542e-01 3.12292099e-01 -3.42715271e-02 1.06885839e+00 2.57366061e-01 -1.82197630e-01 -6.29936099e-01 -1.38276851e+00 -5.50935149e-01 -2.60232538e-01 -8.06375623e-01 1.16649926e+00 4.35424477e-01 1.81140453e-01 8.61575902e-02 4.43139255e-01 -8.97623181e-01 -4.23446387e-01 7.25331545e-01 3.37647498e-01 1.19873963e-01 7.28774011e-01 -1.79937139e-01 8.71535361e-01 -6.63323700e-02 -9.51420188e-01 1.45021498e-01 -2.97088683e-01 -8.01358044e-01 9.27391499e-02 2.60921925e-01 -3.90608251e-01 -8.90878975e-01 1.08905077e+00 2.41818711e-01 4.78917845e-02 7.89389014e-01 -1.67621672e+00 3.24277803e-02 1.37830627e+00 -7.62038529e-01 1.78318202e-01 8.94816160e-01 -4.66196388e-01 1.60181737e+00 -6.32079303e-01 1.11338758e+00 1.14154279e+00 8.25893283e-01 1.30387500e-01 -1.69281042e+00 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26a4f94f-0fe5-4f42-a151-90f67a365ee7	playing-to-learn-better-repeated-games-for	2002.03924	null	https://arxiv.org/abs/2002.03924v1	https://arxiv.org/pdf/2002.03924v1.pdf	Playing to Learn Better: Repeated Games for Adversarial Learning with Multiple Classifiers	We consider the problem of prediction by a machine learning algorithm, called learner, within an adversarial learning setting. The learner's task is to correctly predict the class of data passed to it as a query. However, along with queries containing clean data, the learner could also receive malicious or adversarial queries from an adversary. The objective of the adversary is to evade the learner's prediction mechanism by sending adversarial queries that result in erroneous class prediction by the learner, while the learner's objective is to reduce the incorrect prediction of these adversarial queries without degrading the prediction quality of clean queries. We propose a game theory-based technique called a Repeated Bayesian Sequential Game where the learner interacts repeatedly with a model of the adversary using self play to determine the distribution of adversarial versus clean queries. It then strategically selects a classifier from a set of pre-trained classifiers that balances the likelihood of correct prediction for the query along with reducing the costs to use the classifier. We have evaluated our proposed technique using clean and adversarial text data with deep neural network-based classifiers and shown that the learner can select an appropriate classifier that is commensurate with the query type (clean or adversarial) while remaining aware of the cost to use the classifier.	['Michael McCarrick', 'Joseph B. Collins', 'Prithviraj Dasgupta']	2020-02-10	null	null	null	null	['adversarial-text']	['adversarial']	[ 2.56361574e-01 6.42435133e-01 3.39851230e-01 -2.54804313e-01 -1.23399901e+00 -1.08274603e+00 4.54618663e-01 1.74941495e-01 -5.84438622e-01 4.86029536e-01 -1.27758101e-01 -3.42810363e-01 -8.41508582e-02 -1.30023813e+00 -9.18900132e-01 -8.42187405e-01 1.09653577e-01 8.54582429e-01 2.77737170e-01 -3.43783975e-01 6.33088723e-02 2.61159420e-01 -1.43403113e+00 3.25896800e-01 6.94665253e-01 1.02887952e+00 -1.66283801e-01 1.24562609e+00 1.08378127e-01 1.41397488e+00 -1.05442750e+00 -9.72739100e-01 7.56959081e-01 -4.37124461e-01 -7.69193590e-01 -4.62414235e-01 1.74280614e-01 -4.28042710e-01 -3.58074009e-01 1.38638520e+00 3.66480887e-01 1.87946290e-01 6.83725595e-01 -1.33974326e+00 -2.29393914e-01 6.93029344e-01 1.42918557e-01 -3.58388573e-02 1.88304558e-01 2.34362617e-01 1.17323661e+00 -1.57341257e-01 5.13154030e-01 9.99797583e-01 2.88284987e-01 8.28238189e-01 -1.03319347e+00 -7.75831044e-01 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0ba108dd-14fb-4d0e-b650-baee819cfc5e	preference-or-intent-double-disentangled	2305.11084	null	https://arxiv.org/abs/2305.11084v1	https://arxiv.org/pdf/2305.11084v1.pdf	Preference or Intent? Double Disentangled Collaborative Filtering	People usually have different intents for choosing items, while their preferences under the same intent may also different. In traditional collaborative filtering approaches, both intent and preference factors are usually entangled in the modeling process, which significantly limits the robustness and interpretability of recommendation performances. For example, the low-rating items are always treated as negative feedback while they actually could provide positive information about user intent. To this end, in this paper, we propose a two-fold representation learning approach, namely Double Disentangled Collaborative Filtering (DDCF), for personalized recommendations. The first-level disentanglement is for separating the influence factors of intent and preference, while the second-level disentanglement is performed to build independent sparse preference representations under individual intent with limited computational complexity. Specifically, we employ two variational autoencoder networks, intent recognition network and preference decomposition network, to learn the intent and preference factors, respectively. In this way, the low-rating items will be treated as positive samples for modeling intents while the negative samples for modeling preferences. Finally, extensive experiments on three real-world datasets and four evaluation metrics clearly validate the effectiveness and the interpretability of DDCF.	['Hui Xiong', 'Wei Wu', 'Dazhong Shen', 'HengShu Zhu', 'Chao Wang']	2023-05-18	null	null	null	null	['disentanglement', 'collaborative-filtering', 'intent-recognition']	['methodology', 'miscellaneous', 'natural-language-processing']	[-2.38474205e-01 -3.92824084e-01 -4.67809498e-01 -6.30543768e-01 -5.96182235e-02 -4.45691615e-01 2.65415817e-01 -4.22453396e-02 -1.77187011e-01 3.89687270e-01 7.91271389e-01 -1.97214052e-01 -3.79758894e-01 -7.79743731e-01 -2.10597515e-01 -8.87101352e-01 2.85857409e-01 3.30631793e-01 -3.96546930e-01 -1.90605074e-01 5.40629067e-02 -2.08998263e-01 -1.41207778e+00 4.12874848e-01 1.10887861e+00 1.08938015e+00 2.44935453e-02 8.31414089e-02 -6.95688799e-02 6.34762228e-01 -2.00247064e-01 -5.20072877e-01 1.34098306e-01 -1.26200050e-01 -1.69267625e-01 9.38370898e-02 6.59998953e-02 -5.15221417e-01 -5.46673596e-01 1.03718412e+00 2.78288484e-01 5.03292441e-01 6.92022860e-01 -1.08024240e+00 -1.31540692e+00 8.88113081e-01 -4.20022696e-01 -1.66588381e-01 3.22714716e-01 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26fe9e3e-e2bf-413a-bc3c-f4341e568db9	sim-suction-learning-a-suction-grasp-policy	2305.16378	null	https://arxiv.org/abs/2305.16378v1	https://arxiv.org/pdf/2305.16378v1.pdf	Sim-Suction: Learning a Suction Grasp Policy for Cluttered Environments Using a Synthetic Benchmark	This paper presents Sim-Suction, a robust object-aware suction grasp policy for mobile manipulation platforms with dynamic camera viewpoints, designed to pick up unknown objects from cluttered environments. Suction grasp policies typically employ data-driven approaches, necessitating large-scale, accurately-annotated suction grasp datasets. However, the generation of suction grasp datasets in cluttered environments remains underexplored, leaving uncertainties about the relationship between the object of interest and its surroundings. To address this, we propose a benchmark synthetic dataset, Sim-Suction-Dataset, comprising 500 cluttered environments with 3.2 million annotated suction grasp poses. The efficient Sim-Suction-Dataset generation process provides novel insights by combining analytical models with dynamic physical simulations to create fast and accurate suction grasp pose annotations. We introduce Sim-Suction-Pointnet to generate robust 6D suction grasp poses by learning point-wise affordances from the Sim-Suction-Dataset, leveraging the synergy of zero-shot text-to-segmentation. Real-world experiments for picking up all objects demonstrate that Sim-Suction-Pointnet achieves success rates of 96.76%, 94.23%, and 92.39% on cluttered level 1 objects (prismatic shape), cluttered level 2 objects (more complex geometry), and cluttered mixed objects, respectively. The Sim-Suction policies outperform state-of-the-art benchmarks tested by approximately 21% in cluttered mixed scenes.	['David J. Cappelleri', 'Juncheng Li']	2023-05-25	null	null	null	null	['physical-simulations']	['miscellaneous']	[-6.01845570e-02 -2.17612714e-01 -7.06764087e-02 -1.24588490e-01 -6.62010193e-01 -8.49857450e-01 -1.70743272e-01 -1.64186001e-01 -1.68950841e-01 4.10058886e-01 -3.25417340e-01 -1.06648043e-01 -2.90763229e-01 -3.84717435e-01 -1.13860309e+00 -6.48997247e-01 -4.68448460e-01 9.12543058e-01 6.14387989e-01 -3.46507579e-01 2.30820760e-01 8.48337710e-01 -1.68057382e+00 1.92188144e-01 9.98447418e-01 1.06846166e+00 9.47328568e-01 9.07544792e-01 1.16549298e-01 -3.12367052e-01 -5.85911751e-01 -3.17985982e-01 7.63617933e-01 4.99523282e-01 -4.41081494e-01 8.01232979e-02 6.11582577e-01 -9.91141260e-01 -8.32478106e-02 8.45163286e-01 5.05302787e-01 8.91005844e-02 6.59687579e-01 -1.15441036e+00 -5.57837307e-01 5.01539588e-01 -6.38766885e-01 -2.45236441e-01 4.90584373e-01 7.99269497e-01 6.25484169e-01 -1.17342448e+00 8.66091669e-01 1.47348595e+00 4.82964665e-01 6.28133535e-01 -9.13273036e-01 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3.40608209e-01 2.55818635e-01 -1.18616843e+00 -1.23723075e-01 -2.19065934e-01]	[5.788741111755371, -0.8714131116867065]
1c92aa60-33df-4776-b65e-f8c7560469e2	multiqg-ti-towards-question-generation-from	2307.04643	null	https://arxiv.org/abs/2307.04643v1	https://arxiv.org/pdf/2307.04643v1.pdf	MultiQG-TI: Towards Question Generation from Multi-modal Sources	We study the new problem of automatic question generation (QG) from multi-modal sources containing images and texts, significantly expanding the scope of most of the existing work that focuses exclusively on QG from only textual sources. We propose a simple solution for our new problem, called MultiQG-TI, which enables a text-only question generator to process visual input in addition to textual input. Specifically, we leverage an image-to-text model and an optical character recognition model to obtain the textual description of the image and extract any texts in the image, respectively, and then feed them together with the input texts to the question generator. We only fine-tune the question generator while keeping the other components fixed. On the challenging ScienceQA dataset, we demonstrate that MultiQG-TI significantly outperforms ChatGPT with few-shot prompting, despite having hundred-times less trainable parameters. Additional analyses empirically confirm the necessity of both visual and textual signals for QG and show the impact of various modeling choices.	['Richard Baraniuk', 'Zichao Wang']	2023-07-07	null	null	null	null	['question-generation']	['natural-language-processing']	[ 4.15501058e-01 2.31433421e-01 4.65409428e-01 -5.97974248e-02 -1.55745447e+00 -1.09791028e+00 8.65009844e-01 -9.73652676e-02 -4.25980866e-01 4.06503439e-01 2.05020830e-01 -5.34869671e-01 2.67990589e-01 -6.24308467e-01 -9.30215120e-01 -5.17284095e-01 7.15019882e-01 5.45792282e-01 3.15388292e-01 -2.10583031e-01 3.43524516e-01 -1.51045686e-02 -1.45267582e+00 5.30496001e-01 8.76544178e-01 8.62988472e-01 4.50184733e-01 1.20949507e+00 -3.67546737e-01 1.19017708e+00 -8.97206604e-01 -6.70460403e-01 1.58665448e-01 -8.87529075e-01 -9.56772745e-01 6.28936350e-01 8.97189260e-01 -5.64439714e-01 -5.14169872e-01 7.82052696e-01 6.07589364e-01 8.06382522e-02 6.26563370e-01 -1.28078663e+00 -8.90978813e-01 3.11635494e-01 -4.68868256e-01 1.80324331e-01 6.49124026e-01 7.18168736e-01 1.31140053e+00 -1.04597020e+00 7.85355270e-01 1.23220217e+00 9.70506370e-02 5.85170090e-01 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0b19f65c-d369-47cd-a521-3731844aafd7	a-new-approach-to-descriptors-generation-for	2007.06624	null	https://arxiv.org/abs/2007.06624v1	https://arxiv.org/pdf/2007.06624v1.pdf	A new approach to descriptors generation for image retrieval by analyzing activations of deep neural network layers	In this paper, we consider the problem of descriptors construction for the task of content-based image retrieval using deep neural networks. The idea of neural codes, based on fully connected layers activations, is extended by incorporating the information contained in convolutional layers. It is known that the total number of neurons in the convolutional part of the network is large and the majority of them have little influence on the final classification decision. Therefore, in the paper we propose a novel algorithm that allows us to extract the most significant neuron activations and utilize this information to construct effective descriptors. The descriptors consisting of values taken from both the fully connected and convolutional layers perfectly represent the whole image content. The images retrieved using these descriptors match semantically very well to the query image, and also they are similar in other secondary image characteristics, like background, textures or color distribution. These features of the proposed descriptors are verified experimentally based on the IMAGENET1M dataset using the VGG16 neural network.	['Paweł Staszewski', 'Maciej Jaworski', 'Jinde Cao', 'Leszek Rutkowski']	2020-07-13	null	null	null	null	['content-based-image-retrieval']	['computer-vision']	[ 6.08043857e-02 -2.91469783e-01 -1.99390665e-01 -3.65879029e-01 -1.38308123e-01 -2.83881456e-01 7.16102183e-01 4.69076633e-01 -7.35367715e-01 4.69916493e-01 -1.92454234e-02 3.27725291e-01 -4.16602463e-01 -1.10366929e+00 -6.13045752e-01 -7.06749260e-01 -9.51503739e-02 2.98980493e-02 3.50502253e-01 -3.45983595e-01 4.42759037e-01 8.16246331e-01 -2.04407740e+00 3.95169705e-01 3.88225228e-01 1.47881079e+00 3.04714829e-01 2.97431856e-01 -3.77200842e-01 9.71837759e-01 -5.13467789e-01 1.25310078e-01 2.35246301e-01 -2.04578206e-01 -6.62016153e-01 -1.47861108e-01 3.13552171e-01 -3.52747411e-01 -5.95129430e-01 1.17123425e+00 3.28697950e-01 1.54278129e-01 8.05510700e-01 -1.02194607e+00 -4.51635987e-01 2.83756018e-01 -2.24015508e-02 1.03913642e-01 8.73844549e-02 -4.88364071e-01 9.67214286e-01 -8.76290023e-01 7.26399899e-01 1.08878958e+00 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098c90c7-03c2-4f66-abcb-226d98fb7994	the-finsim-2020-shared-task-learning-semantic	null	null	https://aclanthology.org/2020.finnlp-1.13	https://aclanthology.org/2020.finnlp-1.13.pdf	The FinSim 2020 Shared Task: Learning Semantic Representations for the Financial Domain	null	['Dialekti Valsamou-Stanislawski', 'Virginie Mouilleron', 'Youness Mansar', 'Ismail El Maarouf']	null	null	null	null	finnlp-coling-2020-1	['learning-semantic-representations']	['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 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 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c1f96225-fae3-4e66-93bd-b499f37ad450	privacy-attacks-against-biometric-models-with	2209.11020	null	https://arxiv.org/abs/2209.11020v1	https://arxiv.org/pdf/2209.11020v1.pdf	Privacy Attacks Against Biometric Models with Fewer Samples: Incorporating the Output of Multiple Models	Authentication systems are vulnerable to model inversion attacks where an adversary is able to approximate the inverse of a target machine learning model. Biometric models are a prime candidate for this type of attack. This is because inverting a biometric model allows the attacker to produce a realistic biometric input to spoof biometric authentication systems. One of the main constraints in conducting a successful model inversion attack is the amount of training data required. In this work, we focus on iris and facial biometric systems and propose a new technique that drastically reduces the amount of training data necessary. By leveraging the output of multiple models, we are able to conduct model inversion attacks with 1/10th the training set size of Ahmad and Fuller (IJCB 2020) for iris data and 1/1000th the training set size of Mai et al. (Pattern Analysis and Machine Intelligence 2019) for facial data. We denote our new attack technique as structured random with alignment loss. Our attacks are black-box, requiring no knowledge of the weights of the target neural network, only the dimension, and values of the output vector. To show the versatility of the alignment loss, we apply our attack framework to the task of membership inference (Shokri et al., IEEE S&P 2017) on biometric data. For the iris, membership inference attack against classification networks improves from 52% to 62% accuracy.	['Kaleel Mahmood', 'Benjamin Fuller', 'Sohaib Ahmad']	2022-09-22	null	null	null	null	['inference-attack', 'membership-inference-attack']	['adversarial', 'computer-vision']	[ 8.00842822e-01 1.74921945e-01 -1.08006135e-01 -1.22891091e-01 -1.87320039e-01 -8.24914336e-01 4.11819279e-01 -1.91883907e-01 -5.71369171e-01 4.53222305e-01 -6.20279491e-01 -6.43415570e-01 -7.81160593e-02 -8.25636089e-01 -7.99548507e-01 -6.22549236e-01 3.04167233e-02 3.38691622e-01 -1.99343458e-01 -8.78044143e-02 2.75512785e-01 8.79847646e-01 -1.32867670e+00 1.81052729e-01 5.45500040e-01 1.07635963e+00 -7.94936836e-01 8.99401963e-01 3.13206077e-01 4.49516475e-02 -6.59841597e-01 -7.98330069e-01 6.92914724e-01 -3.51853848e-01 -7.37450302e-01 -3.38051051e-01 7.14594483e-01 -2.59839177e-01 -2.77643621e-01 1.35143650e+00 5.65532982e-01 -3.21884364e-01 6.96291625e-01 -1.52375078e+00 4.83916029e-02 6.10085249e-01 -5.67355514e-01 -2.58619815e-01 4.09761339e-01 1.31790519e-01 5.04601419e-01 -7.49258399e-01 1.33555219e-01 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402c742f-c7e9-4386-83dd-1b3142e70a03	a-pose-only-solution-to-visual-reconstruction	2103.01530	null	https://arxiv.org/abs/2103.01530v3	https://arxiv.org/pdf/2103.01530v3.pdf	A Pose-only Solution to Visual Reconstruction and Navigation	Visual navigation and three-dimensional (3D) scene reconstruction are essential for robotics to interact with the surrounding environment. Large-scale scenes and critical camera motions are great challenges facing the research community to achieve this goal. We raised a pose-only imaging geometry framework and algorithms that can help solve these challenges. The representation is a linear function of camera global translations, which allows for efficient and robust camera motion estimation. As a result, the spatial feature coordinates can be analytically reconstructed and do not require nonlinear optimization. Experiments demonstrate that the computational efficiency of recovering the scene and associated camera poses is significantly improved by 2-4 orders of magnitude. This solution might be promising to unlock real-time 3D visual computing in many forefront applications.	['Dewen Hu', 'Wenxian Yu', 'Yuanxin Wu', 'Lilian Zhang', 'Qi Cai']	2021-03-02	null	null	null	null	['3d-scene-reconstruction']	['computer-vision']	[-1.19919600e-02 -3.53226870e-01 -1.61512401e-02 -7.96060637e-02 -3.24670434e-01 -8.88200879e-01 4.47517812e-01 -4.82576191e-01 -5.05718470e-01 3.68461192e-01 -3.23052257e-01 -3.43366504e-01 2.42433734e-02 -3.80614042e-01 -5.83692014e-01 -6.79547906e-01 2.61697233e-01 3.93046826e-01 4.53766376e-01 -1.33528590e-01 5.54165423e-01 9.68393922e-01 -1.40235782e+00 -5.19595146e-01 4.63850796e-01 7.77388096e-01 7.99090505e-01 7.70727217e-01 5.03351726e-02 5.53401113e-01 5.05576469e-02 1.71608761e-01 5.34197927e-01 -1.12037554e-01 -3.94653529e-01 4.81596977e-01 4.24533546e-01 -6.06906533e-01 -3.98135275e-01 1.17368662e+00 3.34467173e-01 1.60476863e-01 3.94228041e-01 -8.73036921e-01 1.75801700e-03 -4.76273358e-01 -7.44846642e-01 -2.11629570e-01 7.01076031e-01 1.34164512e-01 4.40370381e-01 -1.06939554e+00 7.90599644e-01 9.74164426e-01 5.46788156e-01 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40ed8f31-a5a9-4045-8483-47b0c86b22f5	choquet-integral-and-coalition-game-based	null	null	https://ieeexplore.ieee.org/document/9534669	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9534669	Choquet Integral and Coalition Game-based Ensemble of Deep Learning Models for COVID-19 Screening from Chest X-ray Images	Under the present circumstances, when we are still under the threat of different strains of coronavirus, and since the most widely used method for COVID-19 detection, RT-PCR is a tedious and time-consuming manual procedure with poor precision, the application of Artificial Intelligence (AI) and Computer-Aided Diagnosis (CAD) is inevitable. In this work, we have analyzed Chest X-ray (CXR) images for the detection of the coronavirus. The primary agenda of this proposed research study is to leverage the classification performance of the deep learning models using ensemble learning. Many papers have proposed different ensemble learning techniques in this field, some methods using aggregation functions like Weighted Arithmetic Mean (WAM) among others. However, none of these methods take into consideration the decisions that subsets of the classifiers take. In this paper, we have applied Choquet integral for ensemble and propose a novel method for the evaluation of fuzzy measures using Coalition Game Theory, Information Theory, and Lambda fuzzy approximation. Three different sets of Fuzzy Measures are calculated using three different weighting schemes along with information theory and coalition game theory. Using these three sets of fuzzy measures three Choquet Integrals are calculated and their decisions are finally combined. We have created a database by combining several image repositories developed recently. Impressive results on the newly developed dataset and the challenging COVIDx dataset support the efficacy and robustness of the proposed method. To the best of our knowledge, our experimental results outperform many recently proposed methods. Source code available at https://github.com/subhankar01/Covid-Chestxray-lambda-fuzzy	['Ram Sarkar', 'Jin Hee Yoon Zong Woo Geem', 'Subhankar Sen', 'Pratik Bhowal']	2021-09-09	null	null	null	ieee-journal-of-biomedical-and-health-1	['covid-19-detection']	['medical']	[ 2.26877164e-02 -4.33354616e-01 1.24217525e-01 -2.72124618e-01 -4.69597131e-01 -6.35668874e-01 2.68698603e-01 4.04915810e-01 -5.55492520e-01 7.66551852e-01 -3.42269987e-01 -4.06104624e-01 -7.63832092e-01 -9.75396216e-01 -2.30395868e-01 -7.73174047e-01 -2.06621036e-01 7.11191714e-01 -1.80753946e-01 -3.88798773e-01 3.20008695e-01 4.15376395e-01 -1.77184308e+00 4.84929323e-01 1.31287205e+00 1.26472247e+00 -1.67472884e-01 7.62799561e-01 2.00249806e-01 8.78683567e-01 -3.84043783e-01 -5.09894669e-01 4.16754454e-01 -2.20820576e-01 -4.65837359e-01 -3.03394318e-01 1.02105560e-02 -1.99211076e-01 2.44000465e-01 1.02413905e+00 6.41989887e-01 1.42617032e-01 1.16171753e+00 -1.30124319e+00 -3.73293251e-01 3.65393460e-01 -6.30527854e-01 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ea61d6dd-cf1b-4f87-8fbd-34561896b89f	bayesian-aggregation-improves-traditional	2004.03468	null	https://arxiv.org/abs/2004.03468v1	https://arxiv.org/pdf/2004.03468v1.pdf	Bayesian aggregation improves traditional single image crop classification approaches	Machine learning (ML) methods and neural networks (NN) are widely implemented for crop types recognition and classification based on satellite images. However, most of these studies use several multi-temporal images which could be inapplicable for cloudy regions. We present a comparison between the classical ML approaches and U-Net NN for classifying crops with a single satellite image. The results show the advantages of using field-wise classification over pixel-wise approach. We first used a Bayesian aggregation for field-wise classification and improved on 1.5% results between majority voting aggregation. The best result for single satellite image crop classification is achieved for gradient boosting with an overall accuracy of 77.4% and macro F1-score 0.66.	['Maria Pukalchik', 'Anna Petrovskaia', 'Mikhail Gasanov', 'Raghavendra Belur Jana', 'Ivan Oseledets', 'Ivan Matvienko']	2020-04-07	null	null	null	null	['crop-classification']	['miscellaneous']	[ 2.65088737e-01 -2.31183872e-01 -4.00983661e-01 -4.18070495e-01 -6.21557951e-01 -5.99497855e-01 5.37488580e-01 6.07557833e-01 -4.09323007e-01 1.09547472e+00 -4.55448419e-01 -7.94248819e-01 -1.41117647e-01 -1.34482455e+00 -7.45360136e-01 -1.05250919e+00 -3.22020113e-01 -3.54823731e-02 1.72054961e-01 -7.30915442e-02 1.43363345e-02 4.90897268e-01 -1.82214677e+00 5.53921819e-01 1.04769301e+00 1.39132309e+00 6.94487929e-01 9.42052245e-01 -1.48233116e-01 5.76536298e-01 -4.85585213e-01 2.88372666e-01 3.61723483e-01 1.01437457e-01 -4.61711556e-01 -1.14099771e-01 6.80415273e-01 -3.95176888e-01 3.72088581e-01 1.16667855e+00 1.75863832e-01 -1.77384213e-01 8.52263570e-01 -1.12465322e+00 -5.38624525e-01 8.01075041e-01 -7.82992959e-01 1.07013714e-02 -2.58289009e-01 1.38183888e-02 6.72245324e-01 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c1eb630f-a1ec-47cf-8a19-b52fce260916	adaptive-knowledge-distillation-between-text	2303.03600	null	https://arxiv.org/abs/2303.03600v1	https://arxiv.org/pdf/2303.03600v1.pdf	Adaptive Knowledge Distillation between Text and Speech Pre-trained Models	Learning on a massive amount of speech corpus leads to the recent success of many self-supervised speech models. With knowledge distillation, these models may also benefit from the knowledge encoded by language models that are pre-trained on rich sources of texts. The distillation process, however, is challenging due to the modal disparity between textual and speech embedding spaces. This paper studies metric-based distillation to align the embedding space of text and speech with only a small amount of data without modifying the model structure. Since the semantic and granularity gap between text and speech has been omitted in literature, which impairs the distillation, we propose the Prior-informed Adaptive knowledge Distillation (PAD) that adaptively leverages text/speech units of variable granularity and prior distributions to achieve better global and local alignments between text and speech pre-trained models. We evaluate on three spoken language understanding benchmarks to show that PAD is more effective in transferring linguistic knowledge than other metric-based distillation approaches.	['Erik Cambria', 'Bin Ma', 'Chong Zhang', 'Trung Hieu Nguyen', 'Han Lei', 'Dianwen Ng', 'Qian Chen', 'Wen Wang', 'Yukun Ma', 'Jinjie Ni']	2023-03-07	null	null	null	null	['spoken-language-understanding', 'spoken-language-understanding']	['natural-language-processing', 'speech']	[ 1.74919903e-01 6.42563403e-01 -1.34237483e-01 -7.06554174e-01 -7.37057686e-01 -5.59678972e-01 9.11262095e-01 2.41913825e-01 -6.58500016e-01 5.64205468e-01 8.86917889e-01 -4.12202984e-01 9.46138576e-02 -6.03359878e-01 -6.22008920e-01 -2.94489264e-01 1.82045132e-01 8.18887413e-01 9.46998820e-02 -3.19614321e-01 -6.59444556e-02 -4.95483242e-02 -1.27044439e+00 2.78769881e-01 1.05496407e+00 6.79796755e-01 4.06888336e-01 7.30380714e-01 -8.79721582e-01 5.58097661e-01 -5.14325380e-01 -3.45542192e-01 2.12737918e-01 -6.35021389e-01 -1.11363232e+00 -2.50726700e-01 4.38566864e-01 -3.20679992e-01 -5.86088181e-01 9.55228269e-01 5.76970398e-01 3.80964905e-01 6.38648331e-01 -1.00392830e+00 -1.04456568e+00 1.36939538e+00 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76de74aa-a8f3-4de9-b3b5-03aa37d77a16	s3e-gnn-sparse-spatial-scene-embedding-with	2205.05861	null	https://arxiv.org/abs/2205.05861v1	https://arxiv.org/pdf/2205.05861v1.pdf	S3E-GNN: Sparse Spatial Scene Embedding with Graph Neural Networks for Camera Relocalization	Camera relocalization is the key component of simultaneous localization and mapping (SLAM) systems. This paper proposes a learning-based approach, named Sparse Spatial Scene Embedding with Graph Neural Networks (S3E-GNN), as an end-to-end framework for efficient and robust camera relocalization. S3E-GNN consists of two modules. In the encoding module, a trained S3E network encodes RGB images into embedding codes to implicitly represent spatial and semantic embedding code. With embedding codes and the associated poses obtained from a SLAM system, each image is represented as a graph node in a pose graph. In the GNN query module, the pose graph is transformed to form a embedding-aggregated reference graph for camera relocalization. We collect various scene datasets in the challenging environments to perform experiments. Our results demonstrate that S3E-GNN method outperforms the traditional Bag-of-words (BoW) for camera relocalization due to learning-based embedding and GNN powered scene matching mechanism.	['Tao Sun', 'Lige Liu', 'Yuan Chen', 'Xinyu Jiang', 'Ran Cheng']	2022-05-12	null	null	null	null	['camera-relocalization']	['computer-vision']	[ 5.49056269e-02 -1.26007468e-01 -2.08463416e-01 -4.97126758e-01 -5.99563956e-01 -4.32620823e-01 5.34184635e-01 -5.98508678e-02 -3.46006304e-01 2.57097930e-01 4.20999348e-01 -9.39158127e-02 -2.35122830e-01 -7.68683136e-01 -9.35408771e-01 -3.55630606e-01 8.68798643e-02 3.44816715e-01 1.27148315e-01 -2.63265725e-02 2.39736438e-01 6.26239955e-01 -1.13774955e+00 -3.04538906e-01 3.45063269e-01 8.38938296e-01 5.92069387e-01 6.81684852e-01 -2.28924215e-01 9.86914992e-01 -4.88488656e-03 -5.25636319e-03 2.57160217e-01 -3.27579319e-01 -3.97384435e-01 -1.13007776e-01 7.66783476e-01 -5.37924618e-02 -1.15063334e+00 1.29159415e+00 4.68636006e-01 4.14516747e-01 2.50978053e-01 -1.65951061e+00 -1.01729298e+00 2.96679914e-01 -4.09534365e-01 -2.46420383e-01 7.65692949e-01 -2.11672500e-01 8.85180950e-01 -1.09243536e+00 7.29074717e-01 1.34221363e+00 8.74331474e-01 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6436d44d-eea3-4b65-b53f-a489bc12ed51	benchmarking-large-language-model	2306.16793	null	https://arxiv.org/abs/2306.16793v1	https://arxiv.org/pdf/2306.16793v1.pdf	Benchmarking Large Language Model Capabilities for Conditional Generation	Pre-trained large language models (PLMs) underlie most new developments in natural language processing. They have shifted the field from application-specific model pipelines to a single model that is adapted to a wide range of tasks. Autoregressive PLMs like GPT-3 or PaLM, alongside techniques like few-shot learning, have additionally shifted the output modality to generation instead of classification or regression. Despite their ubiquitous use, the generation quality of language models is rarely evaluated when these models are introduced. Additionally, it is unclear how existing generation tasks--while they can be used to compare systems at a high level--relate to the real world use cases for which people have been adopting them. In this work, we discuss how to adapt existing application-specific generation benchmarks to PLMs and provide an in-depth, empirical study of the limitations and capabilities of PLMs in natural language generation tasks along dimensions such as scale, architecture, input and output language. Our results show that PLMs differ in their applicability to different data regimes and their generalization to multiple languages and inform which PLMs to use for a given generation task setup. We share best practices to be taken into consideration when benchmarking generation capabilities during the development of upcoming PLMs.	['Sebastian Gehrmann', 'Priyanka Agrawal', 'Joshua Maynez']	2023-06-29	null	null	null	null	['few-shot-learning', 'benchmarking', 'text-generation', 'benchmarking']	['methodology', 'miscellaneous', 'natural-language-processing', 'robots']	[ 3.17677617e-01 7.85486922e-02 -1.40651092e-02 -2.39447743e-01 -8.82344127e-01 -6.51967406e-01 1.22858453e+00 1.65257931e-01 -3.22922468e-01 4.84681278e-01 3.66435677e-01 -3.48502994e-01 8.98054168e-02 -9.33774650e-01 -2.97230124e-01 -2.15641961e-01 2.66628832e-01 8.06542337e-01 2.41898149e-02 -6.52862549e-01 9.62401405e-02 3.52147609e-01 -1.62875426e+00 5.46311378e-01 6.42942786e-01 5.02580047e-01 1.88501745e-01 9.80570793e-01 -5.35619199e-01 5.89367747e-01 -9.75657165e-01 -3.10902536e-01 1.39968023e-01 -5.23250639e-01 -7.46544302e-01 -9.98732448e-02 3.57927620e-01 -8.46532136e-02 2.67656684e-01 5.43081045e-01 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99620f25-8d39-4cad-9d26-c73f58d49acf	phrase-based-unsupervised-machine-translation	null	null	https://aclanthology.org/W18-6407	https://aclanthology.org/W18-6407.pdf	Phrase-based Unsupervised Machine Translation with Compositional Phrase Embeddings	This paper describes the University of Tartu{'}s submission to the unsupervised machine translation track of WMT18 news translation shared task. We build several baseline translation systems for both directions of the English-Estonian language pair using monolingual data only; the systems belong to the phrase-based unsupervised machine translation paradigm where we experimented with phrase lengths of up to 3. As a main contribution, we performed a set of standalone experiments with compositional phrase embeddings as a substitute for phrases as individual vocabulary entries. Results show that reasonable n-gram vectors can be obtained by simply summing up individual word vectors which retains or improves the performance of phrase-based unsupervised machine tranlation systems while avoiding limitations of atomic phrase vectors.	['Andre T{\\"a}ttar', 'Maksym Del', 'Mark Fishel']	2018-10-01	null	null	null	ws-2018-10	['unsupervised-machine-translation']	['natural-language-processing']	[ 3.52085233e-01 3.30861062e-02 -5.93825579e-01 -2.74181634e-01 -1.18959367e+00 -8.79691303e-01 1.03272903e+00 -1.91920670e-03 -7.88019538e-01 1.16459882e+00 6.22249186e-01 -1.05738962e+00 2.41375104e-01 -2.19268754e-01 -4.97202814e-01 -5.48237145e-01 3.00659567e-01 1.17335153e+00 1.57938693e-02 -6.46776319e-01 -2.31470726e-02 2.07162008e-01 -6.12188697e-01 3.48870635e-01 6.81899369e-01 4.71625999e-02 -7.32719749e-02 7.63970017e-01 -1.80860370e-01 -4.38586175e-02 -2.43194714e-01 -7.60151267e-01 6.11205757e-01 -4.91471976e-01 -9.04705584e-01 -1.38725825e-02 3.62820596e-01 -2.12301731e-01 -3.12250137e-01 7.88315356e-01 6.59363806e-01 -7.72999600e-02 8.13323021e-01 -8.10267925e-01 -8.89681697e-01 9.92745638e-01 -2.47382596e-01 6.11502051e-01 3.10099542e-01 1.60283304e-03 1.63651454e+00 -1.49817467e+00 8.20519388e-01 9.89195406e-01 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b162552c-85ca-4709-bea6-251761fed970	soliton-crystal-kerr-microcombs-for-high	2101.12356	null	https://arxiv.org/abs/2101.12356v1	https://arxiv.org/pdf/2101.12356v1.pdf	Soliton crystal Kerr microcombs for high-speed, scalable optical neural networks at 10 GigaOPs/s	Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a new approach to ONNs based on integrated Kerr micro-combs that is programmable, highly scalable and capable of reaching ultra-high speeds, demonstrating the building block of the ONN, a single neuron perceptron, by mapping synapses onto 49 wavelengths to achieve a single-unit throughput of 11.9 Giga-OPS at 8 bits per OP, or 95.2 Gbps. We test the perceptron on handwritten-digit recognition and cancer-cell detection, achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off the shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing.	['David J. Moss', 'Mengxi Tan', 'Xingyuan Xu']	2021-01-29	null	null	null	null	['handwritten-digit-recognition']	['computer-vision']	[ 3.68386358e-01 6.67106882e-02 4.05198820e-02 2.79645860e-01 3.37989181e-01 -2.26080000e-01 1.39431641e-01 -2.44558603e-01 -9.95871723e-01 7.29091167e-01 -7.64286101e-01 -6.24620318e-01 -1.00003324e-01 -8.68419707e-01 -3.81656080e-01 -9.48447406e-01 -5.66836447e-02 1.68838859e-01 3.02065551e-01 1.81768704e-02 3.22113186e-01 6.63196862e-01 -1.44866014e+00 -5.41216470e-02 4.38396662e-01 1.40801597e+00 1.41435847e-01 1.04765022e+00 3.66272002e-01 6.41318977e-01 -7.95195043e-01 -7.82398954e-02 3.36347163e-01 1.08477352e-02 -1.18890658e-01 -6.41830802e-01 4.89789575e-01 -3.80662948e-01 -9.89405990e-01 9.96128857e-01 5.51522434e-01 -4.62890565e-01 3.66460353e-01 -9.78558540e-01 -8.04516435e-01 5.14168680e-01 -1.93349764e-01 4.64617670e-01 -2.69401610e-01 6.63560390e-01 7.36923039e-01 -3.18813622e-01 4.51670378e-01 5.98502398e-01 3.38794917e-01 7.89931417e-01 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05ef5169-cf94-4820-9f09-2a6c338d01ec	a-simple-riemannian-manifold-network-for	1805.10628	null	http://arxiv.org/abs/1805.10628v2	http://arxiv.org/pdf/1805.10628v2.pdf	A Simple Riemannian Manifold Network for Image Set Classification	In the domain of image-set based classification, a considerable advance has been made by representing original image sets as covariance matrices which typical lie in a Riemannian manifold. Specifically, it is a Symmetric Positive Definite (SPD) manifold. Traditional manifold learning methods inevitably have the property of high computational complexity or weak performance of the feature representation. In order to overcome these limitations, we propose a very simple Riemannian manifold network for image set classification. Inspired by deep learning architectures, we design a fully connected layer to generate more novel, more powerful SPD matrices. However we exploit the rectifying layer to prevent the input SPD matrices from being singular. We also introduce a non-linear learning of the proposed network with an innovative objective function. Furthermore we devise a pooling layer to further reduce the redundancy of the input SPD matrices, and the log-map layer to project the SPD manifold to the Euclidean space. For learning the connection weights between the input layer and the fully connected layer, we use Two-directional two-dimensional Principal Component Analysis ((2D)2PCA) algorithm. The proposed Riemannian manifold network (RieMNet) avoids complex computing and can be built and trained extremely easy and efficient. We have also developed a deep version of RieMNet, named as DRieMNet. The proposed RieMNet and DRieMNet are evaluated on three tasks: video-based face recognition, set-based object categorization, and set-based cell identification. Extensive experimental results show the superiority of our method over the state-of-the-art.	['Xiao-Jun Wu', 'Rui Wang', 'Josef Kittler']	2018-05-27	null	null	null	null	['object-categorization']	['computer-vision']	[ 8.34086090e-02 -2.19738513e-01 2.22112820e-01 -4.34051722e-01 -2.26100072e-01 -1.64805219e-01 3.89288574e-01 -5.97241402e-01 -5.03709733e-01 3.18671525e-01 -2.80519962e-01 -1.43790975e-01 -3.97394359e-01 -7.07935214e-01 -6.32525444e-01 -1.02421916e+00 -1.63276970e-01 -5.40339239e-02 8.83266795e-04 -8.48293826e-02 1.75455943e-01 6.63703918e-01 -1.43417537e+00 -4.57992981e-04 7.01445580e-01 9.72096801e-01 1.86335310e-01 3.17799658e-01 1.03394888e-01 4.95195240e-01 -2.11073905e-01 -1.85921773e-01 2.86299974e-01 -4.13795769e-01 -5.99613786e-01 5.86380005e-01 1.53805017e-01 -1.26869470e-01 -5.56644619e-01 1.23345733e+00 4.33472663e-01 1.05356835e-01 9.13986504e-01 -1.35636580e+00 -8.84814024e-01 2.44523525e-01 -7.34732628e-01 2.19447583e-01 -1.14388458e-01 -4.57170792e-03 6.27910733e-01 -1.30020165e+00 4.23553079e-01 1.34280264e+00 3.81912023e-01 6.39957666e-01 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60f1742f-d8a8-4ca3-bff1-85b6478fc075	llcaps-learning-to-illuminate-low-light	2307.02452	null	https://arxiv.org/abs/2307.02452v1	https://arxiv.org/pdf/2307.02452v1.pdf	LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion	Wireless capsule endoscopy (WCE) is a painless and non-invasive diagnostic tool for gastrointestinal (GI) diseases. However, due to GI anatomical constraints and hardware manufacturing limitations, WCE vision signals may suffer from insufficient illumination, leading to a complicated screening and examination procedure. Deep learning-based low-light image enhancement (LLIE) in the medical field gradually attracts researchers. Given the exuberant development of the denoising diffusion probabilistic model (DDPM) in computer vision, we introduce a WCE LLIE framework based on the multi-scale convolutional neural network (CNN) and reverse diffusion process. The multi-scale design allows models to preserve high-resolution representation and context information from low-resolution, while the curved wavelet attention (CWA) block is proposed for high-frequency and local feature learning. Furthermore, we combine the reverse diffusion procedure to further optimize the shallow output and generate the most realistic image. The proposed method is compared with ten state-of-the-art (SOTA) LLIE methods and significantly outperforms quantitatively and qualitatively. The superior performance on GI disease segmentation further demonstrates the clinical potential of our proposed model. Our code is publicly accessible.	['Hongliang Ren', 'Mobarakol Islam', 'An Wang', 'Yanan Wu', 'Tong Chen', 'Long Bai']	2023-07-05	null	null	null	null	['image-enhancement', 'low-light-image-enhancement']	['computer-vision', 'computer-vision']	[ 4.83299233e-02 -8.20122883e-02 -3.04509103e-02 2.70021390e-02 -6.08484089e-01 -1.94458216e-01 -1.87419821e-02 9.98932123e-03 -5.11236787e-01 2.37213075e-01 2.51951545e-01 -7.63583034e-02 -2.27374107e-01 -6.63498223e-01 -5.99481940e-01 -1.10794401e+00 -2.41201490e-01 -3.61497432e-01 2.48050615e-01 3.56964730e-02 -2.57354319e-01 3.31390172e-01 -9.02411461e-01 3.93997580e-01 1.18517041e+00 1.14259636e+00 6.91870272e-01 6.77556932e-01 2.69069165e-01 5.01925170e-01 -2.85557896e-01 -1.53119326e-01 1.45370260e-01 -5.81385136e-01 -4.07033116e-01 7.79616609e-02 -1.50430109e-02 -3.41538936e-01 -5.02591908e-01 1.31490898e+00 7.88561523e-01 -7.66258240e-02 3.32348615e-01 -4.79878336e-01 -1.00746918e+00 1.70551211e-01 -6.82587862e-01 3.32654595e-01 1.54280439e-01 1.43962234e-01 4.15560663e-01 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e80d22bf-e446-4e85-be43-afe973f0682a	action-recognition-in-untrimmed-videos-with	1908.04353	null	https://arxiv.org/abs/1908.04353v2	https://arxiv.org/pdf/1908.04353v2.pdf	Action Recognition in Untrimmed Videos with Composite Self-Attention Two-Stream Framework	With the rapid development of deep learning algorithms, action recognition in video has achieved many important research results. One issue in action recognition, Zero-Shot Action Recognition (ZSAR), has recently attracted considerable attention, which classify new categories without any positive examples. Another difficulty in action recognition is that untrimmed data may seriously affect model performance. We propose a composite two-stream framework with a pre-trained model. Our proposed framework includes a classifier branch and a composite feature branch. The graph network model is adopted in each of the two branches, which effectively improves the feature extraction and reasoning ability of the framework. In the composite feature branch, a 3-channel self-attention models are constructed to weight each frame in the video and give more attention to the key frames. Each self-attention models channel outputs a set of attention weights to focus on a particular aspect of the video, and a set of attention weights corresponds to a one-dimensional vector. The 3-channel self-attention models can evaluate key frames from multiple aspects, and the output sets of attention weight vectors form an attention matrix, which effectively enhances the attention of key frames with strong correlation of action. This model can implement action recognition under zero-shot conditions, and has good recognition performance for untrimmed video data. Experimental results on relevant data sets confirm the validity of our model.	['Dong Cao', 'HaiBo Chen', 'Lisha Xu']	2019-08-04	null	null	null	null	['zero-shot-action-recognition']	['computer-vision']	[ 4.83784407e-01 -3.18933874e-01 -3.85438740e-01 -2.22993214e-02 -3.39961022e-01 2.30987325e-01 2.01227382e-01 -3.89261931e-01 -2.04799861e-01 1.38501629e-01 4.94671643e-01 2.98359036e-01 -4.35251482e-02 -9.22987461e-01 -2.36617148e-01 -7.73395896e-01 1.44877017e-01 -1.94385827e-01 6.17848396e-01 1.34157734e-02 2.63730556e-01 3.69658530e-01 -1.66530454e+00 6.15313590e-01 5.34074306e-01 1.30854440e+00 1.67719081e-01 8.84867609e-01 -2.48769253e-01 1.48412573e+00 -6.76603198e-01 -6.55577853e-02 -7.06514195e-02 -6.39669657e-01 -5.30224323e-01 6.26954675e-01 3.07691302e-02 -5.58001876e-01 -8.60422730e-01 1.11077678e+00 4.93574589e-01 2.19075322e-01 4.31419134e-01 -1.53143620e+00 -8.76702726e-01 2.85570532e-01 -6.13442719e-01 6.85902238e-01 4.00317878e-01 3.43460172e-01 8.59270811e-01 -8.76828074e-01 2.04914600e-01 1.49093676e+00 3.03550452e-01 4.47541416e-01 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-1.06398046e+00 -7.08683491e-01 5.02325416e-01 -5.07503450e-02 -8.73674512e-01 -1.86678752e-01 2.99822241e-01]	[8.415014266967773, 0.7645870447158813]
f932fc96-9254-4cbc-896a-a403850a2617	changing-data-sources-in-the-age-of-machine	2306.04338	null	https://arxiv.org/abs/2306.04338v1	https://arxiv.org/pdf/2306.04338v1.pdf	Changing Data Sources in the Age of Machine Learning for Official Statistics	Data science has become increasingly essential for the production of official statistics, as it enables the automated collection, processing, and analysis of large amounts of data. With such data science practices in place, it enables more timely, more insightful and more flexible reporting. However, the quality and integrity of data-science-driven statistics rely on the accuracy and reliability of the data sources and the machine learning techniques that support them. In particular, changes in data sources are inevitable to occur and pose significant risks that are crucial to address in the context of machine learning for official statistics. This paper gives an overview of the main risks, liabilities, and uncertainties associated with changing data sources in the context of machine learning for official statistics. We provide a checklist of the most prevalent origins and causes of changing data sources; not only on a technical level but also regarding ownership, ethics, regulation, and public perception. Next, we highlight the repercussions of changing data sources on statistical reporting. These include technical effects such as concept drift, bias, availability, validity, accuracy and completeness, but also the neutrality and potential discontinuation of the statistical offering. We offer a few important precautionary measures, such as enhancing robustness in both data sourcing and statistical techniques, and thorough monitoring. In doing so, machine learning-based official statistics can maintain integrity, reliability, consistency, and relevance in policy-making, decision-making, and public discourse.	['Michael Reusens', 'Cedric De Boom']	2023-06-07	null	null	null	null	['ethics']	['miscellaneous']	[-6.33525178e-02 -1.23685226e-03 -5.41201949e-01 -3.81072044e-01 -7.38729417e-01 -8.37052107e-01 6.38395905e-01 9.80329752e-01 -7.66209900e-01 7.97563851e-01 8.10115755e-01 -7.48571396e-01 -3.17844540e-01 -6.39378726e-01 -8.10914278e-01 -4.67686236e-01 2.98714280e-01 1.82375088e-02 -3.71869415e-01 7.05759302e-02 4.96298850e-01 4.97890204e-01 -1.04053068e+00 -5.14418259e-02 8.76744509e-01 8.26852798e-01 -4.84949797e-01 1.63317978e-01 -2.77342439e-01 1.01241136e+00 -7.62564480e-01 -6.37135088e-01 1.32964328e-01 2.28944197e-01 -3.85520905e-01 -2.89560586e-01 1.32055938e-01 -4.24626499e-01 3.50520879e-01 8.38106990e-01 1.36668548e-01 -4.01935279e-01 4.71665710e-01 -1.02541125e+00 -9.05343056e-01 4.31950122e-01 -3.43674064e-01 2.95733094e-01 1.34708613e-01 3.64418626e-01 6.18719399e-01 -6.16618454e-01 4.92181301e-01 8.35919142e-01 5.36871791e-01 -2.04493217e-02 -1.07924843e+00 -7.96407819e-01 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88aadef2-c319-413b-b330-d458bbcc6e93	who-is-mistaken	1612.01175	null	http://arxiv.org/abs/1612.01175v2	http://arxiv.org/pdf/1612.01175v2.pdf	Who is Mistaken?	Recognizing when people have false beliefs is crucial for understanding their actions. We introduce the novel problem of identifying when people in abstract scenes have incorrect beliefs. We present a dataset of scenes, each visually depicting an 8-frame story in which a character has a mistaken belief. We then create a representation of characters' beliefs for two tasks in human action understanding: predicting who is mistaken, and when they are mistaken. Experiments suggest that our method for identifying mistaken characters performs better on these tasks than simple baselines. Diagnostics on our model suggest it learns important cues for recognizing mistaken beliefs, such as gaze. We believe models of people's beliefs will have many applications in action understanding, robotics, and healthcare.	['Carl Vondrick', 'Antonio Torralba', 'Benjamin Eysenbach']	2016-12-04	null	null	null	null	['action-understanding']	['computer-vision']	[ 4.11318183e-01 5.20174325e-01 -1.78046346e-01 -7.34863579e-01 -3.52801144e-01 -3.41811568e-01 6.99477732e-01 3.41448039e-01 -3.65805745e-01 6.13993227e-01 8.01416278e-01 -1.29081517e-01 5.44814050e-01 -2.24019855e-01 -8.11428368e-01 -4.46070313e-01 4.21350032e-01 4.51172978e-01 1.41000614e-01 2.25444585e-01 1.04776454e+00 5.36198057e-02 -1.64565790e+00 1.15980291e+00 3.11210543e-01 5.98208070e-01 -1.39471054e-01 8.55590224e-01 1.37329862e-01 1.98303556e+00 -8.35968971e-01 -7.98089802e-01 -3.30617845e-01 -5.10173142e-01 -1.26248145e+00 3.13939661e-01 1.19802189e+00 -9.80259299e-01 7.13845342e-03 1.24322701e+00 -1.35899350e-01 -1.62612364e-01 7.35048175e-01 -1.41655767e+00 -9.67156529e-01 8.65615547e-01 -4.48514104e-01 6.55615032e-01 9.70030308e-01 3.58874172e-01 6.94915652e-01 -6.05306625e-01 -3.09987329e-02 2.06461048e+00 6.68287098e-01 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-2.12882951e-01 -7.56297946e-01 -2.35202070e-02 9.47885931e-01 6.55465782e-01 -6.51256323e-01 -3.89431238e-01 2.69321620e-01]	[10.608596801757812, 1.4939615726470947]
2a762487-c27d-443a-8485-024643d53f44	detection-of-audio-video-synchronization	2104.10116	null	https://arxiv.org/abs/2104.10116v1	https://arxiv.org/pdf/2104.10116v1.pdf	Detection of Audio-Video Synchronization Errors Via Event Detection	We present a new method and a large-scale database to detect audio-video synchronization(A/V sync) errors in tennis videos. A deep network is trained to detect the visual signature of the tennis ball being hit by the racquet in the video stream. Another deep network is trained to detect the auditory signature of the same event in the audio stream. During evaluation, the audio stream is searched by the audio network for the audio event of the ball being hit. If the event is found in audio, the neighboring interval in video is searched for the corresponding visual signature. If the event is not found in the video stream but is found in the audio stream, A/V sync error is flagged. We developed a large-scaled database of 504,300 frames from 6 hours of videos of tennis events, simulated A/V sync errors, and found our method achieves high accuracy on the task.	['Zongyi Liu', 'Sriram Sethuraman', 'Hai Wei', 'Yongjun Wu', 'Joshua P. Ebenezer']	2021-04-20	null	null	null	null	['video-synchronization']	['computer-vision']	[ 7.17375726e-02 -5.81751108e-01 7.72101283e-02 1.89801186e-01 -6.66652799e-01 -4.76805240e-01 -8.50803405e-02 3.50595504e-01 -5.67459941e-01 3.26685816e-01 1.35096848e-01 2.33804360e-01 4.22380157e-02 -5.16376078e-01 -1.07101548e+00 -4.53512460e-01 -5.04739106e-01 1.77410528e-01 6.96529925e-01 5.53542785e-02 3.58283222e-01 5.03811121e-01 -1.72758400e+00 6.32874489e-01 -2.81813532e-01 1.44472659e+00 1.42803386e-01 1.12107062e+00 5.12212634e-01 1.14753473e+00 -1.23561156e+00 8.33709687e-02 3.92608315e-01 -5.56939185e-01 -4.55119312e-01 -2.87609637e-01 6.16275668e-01 -5.76739609e-01 -1.02115440e+00 8.65495086e-01 6.81139648e-01 2.46730998e-01 -1.05284341e-02 -1.71098149e+00 5.32995880e-01 3.85541260e-01 -3.69899303e-01 8.98897827e-01 9.28619087e-01 -4.43570875e-02 5.65685868e-01 -8.18396807e-01 9.32835937e-01 1.00704801e+00 7.54980564e-01 -5.38768359e-02 -7.35528708e-01 -1.12159622e+00 -2.79107213e-01 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dc5cdccc-9435-4ff9-abd6-33655f0f8da5	coordinate-translator-for-learning-deformable	2203.03626	null	https://arxiv.org/abs/2203.03626v2	https://arxiv.org/pdf/2203.03626v2.pdf	Coordinate Translator for Learning Deformable Medical Image Registration	The majority of deep learning (DL) based deformable image registration methods use convolutional neural networks (CNNs) to estimate displacement fields from pairs of moving and fixed images. This, however, requires the convolutional kernels in the CNN to not only extract intensity features from the inputs but also understand image coordinate systems. We argue that the latter task is challenging for traditional CNNs, limiting their performance in registration tasks. To tackle this problem, we first introduce Coordinate Translator, a differentiable module that identifies matched features between the fixed and moving image and outputs their coordinate correspondences without the need for training. It unloads the burden of understanding image coordinate systems for CNNs, allowing them to focus on feature extraction. We then propose a novel deformable registration network, im2grid, that uses multiple Coordinate Translator's with the hierarchical features extracted from a CNN encoder and outputs a deformation field in a coarse-to-fine fashion. We compared im2grid with the state-of-the-art DL and non-DL methods for unsupervised 3D magnetic resonance image registration. Our experiments show that im2grid outperforms these methods both qualitatively and quantitatively.	['Yuan Xue', 'Aaron Carass', 'Jerry L. Prince', 'Shuo Han', 'Lianrui Zuo', 'Yihao Liu']	2022-03-05	null	null	null	null	['deformable-medical-image-registration']	['medical']	[ 1.60606936e-01 1.15734532e-01 3.18428352e-02 -5.44294953e-01 -9.04766917e-01 -5.00163496e-01 4.24405217e-01 -6.04123697e-02 -6.87277734e-01 3.91791403e-01 8.70910883e-02 1.30155593e-01 -7.64114112e-02 -7.55604386e-01 -8.06321561e-01 -8.80199611e-01 -3.95823903e-02 5.19806147e-01 3.81476730e-01 -1.73733205e-01 1.30915433e-01 6.85351074e-01 -9.97024655e-01 1.23958148e-01 6.52841926e-01 8.27898204e-01 1.32324859e-01 4.59595203e-01 2.43431941e-01 6.94159329e-01 -7.15255588e-02 -1.20465634e-02 3.35788041e-01 -4.82080907e-01 -1.20636868e+00 -4.62163568e-01 7.13689387e-01 -6.39359057e-01 -5.59874475e-01 9.38886225e-01 6.87319458e-01 2.44482756e-01 5.96202612e-01 -9.93963063e-01 -8.90134633e-01 4.11192328e-01 -3.69454056e-01 4.97177601e-01 -1.53687608e-03 -3.08715589e-02 6.52981043e-01 -6.93283021e-01 9.04113889e-01 9.06683743e-01 9.80126381e-01 6.19903147e-01 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c96c3a7e-64b4-46f3-94a0-d50fd7159f12	deep-attention-based-alignment-network-for	2301.10015	null	https://arxiv.org/abs/2301.10015v1	https://arxiv.org/pdf/2301.10015v1.pdf	Deep Attention-Based Alignment Network for Melody Generation from Incomplete Lyrics	We propose a deep attention-based alignment network, which aims to automatically predict lyrics and melody with given incomplete lyrics as input in a way similar to the music creation of humans. Most importantly, a deep neural lyrics-to-melody net is trained in an encoder-decoder way to predict possible pairs of lyrics-melody when given incomplete lyrics (few keywords). The attention mechanism is exploited to align the predicted lyrics with the melody during the lyrics-to-melody generation. The qualitative and quantitative evaluation metrics reveal that the proposed method is indeed capable of generating proper lyrics and corresponding melody for composing new songs given a piece of incomplete seed lyrics.	['Suhua Tang', 'Simon Canales', 'Florian Harscoet', 'Yi Yu', 'Zhe Zhang', 'Gurunath Reddy M']	2023-01-23	null	null	null	null	['deep-attention', 'deep-attention']	['computer-vision', 'natural-language-processing']	[ 1.88985318e-01 3.81620787e-03 -6.77451864e-02 -2.68955648e-01 -9.42609549e-01 -7.75787532e-01 8.08318973e-01 -2.66834080e-01 1.14972956e-01 8.16401601e-01 8.31439972e-01 2.94020116e-01 1.18017025e-01 -6.11591756e-01 -8.32773507e-01 -4.73601460e-01 4.54460204e-01 6.79774702e-01 -5.91423571e-01 -3.40266287e-01 2.18816996e-01 3.67547542e-01 -1.61359966e+00 3.70872498e-01 2.90086210e-01 7.84136951e-01 2.77094275e-01 1.18180323e+00 6.67647272e-02 1.14546049e+00 -9.47739720e-01 -6.11691594e-01 4.75846827e-01 -1.04764974e+00 -5.52126288e-01 -5.10215499e-02 7.09484637e-01 -3.71814817e-01 -4.36157167e-01 8.32447469e-01 5.99287868e-01 1.57536075e-01 7.31254458e-01 -9.07005847e-01 -6.28235877e-01 1.19543433e+00 -1.65503360e-02 -8.01476166e-02 4.51818883e-01 2.85572559e-01 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1a73b735-6940-4845-bcb8-162cc086bf53	neural-supersampling-for-real-time-rendering	null	null	https://dl.acm.org/doi/10.1145/3386569.3392376	https://dl.acm.org/doi/pdf/10.1145/3386569.3392376	Neural supersampling for real-time rendering	Due to higher resolutions and refresh rates, as well as more photorealistic effects, real-time rendering has become increasingly challenging for video games and emerging virtual reality headsets. To meet this demand, modern graphics hardware and game engines often reduce the computational cost by rendering at a lower resolution and then upsampling to the native resolution. Following the recent advances in image and video superresolution in computer vision, we propose a machine learning approach that is specifically tailored for high-quality upsampling of rendered content in real-time applications. The main insight of our work is that in rendered content, the image pixels are point-sampled, but precise temporal dynamics are available. Our method combines this specific information that is typically available in modern renderers (i.e., depth and dense motion vectors) with a novel temporal network design that takes into account such specifics and is aimed at maximizing video quality while delivering real-time performance. By training on a large synthetic dataset rendered from multiple 3D scenes with recorded camera motion, we demonstrate high fidelity and temporally stable results in real-time, even in the highly challenging 4 × 4 upsampling scenario, significantly outperforming existing superresolution and temporal antialiasing work.	['Anton Kaplanyan', 'Douglas Lanman', 'Alexander Fix', 'Matt Chapman', 'Salah Nouri', 'Lei Xiao']	2020-08-01	null	null	null	acm-transactions-on-graphics-2020-8	['video-super-resolution']	['computer-vision']	[ 6.24765635e-01 -3.30761194e-01 -5.64564625e-03 1.69108659e-01 -5.85164487e-01 -4.05491620e-01 6.04125381e-01 -3.73556852e-01 -2.02285409e-01 6.40317619e-01 2.65039980e-01 -2.08328199e-03 6.93776645e-03 -8.60247314e-01 -6.44103110e-01 -5.49343944e-01 -1.60552412e-01 5.93043789e-02 4.75108653e-01 -4.97415662e-01 2.40225166e-01 5.93218505e-01 -2.11374855e+00 5.00012755e-01 7.50240505e-01 1.01599848e+00 3.40048373e-01 1.21853435e+00 1.91591769e-01 9.22308683e-01 -2.56395996e-01 -1.04643032e-01 5.31216085e-01 -2.67035037e-01 -5.40051043e-01 3.23949903e-01 9.25315678e-01 -8.88905466e-01 -6.25559568e-01 8.59264970e-01 3.53783399e-01 3.94795746e-01 7.71954283e-02 -6.98476315e-01 -4.11238790e-01 1.75976098e-01 -7.71803558e-01 5.04459143e-01 4.50785100e-01 3.04408520e-01 7.92538881e-01 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9bea086a-67e1-4119-bf19-8083aa3374b2	learning-motion-appearance-co-attention-for	null	null	http://openaccess.thecvf.com//content/ICCV2021/html/Yang_Learning_Motion-Appearance_Co-Attention_for_Zero-Shot_Video_Object_Segmentation_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Yang_Learning_Motion-Appearance_Co-Attention_for_Zero-Shot_Video_Object_Segmentation_ICCV_2021_paper.pdf	Learning Motion-Appearance Co-Attention for Zero-Shot Video Object Segmentation	How to make the appearance and motion information interact effectively to accommodate complex scenarios is a fundamental issue in flow-based zero-shot video object segmentation. In this paper, we propose an Attentive Multi-Modality Collaboration Network (AMC-Net) to utilize appearance and motion information uniformly. Specifically, AMC-Net fuses robust information from multi-modality features and promotes their collaboration in two stages. First, we propose a Multi-Modality Co-Attention Gate (MCG) on the bilateral encoder branches, in which a gate function is used to formulate co-attention scores for balancing the contributions of multi-modality features and suppressing the redundant and misleading information. Then, we propose a Motion Correction Module (MCM) with a visual-motion attention mechanism, which is constructed to emphasize the features of foreground objects by incorporating the spatio-temporal correspondence between appearance and motion cues. Extensive experiments on three public challenging benchmark datasets verify that our proposed network performs favorably against existing state-of-the-art methods via training with fewer data.	['Xiaoxing Zhang', 'Shuo Wang', 'Huchuan Lu', 'Jinqing Qi', 'Lu Zhang', 'Shu Yang']	2021-01-01	null	null	null	iccv-2021-1	['unsupervised-video-object-segmentation']	['computer-vision']	[ 1.74095601e-01 -4.13281351e-01 -2.41965219e-01 -3.09997082e-01 -4.30993021e-01 -2.09053129e-01 4.75914687e-01 -4.36205357e-01 -3.62355471e-01 4.13310617e-01 4.57282126e-01 -5.58437891e-02 4.92395498e-02 -5.09022653e-01 -5.79337120e-01 -7.23745346e-01 3.77672732e-01 -1.96995765e-01 7.60455906e-01 -1.53654128e-01 2.80079216e-01 1.80221766e-01 -1.38988340e+00 4.10473555e-01 9.54475462e-01 8.30954731e-01 3.97267729e-01 5.76984107e-01 -1.83266312e-01 1.25274360e+00 -2.46779069e-01 -1.86574444e-01 1.12890638e-01 -5.84299862e-01 -8.35674405e-01 4.05750781e-01 3.53844106e-01 -6.24852121e-01 -4.90985602e-01 9.63431537e-01 4.35054094e-01 4.67226416e-01 5.30059159e-01 -1.40024674e+00 -6.66680217e-01 1.84844956e-01 -1.10305619e+00 7.15852082e-01 1.02725364e-01 6.19885206e-01 8.38623047e-01 -9.53246951e-01 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67af1a01-e715-41b4-875b-30078b28d059	spatiotemporal-modeling-of-seismic-images-for	2006.15472	null	https://arxiv.org/abs/2006.15472v1	https://arxiv.org/pdf/2006.15472v1.pdf	Spatiotemporal Modeling of Seismic Images for Acoustic Impedance Estimation	Seismic inversion refers to the process of estimating reservoir rock properties from seismic reflection data. Conventional and machine learning-based inversion workflows usually work in a trace-by-trace fashion on seismic data, utilizing little to no information from the spatial structure of seismic images. We propose a deep learning-based seismic inversion workflow that models each seismic trace not only temporally but also spatially. This utilizes information-relatedness in seismic traces in depth and spatial directions to make efficient rock property estimations. We empirically compare our proposed workflow with some other sequence modeling-based neural networks that model seismic data only temporally. Our results on the SEAM dataset demonstrate that, compared to the other architectures used in the study, the proposed workflow is able to achieve the best performance, with an average $r^{2}$ coefficient of 79.77\%.	['Motaz Alfarraj', 'Ahmad Mustafa', 'Ghassan AlRegib']	2020-06-28	null	null	null	null	['seismic-inversion']	['miscellaneous']	[ 1.39145538e-01 -2.19533503e-01 2.66315162e-01 -1.48879170e-01 -9.37493920e-01 -1.61098912e-01 5.81329346e-01 -3.68162766e-02 -7.16680110e-01 5.15823185e-01 1.31748199e-01 -4.25133675e-01 -3.46371353e-01 -1.14653158e+00 -8.98760974e-01 -8.03544819e-01 -5.90259790e-01 8.23986530e-01 5.02565801e-01 -3.14464509e-01 8.95011365e-01 4.93831784e-01 -1.24474967e+00 3.39055121e-01 4.93953556e-01 8.91502261e-01 3.97567153e-01 5.98480821e-01 -1.90259859e-01 1.15589702e+00 -1.81768179e-01 1.26064345e-01 2.01182604e-01 -1.05223954e-01 -8.73318791e-01 -4.41310734e-01 2.16942668e-01 -4.88780379e-01 -5.82519054e-01 4.33989227e-01 5.27775764e-01 6.96714595e-02 7.21617818e-01 -6.09822273e-01 -1.20943539e-01 7.89828777e-01 -8.11211824e-01 3.17183346e-01 1.40650095e-02 1.11737400e-01 5.35886943e-01 -1.15311217e+00 3.69460016e-01 6.21351242e-01 1.21406889e+00 -4.01750766e-03 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-8.45660865e-01 1.61006406e-01 5.11850655e-01 -5.52366614e-01 -7.91302174e-02 4.11447644e-01]	[6.872437000274658, 2.522125482559204]
cb7353b6-4d28-4524-ab52-a6743c8f1da2	cross-lingual-joint-entity-and-word-embedding	null	null	https://aclanthology.org/D19-6107	https://aclanthology.org/D19-6107.pdf	Cross-lingual Joint Entity and Word Embedding to Improve Entity Linking and Parallel Sentence Mining	Entities, which refer to distinct objects in the real world, can be viewed as language universals and used as effective signals to generate less ambiguous semantic representations and align multiple languages. We propose a novel method, CLEW, to generate cross-lingual data that is a mix of entities and contextual words based on Wikipedia. We replace each anchor link in the source language with its corresponding entity title in the target language if it exists, or in the source language otherwise. A cross-lingual joint entity and word embedding learned from this kind of data not only can disambiguate linkable entities but can also effectively represent unlinkable entities. Because this multilingual common space directly relates the semantics of contextual words in the source language to that of entities in the target language, we leverage it for unsupervised cross-lingual entity linking. Experimental results show that CLEW significantly advances the state-of-the-art: up to 3.1{\%} absolute F-score gain for unsupervised cross-lingual entity linking. Moreover, it provides reliable alignment on both the word/entity level and the sentence level, and thus we use it to mine parallel sentences for all (302, 2) language pairs in Wikipedia.	['Thamme Gowda', 'Scott Miller', 'Heng Ji', 'Xiaoman Pan', 'Jonathan May']	2019-11-01	null	null	null	ws-2019-11	['cross-lingual-entity-linking']	['natural-language-processing']	[-4.47631836e-01 1.19893149e-01 -5.87141335e-01 -2.80741692e-01 -1.02659547e+00 -9.44721162e-01 6.44785523e-01 5.47051430e-01 -7.60091782e-01 9.88049686e-01 6.58308983e-01 -8.49754438e-02 9.15845260e-02 -9.67424393e-01 -8.80529225e-01 -1.61891803e-01 1.37194261e-01 6.48200333e-01 1.46892995e-01 -6.08517051e-01 -3.06160569e-01 -2.44136572e-01 -9.62559640e-01 1.15866244e-01 1.28564143e+00 3.36827606e-01 2.01407939e-01 -2.12975860e-01 -8.04532588e-01 3.63129139e-01 -6.11128807e-01 -1.08779418e+00 -2.12966055e-02 -2.75739819e-01 -7.74421394e-01 -5.85307240e-01 7.49582887e-01 5.66747367e-01 -2.91643560e-01 1.42200983e+00 5.07001817e-01 -2.05844060e-01 5.92873275e-01 -1.19830048e+00 -1.19651580e+00 1.27963448e+00 -5.58695853e-01 1.05772153e-01 4.10141706e-01 -3.99237961e-01 1.54900634e+00 -1.01806414e+00 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329005c7-384c-46b4-8be8-49ce748997e4	brain-age-prediction-using-deep-learning	null	null	https://www.nature.com/articles/s41467-019-13163-9	https://www.nature.com/articles/s41467-019-13163-9.pdf	Brain age prediction using deep learning uncovers associated sequence variants	Machine learning algorithms can be trained to estimate age from brain structural MRI. The difference between an individual’s predicted and chronological age, predicted age difference (PAD), is a phenotype of relevance to aging and brain disease. Here, we present a new deep learning approach to predict brain age from a T1-weighted MRI. The method was trained on a dataset of healthy Icelanders and tested on two datasets, IXI and UK Biobank, utilizing transfer learning to improve accuracy on new sites. A genome-wide association study (GWAS) of PAD in the UK Biobank data (discovery set: N=12378, replication set: N=4456) yielded two sequence variants, rs1452628-T (β=−0.08, P=1.15×10−9) and rs2435204-G (β=0.102, P=9.73×10−12). The former is near KCNK2 and correlates with reduced sulcal width, whereas the latter correlates with reduced white matter surface area and tags a well-known inversion at 17q21.31 (H2).	['M. O. Ulfarsson', 'K. Stefansson', 'H. Stefansson', 'D. F. Gudbjartsson', 'G. Bragi Walters', 'L. M. Ellingsen', 'T. E. Thorgeirsson', 'G. Bjornsdottir', 'B. A. Jonsson']	2019-11-27	null	null	null	null	['age-estimation', 'age-estimation']	['computer-vision', 'miscellaneous']	[ 1.63958862e-01 4.21552539e-01 -3.80672067e-02 -5.80214202e-01 -5.84398866e-01 -3.43849286e-02 4.23541844e-01 1.93544671e-01 -9.66276824e-01 1.19659078e+00 5.07834256e-01 -3.01031142e-01 -1.27782643e-01 -5.60576916e-01 -6.56180918e-01 -4.35214102e-01 -8.65291536e-01 5.11375904e-01 1.65124834e-02 2.18575150e-01 1.49854019e-01 -2.83914000e-01 -1.03302920e+00 -5.79918455e-03 1.33271515e+00 8.40135276e-01 1.24944769e-01 6.23073220e-01 4.14458096e-01 -6.24663644e-02 -5.00451505e-01 -5.04213512e-01 2.65669711e-02 -4.09758925e-01 -6.46052301e-01 -9.33968425e-01 7.65822232e-01 -2.49868691e-01 -1.14485122e-01 9.05775011e-01 1.09514654e+00 -4.34015483e-01 7.93330848e-01 -7.70154059e-01 -7.55977809e-01 7.67300963e-01 -9.02573287e-01 6.65333748e-01 -1.59568682e-01 3.56533416e-02 6.99020505e-01 -5.71273208e-01 7.96243310e-01 9.43143904e-01 1.33011818e+00 7.77921319e-01 -1.15049720e+00 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-2.54342467e-01 -5.89744896e-02 -8.42409194e-01 2.07210511e-01 1.04528740e-01]	[14.093168258666992, -1.5523468255996704]
d974bdc4-c4ff-4b82-9882-e86f5363ddda	side-information-for-face-completion-a-robust	1801.07580	null	http://arxiv.org/abs/1801.07580v1	http://arxiv.org/pdf/1801.07580v1.pdf	Side Information for Face Completion: a Robust PCA Approach	Robust principal component analysis (RPCA) is a powerful method for learning low-rank feature representation of various visual data. However, for certain types as well as significant amount of error corruption, it fails to yield satisfactory results; a drawback that can be alleviated by exploiting domain-dependent prior knowledge or information. In this paper, we propose two models for the RPCA that take into account such side information, even in the presence of missing values. We apply this framework to the task of UV completion which is widely used in pose-invariant face recognition. Moreover, we construct a generative adversarial network (GAN) to extract side information as well as subspaces. These subspaces not only assist in the recovery but also speed up the process in case of large-scale data. We quantitatively and qualitatively evaluate the proposed approaches through both synthetic data and five real-world datasets to verify their effectiveness.	['Shiyang Cheng', 'Niannan Xue', 'Yannis Panagakis', 'Stefanos Zafeiriou', 'Jiankang Deng']	2018-01-20	null	null	null	null	['robust-face-recognition', 'facial-inpainting']	['computer-vision', 'computer-vision']	[ 4.33406651e-01 -3.76593143e-01 1.77621022e-01 -1.42406508e-01 -8.50057244e-01 -7.05648899e-01 6.69403374e-01 -4.52176988e-01 -1.24159336e-01 7.55747795e-01 3.88070554e-01 6.41870201e-02 -2.88116366e-01 -5.34023464e-01 -6.07646465e-01 -9.68328238e-01 3.02717656e-01 2.07281947e-01 -3.43239456e-01 -1.06566250e-01 1.27533168e-01 8.70393217e-01 -1.54916465e+00 6.08645491e-02 7.63251603e-01 6.88625038e-01 1.12104146e-02 2.22584471e-01 1.57087192e-01 5.27769268e-01 -3.56163591e-01 -3.39625478e-01 6.23049319e-01 -9.67358425e-02 -3.55856180e-01 4.55792904e-01 4.62775886e-01 -2.37791821e-01 -3.85365874e-01 1.02191210e+00 5.76052725e-01 7.01153353e-02 9.26667631e-01 -1.28498828e+00 -3.39215279e-01 -2.22742468e-01 -8.83094311e-01 -1.64771080e-01 5.28786540e-01 1.08837776e-01 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2.08122551e-01 1.00539076e+00 -7.39206672e-01 -8.19407344e-01 -8.97626579e-01 -7.28655279e-01 8.12153161e-01 3.23570520e-01 -7.99507856e-01 -4.48415220e-01 9.05010253e-02]	[12.742056846618652, 0.2941459119319916]
1121a6c5-207e-4a4d-b5e0-ff975c8f3b97	sndcnn-self-normalizing-deep-cnns-with-scaled	1910.01992	null	https://arxiv.org/abs/1910.01992v3	https://arxiv.org/pdf/1910.01992v3.pdf	SNDCNN: Self-normalizing deep CNNs with scaled exponential linear units for speech recognition	Very deep CNNs achieve state-of-the-art results in both computer vision and speech recognition, but are difficult to train. The most popular way to train very deep CNNs is to use shortcut connections (SC) together with batch normalization (BN). Inspired by Self- Normalizing Neural Networks, we propose the self-normalizing deep CNN (SNDCNN) based acoustic model topology, by removing the SC/BN and replacing the typical RELU activations with scaled exponential linear unit (SELU) in ResNet-50. SELU activations make the network self-normalizing and remove the need for both shortcut connections and batch normalization. Compared to ResNet- 50, we can achieve the same or lower (up to 4.5% relative) word error rate (WER) while boosting both training and inference speed by 60%-80%. We also explore other model inference optimization schemes to further reduce latency for production use.	['Tim Ng', 'Leo Liu', 'Zhen Huang', 'Xiaodan Zhuang', 'Henry Mason', 'Daben Liu']	2019-10-04	null	null	null	null	['inference-optimization']	['audio']	[ 2.19630189e-02 2.06165567e-01 4.91914637e-02 -7.77882278e-01 -3.62802356e-01 -3.46332282e-01 6.13467157e-01 8.02432224e-02 -1.11490965e+00 3.73117685e-01 1.55994818e-01 -5.95840275e-01 4.97135967e-01 -8.81275654e-01 -8.03431511e-01 -5.29100537e-01 4.71856803e-01 1.96167231e-01 3.23657691e-01 -2.94935614e-01 -1.51234746e-01 6.40561342e-01 -1.41331303e+00 5.00017079e-03 4.39996570e-01 8.92728686e-01 3.73077065e-01 9.60961878e-01 -3.88852298e-01 6.58759296e-01 -7.88710892e-01 -5.28304398e-01 1.18689463e-01 6.34381622e-02 -6.49564564e-01 -5.77203095e-01 5.20735502e-01 -5.54144204e-01 -4.34714913e-01 1.17929816e+00 7.51119316e-01 4.10879523e-01 3.41012776e-01 -9.23608601e-01 -7.45467603e-01 1.00456011e+00 -3.33174318e-01 1.74333706e-01 -2.65365452e-01 2.40507256e-02 8.79305780e-01 -8.38524640e-01 5.43997511e-02 1.49300528e+00 6.87070489e-01 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-4.75768000e-01 -1.47011054e+00 -3.25888842e-01 -7.75576988e-03 3.81056070e-01 -2.36648485e-01 -2.89041609e-01 -1.79784685e-01]	[14.215165138244629, 6.477771759033203]
4f7e0ed7-8b1c-49a2-b822-32022aeebfd4	massively-multilingual-word-embeddings	1602.01925	null	http://arxiv.org/abs/1602.01925v2	http://arxiv.org/pdf/1602.01925v2.pdf	Massively Multilingual Word Embeddings	We introduce new methods for estimating and evaluating embeddings of words in more than fifty languages in a single shared embedding space. Our estimation methods, multiCluster and multiCCA, use dictionaries and monolingual data; they do not require parallel data. Our new evaluation method, multiQVEC-CCA, is shown to correlate better than previous ones with two downstream tasks (text categorization and parsing). We also describe a web portal for evaluation that will facilitate further research in this area, along with open-source releases of all our methods.	['Waleed Ammar', 'Noah A. Smith', 'Chris Dyer', 'Yulia Tsvetkov', 'George Mulcaire', 'Guillaume Lample']	2016-02-05	null	null	null	null	['multilingual-word-embeddings']	['methodology']	[-4.77467954e-01 -4.01582837e-01 -4.02322888e-01 -6.26310706e-01 -1.20506108e+00 -1.01554644e+00 7.44142234e-01 3.31360340e-01 -8.96449029e-01 4.47796196e-01 6.31446779e-01 -7.50681460e-01 3.87602985e-01 -3.90167892e-01 -1.28993183e-01 -3.67675841e-01 6.35245536e-03 5.95992744e-01 1.97176170e-02 -3.15471649e-01 2.14903459e-01 -4.70607658e-04 -1.00869751e+00 3.19280982e-01 7.54434407e-01 2.42649287e-01 3.73396426e-01 1.06626725e+00 -4.19246882e-01 3.25018764e-01 -2.74459511e-01 -1.01178801e+00 1.85193885e-02 -5.29267378e-02 -9.24286544e-01 -5.82007647e-01 7.58686960e-01 -3.20982397e-01 -1.32985905e-01 1.07768142e+00 8.48603010e-01 4.70243059e-02 6.45403028e-01 -8.48416805e-01 -1.25862336e+00 1.07728875e+00 -1.55914769e-01 4.94173527e-01 2.72603482e-01 -3.32845271e-01 1.94015527e+00 -1.25000393e+00 7.12355912e-01 1.54461300e+00 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08da9d3c-29e5-4e8d-ab73-311c3f8c7529	query-based-single-document-summarization	null	null	https://aclanthology.org/U15-1001	https://aclanthology.org/U15-1001.pdf	Query-Based Single Document Summarization Using an Ensemble Noisy Auto-Encoder	null	["Diego Moll{\\'a} Aliod", 'Len Hamey', 'Mahmood Yousefi Azar', 'Kairit Sirts']	2015-12-01	query-based-single-document-summarization-1	https://aclanthology.org/U15-1001	https://aclanthology.org/U15-1001.pdf	alta-2015-12	['query-based-extractive-summarization']	['natural-language-processing']	[-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 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a44f71cc-ded4-4f76-afba-5624641694ea	learning-an-invariant-speech-representation	1406.3884	null	http://arxiv.org/abs/1406.3884v1	http://arxiv.org/pdf/1406.3884v1.pdf	Learning An Invariant Speech Representation	Recognition of speech, and in particular the ability to generalize and learn from small sets of labelled examples like humans do, depends on an appropriate representation of the acoustic input. We formulate the problem of finding robust speech features for supervised learning with small sample complexity as a problem of learning representations of the signal that are maximally invariant to intraclass transformations and deformations. We propose an extension of a theory for unsupervised learning of invariant visual representations to the auditory domain and empirically evaluate its validity for voiced speech sound classification. Our version of the theory requires the memory-based, unsupervised storage of acoustic templates -- such as specific phones or words -- together with all the transformations of each that normally occur. A quasi-invariant representation for a speech segment can be obtained by projecting it to each template orbit, i.e., the set of transformed signals, and computing the associated one-dimensional empirical probability distributions. The computations can be performed by modules of filtering and pooling, and extended to hierarchical architectures. In this paper, we apply a single-layer, multicomponent representation for phonemes and demonstrate improved accuracy and decreased sample complexity for vowel classification compared to standard spectral, cepstral and perceptual features.	['Stephen Voinea', 'Lorenzo Rosasco', 'Tomaso Poggio', 'Georgios Evangelopoulos', 'Chiyuan Zhang']	2014-06-16	null	null	null	null	['vowel-classification', 'sound-classification']	['audio', 'audio']	[ 6.35896087e-01 -7.26993009e-02 2.69390047e-01 -5.88857889e-01 -5.54944396e-01 -5.61715245e-01 5.15173197e-01 7.18478411e-02 -5.00043154e-01 3.80372047e-01 2.26126283e-01 -1.68561384e-01 -1.13984048e-01 -6.47366524e-01 -4.68999982e-01 -8.54777813e-01 -2.13563159e-01 1.95654407e-01 4.22376692e-01 1.27064660e-01 2.64686704e-01 8.43768597e-01 -2.15037775e+00 6.07462764e-01 1.68965325e-01 1.08932233e+00 4.14526641e-01 1.02730501e+00 -4.13983949e-02 2.95364767e-01 -5.86929083e-01 1.95750386e-01 3.35582681e-02 -3.94409925e-01 -8.29519987e-01 3.60905826e-01 4.91962016e-01 2.42973268e-01 -1.35643005e-01 1.10225725e+00 4.99914140e-01 6.89757705e-01 1.10024476e+00 -9.34449017e-01 -9.35971379e-01 3.41576278e-01 4.09496635e-01 3.45935851e-01 2.53136903e-01 -1.58151507e-01 1.06047285e+00 -1.34607136e+00 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c5ceca73-77ce-4426-9128-4503c1ac0cc3	we-built-a-fake-news-click-bait-filter-what-1	null	null	https://aclanthology.org/R17-1045	https://aclanthology.org/R17-1045.pdf	We Built a Fake News / Click Bait Filter: What Happened Next Will Blow Your Mind!	It is completely amazing! Fake news and {``}click baits{''} have totally invaded the cyberspace. Let us face it: everybody hates them for three simple reasons. Reason {\#}2 will absolutely amaze you. What these can achieve at the time of election will completely blow your mind! Now, we all agree, this cannot go on, you know, somebody has to stop it. So, we did this research, and trust us, it is totally great research, it really is! Make no mistake. This is the best research ever! Seriously, come have a look, we have it all: neural networks, attention mechanism, sentiment lexicons, author profiling, you name it. Lexical features, semantic features, we absolutely have it all. And we have totally tested it, trust us! We have results, and numbers, really big numbers. The best numbers ever! Oh, and analysis, absolutely top notch analysis. Interested? Come read the shocking truth about fake news and clickbait in the Bulgarian cyberspace. You won{'}t believe what we have found!	['Preslav Nakov', 'Pepa Gencheva', 'Ivan Koychev', 'Georgi Karadzhov']	2017-09-01	null	null	null	ranlp-2017-9	['clickbait-detection']	['natural-language-processing']	[-6.25626802e-01 -7.38656670e-02 -3.40813845e-01 -3.41040075e-01 -3.52800161e-01 -7.93797255e-01 2.51561195e-01 -1.39712282e-02 -4.64216411e-01 1.10692549e+00 3.41311067e-01 -8.52593362e-01 -1.83841392e-01 -6.56765103e-01 -9.07928228e-01 -3.59664977e-01 7.64467537e-01 4.25630152e-01 2.95987576e-01 -1.35998118e+00 1.08042550e+00 -7.39132380e-03 -1.26285887e+00 2.68534392e-01 6.72689974e-01 6.95100427e-01 -4.13021564e-01 5.30805707e-01 4.48540831e-03 1.36003804e+00 -6.30022943e-01 -1.20555341e+00 8.68334770e-02 -3.09312016e-01 -1.08135176e+00 -4.48296160e-01 3.82528901e-01 -3.93997133e-01 -1.66984901e-01 1.38974416e+00 3.96105126e-02 -2.14826271e-01 3.05104524e-01 -1.27313721e+00 -1.11530125e+00 5.23088813e-01 -5.04453957e-01 7.80414402e-01 1.56809255e-01 1.02699712e-01 9.90469635e-01 -4.15996194e-01 9.40119147e-01 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1.10451348e-01 -4.86229628e-01 -9.70289171e-01 -4.83597189e-01 2.99526691e-01 1.15569443e-01 -5.75561464e-01 -1.14179301e+00 -6.64646685e-01]	[8.153901100158691, 10.180121421813965]
98c5ca77-07db-4b2b-9645-27f83a56c293	interpretable-not-just-posthoc-explainable-1	2304.09981	null	https://arxiv.org/abs/2304.09981v1	https://arxiv.org/pdf/2304.09981v1.pdf	Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions	We used survival analysis to quantify the impact of postdischarge evaluation and management (E/M) services in preventing hospital readmission or death. Our approach avoids a specific pitfall of applying machine learning to this problem, which is an inflated estimate of the effect of interventions, due to survivors bias -- where the magnitude of inflation may be conditional on heterogeneous confounders in the population. This bias arises simply because in order to receive an intervention after discharge, a person must not have been readmitted in the intervening period. After deriving an expression for this phantom effect, we controlled for this and other biases within an inherently interpretable Bayesian survival framework. We identified case management services as being the most impactful for reducing readmissions overall, particularly for patients discharged to long term care facilities, with high resource utilization in the quarter preceding admission.	['Carson C. Chow', 'Ted L. Chang', 'Rohit Mahajan', 'Sonya Mahajan', 'Sarah Nowak', 'Joshua C. Chang', 'Hongjing Xia']	2023-04-19	null	null	null	null	['survival-analysis']	['miscellaneous']	[ 3.62563372e-01 3.27489108e-01 -5.58462679e-01 -2.48959064e-01 -1.10090661e+00 -2.49030024e-01 2.24435404e-01 6.51623905e-01 -9.22096133e-01 9.86076772e-01 1.04407132e+00 -9.73639846e-01 -6.36695743e-01 -7.05677211e-01 -6.01363420e-01 -7.04119384e-01 -2.70765424e-01 6.31052613e-01 -4.87296909e-01 4.61954683e-01 2.97126651e-01 4.79758590e-01 -6.58226132e-01 1.10988483e-01 4.80027020e-01 2.90678948e-01 -1.05005182e-01 4.99493033e-01 2.43732795e-01 7.42388368e-01 -3.31360221e-01 -2.40417644e-01 -5.59202507e-02 -5.27030110e-01 -5.33131361e-01 -3.82161796e-01 -3.25774252e-01 -9.05739844e-01 -2.28480548e-01 2.81300485e-01 6.05063379e-01 -2.26536512e-01 1.53882122e+00 -9.57293630e-01 -1.22140199e-01 8.58700752e-01 2.73115728e-02 2.86566377e-01 3.13613296e-01 2.32132584e-01 5.64378798e-01 -5.34084141e-01 1.78179696e-01 1.16069937e+00 7.73614168e-01 3.41479152e-01 -1.27899384e+00 -4.84399289e-01 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-6.53808832e-01 1.21945061e-01 6.22063398e-01 -4.59045321e-01 -1.07811773e+00 -1.34844938e-02]	[7.959614276885986, 5.4397406578063965]
8bef3edf-ece9-4224-8f69-4449c3d79f45	population-mapping-in-informal-settlements	2009.08410	null	https://arxiv.org/abs/2009.08410v1	https://arxiv.org/pdf/2009.08410v1.pdf	Population Mapping in Informal Settlements with High-Resolution Satellite Imagery and Equitable Ground-Truth	We propose a generalizable framework for the population estimation of dense, informal settlements in low-income urban areas--so called 'slums'--using high-resolution satellite imagery. Precise population estimates are a crucial factor for efficient resource allocations by government authorities and NGO's, for instance in medical emergencies. We utilize equitable ground-truth data, which is gathered in collaboration with local communities: Through training and community mapping, the local population contributes their unique domain knowledge, while also maintaining agency over their data. This practice allows us to avoid carrying forward potential biases into the modeling pipeline, which might arise from a less rigorous ground-truthing approach. We contextualize our approach in respect to the ongoing discussion within the machine learning community, aiming to make real-world machine learning applications more inclusive, fair and accountable. Because of the resource intensive ground-truth generation process, our training data is limited. We propose a gridded population estimation model, enabling flexible and customizable spatial resolutions. We test our pipeline on three experimental site in Nigeria, utilizing pre-trained and fine-tune vision networks to overcome data sparsity. Our findings highlight the difficulties of transferring common benchmark models to real-world tasks. We discuss this and propose steps forward.	['João Porto de Albuquerque', 'Godwin Yeboah', 'Stephen A Jarvis', 'Konstantin Klemmer']	2020-09-17	null	null	null	null	['population-mapping']	['computer-vision']	[ 2.18451738e-01 2.23802447e-01 -2.17901021e-01 -2.10948601e-01 -8.77912760e-01 -2.07727998e-01 3.93989980e-01 2.72973120e-01 -7.94140756e-01 1.40458560e+00 7.51323640e-01 -4.38236088e-01 -1.52480170e-01 -1.31905007e+00 -5.07755041e-01 -7.21282423e-01 -4.35451090e-01 7.56003022e-01 -2.85053909e-01 -3.81937504e-01 1.27242029e-01 4.86822426e-01 -1.25976264e+00 -1.90407597e-02 1.04886317e+00 4.09076363e-01 2.86256820e-01 5.78093529e-01 7.48234242e-02 6.75562739e-01 -2.83706903e-01 -1.07725695e-01 4.13818777e-01 2.67425478e-01 -7.88677931e-01 5.35476953e-02 5.54729879e-01 -5.46090841e-01 1.57405525e-01 9.36792135e-01 5.67268133e-01 -7.71728717e-03 7.79518247e-01 -1.13474715e+00 -8.99806738e-01 3.49965334e-01 -8.40166688e-01 4.89812195e-01 3.09144437e-01 1.96615845e-01 5.94139457e-01 -5.30987561e-01 5.02679586e-01 1.14439476e+00 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b5245ea8-ec9c-4837-8b99-d400c9c810dc	unos-unified-unsupervised-optical-flow-and	null	null	http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_UnOS_Unified_Unsupervised_Optical-Flow_and_Stereo-Depth_Estimation_by_Watching_Videos_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_UnOS_Unified_Unsupervised_Optical-Flow_and_Stereo-Depth_Estimation_by_Watching_Videos_CVPR_2019_paper.pdf	UnOS: Unified Unsupervised Optical-Flow and Stereo-Depth Estimation by Watching Videos	In this paper, we propose UnOS, an unified system for unsupervised optical flow and stereo depth estimation using convolutional neural network (CNN) by taking advantages of their inherent geometrical consistency based on the rigid-scene assumption. UnOS significantly outperforms other state-of-the-art (SOTA) unsupervised approaches that treated the two tasks independently. Specifically, given two consecutive stereo image pairs from a video, UnOS estimates per-pixel stereo depth images, camera ego-motion and optical flow with three parallel CNNs. Based on these quantities, UnOS computes rigid optical flow and compares it against the optical flow estimated from the FlowNet, yielding pixels satisfying the rigid-scene assumption. Then, we encourage geometrical consistency between the two estimated flows within rigid regions, from which we derive a rigid-aware direct visual odometry (RDVO) module. We also propose rigid and occlusion-aware flow-consistency losses for the learning of UnOS. We evaluated our results on the popular KITTI dataset over 4 related tasks, i.e. stereo depth, optical flow, visual odometry and motion segmentation.	[' Wei Xu', ' Yi Yang', ' Chenxu Luo', ' Zhenheng Yang', ' Peng Wang', 'Yang Wang']	2019-06-01	null	null	null	cvpr-2019-6	['stereo-depth-estimation']	['computer-vision']	[ 2.81554405e-02 -9.55889747e-02 -3.89478326e-01 -3.01129103e-01 -1.07169881e-01 -5.92700779e-01 5.48933268e-01 -4.39509898e-01 -5.37825286e-01 6.98813438e-01 2.99698770e-01 9.86410975e-02 2.61852086e-01 -7.51911819e-01 -8.70699406e-01 -4.93363380e-01 2.89113492e-01 5.08650839e-01 3.79560947e-01 2.45904386e-01 5.60197353e-01 6.15690351e-01 -1.32852888e+00 -2.97686398e-01 9.65819299e-01 9.08098936e-01 6.33000284e-02 9.49044883e-01 -9.19772461e-02 1.50210142e+00 7.20678046e-02 -2.06083879e-01 4.27509993e-01 -2.76226848e-01 -1.37623966e+00 2.84921825e-01 1.43666971e+00 -1.06172001e+00 -9.00024593e-01 1.04552197e+00 2.70756960e-01 5.05437672e-01 5.28496087e-01 -1.20558083e+00 -3.35795552e-01 7.35258535e-02 -5.50503910e-01 4.93008852e-01 5.24582326e-01 4.40095633e-01 9.85663176e-01 -5.77432215e-01 1.16761899e+00 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606400ef-4d82-42d7-84d7-3ed7704c8fd6	short-text-clustering-with-transformers	2102.00541	null	https://arxiv.org/abs/2102.00541v1	https://arxiv.org/pdf/2102.00541v1.pdf	Short Text Clustering with Transformers	Recent techniques for the task of short text clustering often rely on word embeddings as a transfer learning component. This paper shows that sentence vector representations from Transformers in conjunction with different clustering methods can be successfully applied to address the task. Furthermore, we demonstrate that the algorithm of enhancement of clustering via iterative classification can further improve initial clustering performance with different classifiers, including those based on pre-trained Transformer language models.	['Mikhail Burtsev', 'Leonid Pugachev']	2021-01-31	null	null	null	null	['text-clustering', 'short-text-clustering']	['natural-language-processing', 'natural-language-processing']	[ 1.56466141e-01 -2.01457009e-01 -3.04459512e-01 -4.75476623e-01 -9.09254968e-01 -5.35666943e-01 6.02810502e-01 3.25852722e-01 -3.38006794e-01 2.72525996e-01 5.05036712e-01 -6.62343264e-01 -1.09795658e-02 -5.78428030e-01 -5.72459921e-02 -7.01942444e-01 1.10858858e-01 8.26222897e-01 2.76309729e-01 -4.04507309e-01 4.77780849e-01 1.88959733e-01 -1.38913071e+00 4.37768549e-01 9.86596107e-01 5.94730020e-01 2.70470738e-01 6.39058888e-01 -8.13740194e-01 9.17344928e-01 -7.29975224e-01 -2.75642008e-01 -7.82754272e-02 -2.86928773e-01 -9.17365193e-01 2.05462888e-01 5.27510010e-02 4.95215803e-01 -3.65684032e-01 8.30582798e-01 2.18252584e-01 2.72493005e-01 1.12090206e+00 -1.08317542e+00 -6.29392743e-01 1.18475890e+00 -2.59910017e-01 1.03082776e-01 2.86346853e-01 -5.82575858e-01 1.28513145e+00 -9.23911572e-01 2.87438601e-01 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e04d2d0e-4801-41dd-9355-bd7290b8843b	raw-x-vector-multi-scale-time-domain-speaker	2010.12951	null	https://arxiv.org/abs/2010.12951v3	https://arxiv.org/pdf/2010.12951v3.pdf	Y-Vector: Multiscale Waveform Encoder for Speaker Embedding	State-of-the-art text-independent speaker verification systems typically use cepstral features or filter bank energies as speech features. Recent studies attempted to extract speaker embeddings directly from raw waveforms and have shown competitive results. In this paper, we propose a novel multi-scale waveform encoder that uses three convolution branches with different time scales to compute speech features from the waveform. These features are then processed by squeeze-and-excitation blocks, a multi-level feature aggregator, and a time delayed neural network (TDNN) to compute speaker embedding. We show that the proposed embeddings outperform existing raw-waveform-based speaker embeddings on speaker verification by a large margin. A further analysis of the learned filters shows that the multi-scale encoder attends to different frequency bands at its different scales while resulting in a more flat overall frequency response than any of the single-scale counterparts.	['Zhiyao Duan', 'Fei Jiang', 'Ge Zhu']	2020-10-24	null	null	null	null	['text-independent-speaker-verification']	['speech']	[ 6.95308298e-02 -3.21309924e-01 6.11130781e-02 -7.06350863e-01 -1.12787914e+00 -6.45992756e-01 5.74906409e-01 2.25104019e-02 -3.51925433e-01 3.70655417e-01 5.14944375e-01 -3.42145145e-01 -3.91022563e-02 -3.55776757e-01 -3.29089701e-01 -7.93400109e-01 -4.60427940e-01 -2.13869601e-01 -2.68006586e-02 -2.08125934e-01 5.17506413e-02 4.38434452e-01 -1.62538874e+00 8.21526200e-02 4.75920826e-01 1.14041603e+00 -2.28462622e-01 1.11475587e+00 1.13200687e-01 1.72028154e-01 -1.01137507e+00 -2.82190800e-01 1.70774430e-01 -2.31301308e-01 -1.96362212e-01 -2.89926678e-01 4.16859388e-01 -3.86686295e-01 -6.20961666e-01 9.16247785e-01 9.68469143e-01 3.15542817e-01 5.41651785e-01 -1.05034077e+00 -9.49868262e-01 8.00648808e-01 -1.43205136e-01 4.81893033e-01 3.00694585e-01 -1.66847095e-01 1.06520879e+00 -1.24330401e+00 2.97431624e-03 1.12492287e+00 9.91601110e-01 4.43854868e-01 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-1.30202329e+00 -7.11665511e-01 1.36779338e-01 1.13682318e+00 -4.95247364e-01 -7.64884412e-01 1.98757544e-01]	[14.3972806930542, 6.052150249481201]
386a2095-7263-44bc-95f5-c998efdf632f	mface-multilingual-summarization-with-factual	2212.10622	null	https://arxiv.org/abs/2212.10622v1	https://arxiv.org/pdf/2212.10622v1.pdf	mFACE: Multilingual Summarization with Factual Consistency Evaluation	Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-trained language models and the availability of large-scale datasets. Despite promising results, current models still suffer from generating factually inconsistent summaries, reducing their utility for real-world application. Several recent efforts attempt to address this by devising models that automatically detect factual inconsistencies in machine generated summaries. However, they focus exclusively on English, a language with abundant resources. In this work, we leverage factual consistency evaluation models to improve multilingual summarization. We explore two intuitive approaches to mitigate hallucinations based on the signal provided by a multilingual NLI model, namely data filtering and controlled generation. Experimental results in the 45 languages from the XLSum dataset show gains over strong baselines in both automatic and human evaluation.	['Mirella Lapata', 'Elizabeth Clark', 'Jonathan Herzig', 'Joshua Maynez', 'Shashi Narayan', 'Roee Aharoni']	2022-12-20	null	null	null	null	['abstractive-text-summarization']	['natural-language-processing']	[ 2.76008546e-01 2.68457085e-01 -4.67168510e-01 -2.32994273e-01 -1.71331561e+00 -5.67179859e-01 1.00627792e+00 5.71119606e-01 -3.09528381e-01 1.20134616e+00 1.21487975e+00 6.71356097e-02 2.94323742e-01 -4.73689169e-01 -6.04326308e-01 1.65733732e-02 1.35413229e-01 3.81788492e-01 -1.68864682e-01 -3.73980016e-01 4.52046394e-01 -1.17735788e-01 -1.17398012e+00 6.50633633e-01 1.62706935e+00 3.52343529e-01 -2.34421052e-04 5.41549981e-01 -5.73098585e-02 1.05750275e+00 -1.10506761e+00 -6.55729711e-01 -2.49896482e-01 -9.00630832e-01 -7.90046930e-01 -6.66666478e-02 6.75696135e-01 -1.03544153e-01 -1.01622552e-01 1.07049227e+00 9.44384158e-01 -2.19697312e-01 4.84647214e-01 -1.03203404e+00 -7.78565526e-01 1.09355974e+00 -3.95661324e-01 1.56649888e-01 8.87838364e-01 1.37701884e-01 1.36913466e+00 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d8bbb965-66bc-4cd1-9401-4d04a9c694d9	mitigating-the-position-bias-of-transformer	2101.06980	null	https://arxiv.org/abs/2101.06980v1	https://arxiv.org/pdf/2101.06980v1.pdf	Mitigating the Position Bias of Transformer Models in Passage Re-Ranking	Supervised machine learning models and their evaluation strongly depends on the quality of the underlying dataset. When we search for a relevant piece of information it may appear anywhere in a given passage. However, we observe a bias in the position of the correct answer in the text in two popular Question Answering datasets used for passage re-ranking. The excessive favoring of earlier positions inside passages is an unwanted artefact. This leads to three common Transformer-based re-ranking models to ignore relevant parts in unseen passages. More concerningly, as the evaluation set is taken from the same biased distribution, the models overfitting to that bias overestimate their true effectiveness. In this work we analyze position bias on datasets, the contextualized representations, and their effect on retrieval results. We propose a debiasing method for retrieval datasets. Our results show that a model trained on a position-biased dataset exhibits a significant decrease in re-ranking effectiveness when evaluated on a debiased dataset. We demonstrate that by mitigating the position bias, Transformer-based re-ranking models are equally effective on a biased and debiased dataset, as well as more effective in a transfer-learning setting between two differently biased datasets.	['Allan Hanbury', 'Markus Zlabinger', 'Sophia Althammer', 'Aldo Lipani', 'Sebastian Hofstätter']	2021-01-18	null	null	null	null	['passage-re-ranking']	['natural-language-processing']	[ 1.20403029e-01 -1.62382945e-01 -1.93003818e-01 -2.61189491e-01 -1.47268343e+00 -8.79128277e-01 6.91184640e-01 5.87354422e-01 -6.15322649e-01 9.54995751e-01 5.30857205e-01 -1.30554438e-01 -4.45970416e-01 -8.22372198e-01 -8.99156332e-01 -4.91124511e-01 5.16926408e-01 7.15347052e-01 5.48459888e-01 -5.08908510e-01 8.57708097e-01 1.60539687e-01 -1.59711456e+00 7.67638624e-01 9.15091872e-01 7.00312734e-01 -1.51319027e-01 7.65860736e-01 -4.70171608e-02 1.08943558e+00 -1.16124701e+00 -4.89927351e-01 7.91151822e-02 -5.45409977e-01 -1.34806454e+00 -6.42302811e-01 1.10951209e+00 -3.50619584e-01 -4.21873778e-01 8.95360708e-01 6.44922376e-01 2.67856479e-01 8.69719684e-01 -9.03741241e-01 -7.24789560e-01 6.77591264e-01 -3.22661772e-02 6.92949951e-01 4.65628624e-01 -5.48293114e-01 1.12206507e+00 -7.95544267e-01 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bb7a3026-1fe6-431f-95c3-af7bb9143204	the-treachery-of-images-bayesian-scene	2305.04718	null	https://arxiv.org/abs/2305.04718v1	https://arxiv.org/pdf/2305.04718v1.pdf	The Treachery of Images: Bayesian Scene Keypoints for Deep Policy Learning in Robotic Manipulation	In policy learning for robotic manipulation, sample efficiency is of paramount importance. Thus, learning and extracting more compact representations from camera observations is a promising avenue. However, current methods often assume full observability of the scene and struggle with scale invariance. In many tasks and settings, this assumption does not hold as objects in the scene are often occluded or lie outside the field of view of the camera, rendering the camera observation ambiguous with regard to their location. To tackle this problem, we present BASK, a Bayesian approach to tracking scale-invariant keypoints over time. Our approach successfully resolves inherent ambiguities in images, enabling keypoint tracking on symmetrical objects and occluded and out-of-view objects. We employ our method to learn challenging multi-object robot manipulation tasks from wrist camera observations and demonstrate superior utility for policy learning compared to other representation learning techniques. Furthermore, we show outstanding robustness towards disturbances such as clutter, occlusions, and noisy depth measurements, as well as generalization to unseen objects both in simulation and real-world robotic experiments.	['Abhinav Valada', 'Joschka Boedecker', 'Wolfram Burgard', 'Tim Welschehold', 'Eugenio Chisari', 'Jan Ole von Hartz']	2023-05-08	null	null	null	null	['robot-manipulation']	['robots']	[ 1.87176630e-01 -2.55426347e-01 -3.04995418e-01 4.60647121e-02 -6.15420818e-01 -9.43888247e-01 4.17937726e-01 1.17468737e-01 -5.09417474e-01 7.90026128e-01 -1.34292126e-01 9.35114697e-02 -4.13472027e-01 -1.53514296e-01 -8.70647132e-01 -8.60624492e-01 2.96697579e-03 5.51528037e-01 3.90765220e-01 1.96184710e-01 2.95092493e-01 9.42495048e-01 -1.15319514e+00 -2.74195969e-01 2.35149071e-01 8.38266790e-01 6.38904214e-01 5.62389433e-01 3.84749562e-01 5.42414308e-01 -6.43004954e-01 3.44466299e-01 4.88018483e-01 2.46042132e-01 -3.40085685e-01 4.82958645e-01 5.50462186e-01 -6.09636664e-01 -7.11889744e-01 1.21239150e+00 3.23948711e-01 4.29426908e-01 5.00716269e-01 -1.33028471e+00 -2.65990376e-01 -8.43248442e-02 -6.10375762e-01 1.20341167e-01 3.69165272e-01 1.32007480e-01 6.56435847e-01 -5.00323057e-01 6.59136176e-01 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-1.19621754e+00 -7.60188937e-01 -6.40850186e-01 2.08482459e-01 9.43702757e-01 -1.20145798e+00 -1.71614856e-01 1.59780324e-01]	[4.772202968597412, 0.5573484301567078]
b1ab7f27-9b42-4fcb-8790-b41b2904de6e	fast-parallel-exact-inference-on-bayesian	2212.04241	null	https://arxiv.org/abs/2212.04241v1	https://arxiv.org/pdf/2212.04241v1.pdf	Fast Parallel Exact Inference on Bayesian Networks: Poster	Bayesian networks (BNs) are attractive, because they are graphical and interpretable machine learning models. However, exact inference on BNs is time-consuming, especially for complex problems. To improve the efficiency, we propose a fast BN exact inference solution named Fast-BNI on multi-core CPUs. Fast-BNI enhances the efficiency of exact inference through hybrid parallelism that tightly integrates coarse- and fine-grained parallelism. We also propose techniques to further simplify the bottleneck operations of BN exact inference. Fast-BNI source code is freely available at https://github.com/jjiantong/FastBN.	['Ajmal Mian', 'Atif Mansoor', 'Zeyi Wen', 'Jiantong Jiang']	2022-12-08	null	null	null	null	['interpretable-machine-learning']	['methodology']	[-3.43055874e-01 2.21822821e-02 -4.12761062e-01 -8.23367059e-01 -6.30863011e-01 -2.73113072e-01 2.82438129e-01 -1.63409308e-01 -1.45741001e-01 1.08960795e+00 4.06967923e-02 -8.10591817e-01 -3.24455917e-01 -1.17714095e+00 -6.67434037e-01 -3.58592987e-01 -5.63821010e-03 9.51509058e-01 1.81033134e-01 5.07445991e-01 4.79362309e-02 7.01231420e-01 -8.43192041e-01 3.11339110e-01 5.68123996e-01 7.61325598e-01 -1.28681362e-01 8.91000032e-01 -1.16357073e-01 6.12960339e-01 -3.12248051e-01 -6.24519527e-01 -5.77297620e-02 -1.10921755e-01 -9.01845872e-01 -8.58836293e-01 2.73067385e-01 -7.51346588e-01 -5.28114021e-01 1.32419193e+00 3.30521107e-01 -1.55108109e-01 4.49296385e-01 -1.51252627e+00 -1.96209773e-02 1.14015353e+00 -8.68960023e-01 3.55813056e-01 2.25676551e-01 3.78093570e-02 1.12712765e+00 -8.16030145e-01 1.94970280e-01 1.91775155e+00 7.56840587e-01 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8dc30247-bca8-49c0-acf5-ba53206209a5	graph-mixup-with-soft-alignments	2306.06788	null	https://arxiv.org/abs/2306.06788v1	https://arxiv.org/pdf/2306.06788v1.pdf	Graph Mixup with Soft Alignments	We study graph data augmentation by mixup, which has been used successfully on images. A key operation of mixup is to compute a convex combination of a pair of inputs. This operation is straightforward for grid-like data, such as images, but challenging for graph data. The key difficulty lies in the fact that different graphs typically have different numbers of nodes, and thus there lacks a node-level correspondence between graphs. In this work, we propose S-Mixup, a simple yet effective mixup method for graph classification by soft alignments. Specifically, given a pair of graphs, we explicitly obtain node-level correspondence via computing a soft assignment matrix to match the nodes between two graphs. Based on the soft assignments, we transform the adjacency and node feature matrices of one graph, so that the transformed graph is aligned with the other graph. In this way, any pair of graphs can be mixed directly to generate an augmented graph. We conduct systematic experiments to show that S-Mixup can improve the performance and generalization of graph neural networks (GNNs) on various graph classification tasks. In addition, we show that S-Mixup can increase the robustness of GNNs against noisy labels.	['Na Zou', 'Shuiwang Ji', 'Meng Liu', 'Zhimeng Jiang', 'Hongyi Ling']	2023-06-11	null	null	null	null	['graph-classification']	['graphs']	[ 4.30029243e-01 2.33165264e-01 -1.10969879e-02 -3.38164061e-01 -2.34469146e-01 -6.59706116e-01 4.10128206e-01 4.03198779e-01 -9.18569118e-02 2.27741241e-01 -1.50448829e-01 -2.48301759e-01 5.50794750e-02 -1.07939863e+00 -8.07764411e-01 -7.97574401e-01 7.24967942e-02 2.88208842e-01 2.99350228e-02 -2.34568685e-01 -2.77351767e-01 3.86686653e-01 -9.97305632e-01 6.40493482e-02 9.08336580e-01 7.40519822e-01 1.06178321e-01 4.48552132e-01 -1.68809026e-01 5.21909416e-01 -4.04724211e-01 -5.10433197e-01 5.54173648e-01 -5.06035626e-01 -6.49433732e-01 1.86638549e-01 6.61773324e-01 1.52403593e-01 -6.90910220e-01 1.55439401e+00 2.64492989e-01 9.34973210e-02 5.00434756e-01 -1.57507813e+00 -7.71256149e-01 9.65663612e-01 -5.98035574e-01 -1.85555682e-01 1.26522765e-01 -6.51056543e-02 1.25593877e+00 -5.33955157e-01 4.64384705e-01 1.26889169e+00 6.52944803e-01 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a4903040-bf35-4261-8f51-6b0e908be92c	time-series-regression	2006.12672	null	https://arxiv.org/abs/2006.12672v3	https://arxiv.org/pdf/2006.12672v3.pdf	Time Series Extrinsic Regression	This paper studies Time Series Extrinsic Regression (TSER): a regression task of which the aim is to learn the relationship between a time series and a continuous scalar variable; a task closely related to time series classification (TSC), which aims to learn the relationship between a time series and a categorical class label. This task generalizes time series forecasting (TSF), relaxing the requirement that the value predicted be a future value of the input series or primarily depend on more recent values. In this paper, we motivate and study this task, and benchmark existing solutions and adaptations of TSC algorithms on a novel archive of 19 TSER datasets which we have assembled. Our results show that the state-of-the-art TSC algorithm Rocket, when adapted for regression, achieves the highest overall accuracy compared to adaptations of other TSC algorithms and state-of-the-art machine learning (ML) algorithms such as XGBoost, Random Forest and Support Vector Regression. More importantly, we show that much research is needed in this field to improve the accuracy of ML models. We also find evidence that further research has excellent prospects of improving upon these straightforward baselines.	['Geoffrey I. Webb', 'Francois Petitjean', 'Christoph Bergmeir', 'Chang Wei Tan']	2020-06-23	null	null	null	null	['time-series-regression']	['time-series']	[ 3.48474622e-01 -3.86319995e-01 -6.28393054e-01 -6.90764725e-01 -7.64835238e-01 -6.63859844e-01 9.47002947e-01 3.84606153e-01 -2.92132258e-01 7.70638824e-01 1.36339232e-01 -8.00415337e-01 -4.53012258e-01 -5.12495100e-01 -7.25133657e-01 -7.12338567e-01 -7.13476598e-01 4.83874679e-01 -8.03727955e-02 -4.37036008e-01 3.52306902e-01 4.57839310e-01 -1.52964091e+00 2.71691859e-01 3.96916568e-01 1.42349505e+00 -4.48676378e-01 6.18787825e-01 8.13613981e-02 9.53823745e-01 -5.89706004e-01 1.40728084e-02 3.09303850e-01 -2.99424380e-01 -5.92861593e-01 -3.63737941e-01 6.44263625e-02 6.82417899e-02 5.09414449e-03 2.27613270e-01 -4.77605611e-02 3.23284686e-01 7.95790553e-01 -1.81061888e+00 -3.73738587e-01 6.42255604e-01 -6.15269840e-01 4.53463435e-01 2.26834148e-01 -1.19598620e-01 1.06560254e+00 -2.32360154e-01 4.69421655e-01 1.08305919e+00 1.10263705e+00 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ee384bbf-152d-48d0-a3af-6b2efe38ef38	multi-layer-pruning-framework-for-compressing	1811.08342	null	http://arxiv.org/abs/1811.08342v1	http://arxiv.org/pdf/1811.08342v1.pdf	Multi-layer Pruning Framework for Compressing Single Shot MultiBox Detector	We propose a framework for compressing state-of-the-art Single Shot MultiBox Detector (SSD). The framework addresses compression in the following stages: Sparsity Induction, Filter Selection, and Filter Pruning. In the Sparsity Induction stage, the object detector model is sparsified via an improved global threshold. In Filter Selection & Pruning stage, we select and remove filters using sparsity statistics of filter weights in two consecutive convolutional layers. This results in the model with the size smaller than most existing compact architectures. We evaluate the performance of our framework with multiple datasets and compare over multiple methods. Experimental results show that our method achieves state-of-the-art compression of 6.7X and 4.9X on PASCAL VOC dataset on models SSD300 and SSD512 respectively. We further show that the method produces maximum compression of 26X with SSD512 on German Traffic Sign Detection Benchmark (GTSDB). Additionally, we also empirically show our method's adaptability for classification based architecture VGG16 on datasets CIFAR and German Traffic Sign Recognition Benchmark (GTSRB) achieving a compression rate of 125X and 200X with the reduction in flops by 90.50% and 96.6% respectively with no loss of accuracy. In addition to this, our method does not require any special libraries or hardware support for the resulting compressed models.	['Manikandan. R', 'Vinay P. Namboodiri', 'Neeraj Matiyali', 'Pravendra Singh']	2018-11-20	null	null	null	null	['traffic-sign-recognition', 'traffic-sign-detection']	['computer-vision', 'computer-vision']	[ 3.33412290e-01 -2.11801350e-01 -8.10964406e-02 -3.16190839e-01 -4.85872924e-01 -2.32162029e-01 4.85066652e-01 -3.97718623e-02 -8.33077431e-01 4.12415504e-01 1.29638493e-01 -3.80819499e-01 -8.30591470e-02 -6.28277183e-01 -7.56382823e-01 -4.04387355e-01 1.66474432e-01 6.44481555e-02 1.03591669e+00 2.77862828e-02 3.71645600e-01 5.18898308e-01 -2.00342369e+00 6.73377275e-01 5.01997769e-01 1.18302369e+00 3.89986962e-01 1.10770249e+00 1.91466838e-01 8.46146762e-01 -4.86312091e-01 -5.41659772e-01 8.60566080e-01 -1.22480951e-01 -3.87505054e-01 1.68540984e-01 1.12104499e+00 -6.28495514e-01 -7.85848618e-01 1.10926199e+00 4.33650315e-01 -8.30508173e-02 4.30374920e-01 -9.44873810e-01 2.25031562e-02 6.14000440e-01 -8.58941972e-01 5.94864011e-01 -4.06677485e-01 3.60931456e-01 8.83257329e-01 -8.57898474e-01 8.38900149e-01 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848de19d-3fd4-41b5-86d3-03c0c95444b4	dasgil-domain-adaptation-for-semantic-and	2010.00573	null	https://arxiv.org/abs/2010.00573v2	https://arxiv.org/pdf/2010.00573v2.pdf	DASGIL: Domain Adaptation for Semantic and Geometric-aware Image-based Localization	Long-Term visual localization under changing environments is a challenging problem in autonomous driving and mobile robotics due to season, illumination variance, etc. Image retrieval for localization is an efficient and effective solution to the problem. In this paper, we propose a novel multi-task architecture to fuse the geometric and semantic information into the multi-scale latent embedding representation for visual place recognition. To use the high-quality ground truths without any human effort, the effective multi-scale feature discriminator is proposed for adversarial training to achieve the domain adaptation from synthetic virtual KITTI dataset to real-world KITTI dataset. The proposed approach is validated on the Extended CMU-Seasons dataset and Oxford RobotCar dataset through a series of crucial comparison experiments, where our performance outperforms state-of-the-art baselines for retrieval-based localization and large-scale place recognition under the challenging environment.	['Zhijian Qiao', 'Ming Cheng', 'Hesheng Wang', 'Hanjiang Hu', 'Zhe Liu']	2020-10-01	null	null	null	null	['image-based-localization']	['computer-vision']	[-8.48290175e-02 -5.53050816e-01 -2.18897551e-01 -4.34940845e-01 -1.16940951e+00 -7.43507683e-01 7.05932975e-01 -2.92802572e-01 -8.73247206e-01 7.25040734e-01 -2.87794024e-01 9.91205312e-03 -6.12587407e-02 -4.75362450e-01 -1.05247819e+00 -7.56389916e-01 2.43502986e-02 4.61383462e-01 3.99681568e-01 -4.00253892e-01 3.93791407e-01 5.84870994e-01 -1.66682684e+00 -2.63894320e-01 8.48864615e-01 9.01729941e-01 5.55054903e-01 5.09457648e-01 1.59606989e-02 5.06879747e-01 -4.02170509e-01 1.58114031e-01 5.31723917e-01 1.42237708e-01 -2.87259161e-01 -3.06027472e-01 7.63815224e-01 -1.74949877e-02 -5.12391269e-01 1.05392575e+00 6.95160627e-01 5.07141054e-01 7.68906772e-01 -1.58030093e+00 -7.02859879e-01 -3.10083896e-01 -4.94330972e-01 8.71190429e-02 2.36014515e-01 1.55501112e-01 5.42847455e-01 -1.03237724e+00 8.50841641e-01 1.25275707e+00 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7dd75949-125a-4b8f-9677-5b823773f46f	contextual-graph-reasoning-networks	null	null	https://openreview.net/forum?id=Efiwpsy0ZE_	https://openreview.net/pdf?id=Efiwpsy0ZE_	Contextual Graph Reasoning Networks	Graph Reasoning has shown great potential recently in modeling long-range dependencies, which are crucial for various computer vision tasks. However, the graph representation learned by existing methods is not effective enough as the relation between feature and graph is under-explored. In this work, we propose a novel method named Contextual Graph Reasoning (CGR) that learns a context-aware relation between feature and graph. This is achieved by constructing the projection matrix based on a global set of descriptors during graph projection, and calibrating the evolved graph based on the self-attention of all nodes during graph reprojection. Therefore, contextual information is well explored in both graph projection and reprojection with our method. To verify the effectiveness of our method, we conduct extensive experiments on semantic segmentation, instance segmentation, and 2D human pose estimation. Our method consistently achieves remarkable improvements over state-of-the-art methods, demonstrating the effectiveness and generalization ability of our method.	['Ming Wu', 'Ming Lu', 'Chuang Zhang', 'Mingming Gong', 'Yangyuxuan Kang', 'Jiaming Liu', 'Zhaoqing Wang']	2021-01-01	null	null	null	null	['2d-human-pose-estimation']	['computer-vision']	[ 7.24130571e-02 2.92015940e-01 -4.99146953e-02 -4.80628222e-01 -2.45775104e-01 -3.53384465e-01 5.89367211e-01 1.21094562e-01 -8.26462582e-02 3.33748341e-01 6.39613047e-02 6.18630163e-02 -8.50070640e-02 -7.17709482e-01 -6.85701072e-01 -5.66087484e-01 1.78792849e-01 5.57445943e-01 4.27429169e-01 -2.84704566e-01 1.69342369e-01 4.55984563e-01 -1.09253347e+00 -3.41884524e-01 9.43121135e-01 6.33884728e-01 3.04558367e-01 3.65014344e-01 -1.56801388e-01 4.30345833e-01 -2.34335274e-01 -5.58823466e-01 2.33060896e-01 -5.30737221e-01 -5.84166408e-01 5.34025133e-01 3.17265362e-01 -4.03682590e-02 -4.68830347e-01 1.23461115e+00 2.58937865e-01 3.57045770e-01 3.94728303e-01 -1.18857276e+00 -6.38660192e-01 4.61404055e-01 -7.58836210e-01 -2.10396141e-01 4.74785596e-01 1.17433835e-02 1.16462708e+00 -7.36050248e-01 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-6.95960581e-01 -2.53291279e-01 -5.95186949e-01 -5.11010110e-01 7.17047572e-01 -1.29864663e-01 -1.01267159e+00 -2.12326944e-01 -4.52633575e-02]	[9.607000350952148, 0.8933359384536743]
837d4d67-dd3e-4df9-852f-92f25b7ce853	coarse-grained-and-emergent-distributed	2011.08138	null	https://arxiv.org/abs/2011.08138v2	https://arxiv.org/pdf/2011.08138v2.pdf	Coarse-grained and emergent distributed parameter systems from data	We explore the derivation of distributed parameter system evolution laws (and in particular, partial differential operators and associated partial differential equations, PDEs) from spatiotemporal data. This is, of course, a classical identification problem; our focus here is on the use of manifold learning techniques (and, in particular, variations of Diffusion Maps) in conjunction with neural network learning algorithms that allow us to attempt this task when the dependent variables, and even the independent variables of the PDE are not known a priori and must be themselves derived from the data. The similarity measure used in Diffusion Maps for dependent coarse variable detection involves distances between local particle distribution observations; for independent variable detection we use distances between local short-time dynamics. We demonstrate each approach through an illustrative established PDE example. Such variable-free, emergent space identification algorithms connect naturally with equation-free multiscale computation tools.	['Ioannis Kevrekidis', 'Tom Bertalan', 'Felix P. Kemeth', 'Hassan Arbabi']	2020-11-16	null	null	null	null	['variable-detection']	['natural-language-processing']	[-7.24778175e-02 -4.39784527e-01 5.21179378e-01 3.86575967e-01 -2.38430083e-01 -7.89269328e-01 7.61002123e-01 2.84915745e-01 -4.84117597e-01 1.05663157e+00 -2.84158617e-01 3.32706445e-03 -5.48960328e-01 -7.82760680e-01 -1.10243946e-01 -1.23935986e+00 -5.85444927e-01 7.46372402e-01 2.11648792e-01 -4.92363274e-01 1.95339307e-01 8.19148540e-01 -1.08716500e+00 -5.55568814e-01 7.49353945e-01 8.42789292e-01 -2.48689819e-02 1.16268599e+00 -1.47364184e-01 7.89189279e-01 -3.61731738e-01 3.33083540e-01 1.37048364e-01 -7.42839277e-01 -6.61944747e-01 -1.04656722e-02 -8.51343870e-02 1.64769813e-01 -2.83126622e-01 1.02927041e+00 2.92364269e-01 6.57663643e-01 1.32855356e+00 -9.95499790e-01 -9.32927907e-01 1.66668609e-01 -2.69696832e-01 7.95843482e-01 -5.12224846e-02 9.82092246e-02 5.83943307e-01 -7.74818718e-01 7.47711897e-01 9.86542344e-01 8.56183350e-01 2.41873085e-01 -1.62369251e+00 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4.02630419e-01 8.79473746e-01 -8.46273959e-01 -9.18015659e-01 2.61957258e-01]	[6.546254634857178, 3.756570816040039]
818c1684-cd00-491e-952e-8a257b231edc	classification-of-breast-cancer-histology-1	1802.09424	null	http://arxiv.org/abs/1802.09424v1	http://arxiv.org/pdf/1802.09424v1.pdf	Classification of breast cancer histology images using transfer learning	Breast cancer is one of the leading causes of mortality in women. Early detection and treatment are imperative for improving survival rates, which have steadily increased in recent years as a result of more sophisticated computer-aided-diagnosis (CAD) systems. A critical component of breast cancer diagnosis relies on histopathology, a laborious and highly subjective process. Consequently, CAD systems are essential to reduce inter-rater variability and supplement the analyses conducted by specialists. In this paper, a transfer-learning based approach is proposed, for the task of breast histology image classification into four tissue sub-types, namely, normal, benign, \textit{in situ} carcinoma and invasive carcinoma. The histology images, provided as part of the BACH 2018 grand challenge, were first normalized to correct for color variations resulting from inconsistencies during slide preparation. Subsequently, image patches were extracted and used to fine-tune Google`s Inception-V3 and ResNet50 convolutional neural networks (CNNs), both pre-trained on the ImageNet database, enabling them to learn domain-specific features, necessary to classify the histology images. The ResNet50 network (based on residual learning) achieved a test classification accuracy of 97.50% for four classes, outperforming the Inception-V3 network which achieved an accuracy of 91.25%.	['Amirabbas Davari', 'Sulaiman Vesal', 'Stephan Ellmann', 'Nishant Ravikumar', 'Andreas Maier']	2018-02-26	null	null	null	null	['classification-of-breast-cancer-histology']	['medical']	[ 3.55380833e-01 6.32628724e-02 -1.11739390e-01 -3.08347851e-01 -1.09726501e+00 -5.18139839e-01 2.92041510e-01 5.12526453e-01 -5.71545541e-01 5.93880832e-01 -2.14925632e-01 -5.74584484e-01 6.11704923e-02 -8.42793345e-01 -3.51050645e-01 -9.25276875e-01 6.58400962e-03 4.18713212e-01 3.65339667e-02 6.20769970e-02 -6.72409534e-02 6.66238785e-01 -1.30877030e+00 5.99183977e-01 7.61451840e-01 1.15583348e+00 -9.67906415e-02 9.30116236e-01 -6.07944727e-02 6.70002639e-01 -4.29833263e-01 -4.03093487e-01 3.81438918e-02 -3.12157005e-01 -7.20841944e-01 -1.52639747e-01 2.53050625e-01 -6.73967898e-02 -2.04379275e-01 1.01534688e+00 6.18035734e-01 -5.52013338e-01 8.07669580e-01 -8.62113237e-01 -3.38611096e-01 2.29408070e-01 -5.29528916e-01 2.64294058e-01 -2.70027488e-01 2.11929813e-01 6.90842152e-01 -7.42556989e-01 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81b52682-78f7-42c9-8fb7-ef12d3d3be79	syngec-syntax-enhanced-grammatical-error	2210.12484	null	https://arxiv.org/abs/2210.12484v1	https://arxiv.org/pdf/2210.12484v1.pdf	SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser	This work proposes a syntax-enhanced grammatical error correction (GEC) approach named SynGEC that effectively incorporates dependency syntactic information into the encoder part of GEC models. The key challenge for this idea is that off-the-shelf parsers are unreliable when processing ungrammatical sentences. To confront this challenge, we propose to build a tailored GEC-oriented parser (GOPar) using parallel GEC training data as a pivot. First, we design an extended syntax representation scheme that allows us to represent both grammatical errors and syntax in a unified tree structure. Then, we obtain parse trees of the source incorrect sentences by projecting trees of the target correct sentences. Finally, we train GOPar with such projected trees. For GEC, we employ the graph convolution network to encode source-side syntactic information produced by GOPar, and fuse them with the outputs of the Transformer encoder. Experiments on mainstream English and Chinese GEC datasets show that our proposed SynGEC approach consistently and substantially outperforms strong baselines and achieves competitive performance. Our code and data are all publicly available at https://github.com/HillZhang1999/SynGEC.	['Min Zhang', 'Chen Li', 'Zuyi Bao', 'Zhenghua Li', 'Bo Zhang', 'Yue Zhang']	2022-10-22	null	null	null	null	['syntax-representation', 'grammatical-error-correction']	['natural-language-processing', 'natural-language-processing']	[ 1.42256796e-01 3.97181064e-01 4.13207442e-01 -7.11234510e-01 -9.96414185e-01 -3.72025579e-01 -6.72428310e-02 1.11033723e-01 -2.60851204e-01 4.05176640e-01 3.96607637e-01 -7.16570318e-01 5.39900184e-01 -9.18988466e-01 -9.15773273e-01 -2.20501170e-01 2.37359419e-01 2.42835194e-01 3.64081608e-03 -3.32740366e-01 1.12127140e-01 -2.16986611e-01 -8.93849611e-01 4.53648865e-01 1.35912931e+00 4.57850635e-01 6.50458336e-01 8.33933353e-01 -3.65138501e-01 7.62443841e-01 -4.35316652e-01 -1.06281388e+00 -1.70215994e-01 -5.30941248e-01 -8.64742100e-01 -4.12207454e-01 1.53047934e-01 -7.55681247e-02 3.59529778e-02 1.43988037e+00 2.64901638e-01 -3.52717429e-01 9.43260491e-02 -7.06237733e-01 -1.00895321e+00 1.30995667e+00 -1.55877873e-01 -1.03598647e-02 2.09724888e-01 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25fe8d96-6ac0-4255-a4bc-0015827c1ce6	quantum-heavy-tailed-bandits	2301.09680	null	https://arxiv.org/abs/2301.09680v1	https://arxiv.org/pdf/2301.09680v1.pdf	Quantum Heavy-tailed Bandits	In this paper, we study multi-armed bandits (MAB) and stochastic linear bandits (SLB) with heavy-tailed rewards and quantum reward oracle. Unlike the previous work on quantum bandits that assumes bounded/sub-Gaussian distributions for rewards, here we investigate the quantum bandits problem under a weaker assumption that the distributions of rewards only have bounded $(1+v)$-th moment for some $v\in (0,1]$. In order to achieve regret improvements for heavy-tailed bandits, we first propose a new quantum mean estimator for heavy-tailed distributions, which is based on the Quantum Monte Carlo Mean Estimator and achieves a quadratic improvement of estimation error compared to the classical one. Based on our quantum mean estimator, we focus on quantum heavy-tailed MAB and SLB and propose quantum algorithms based on the Upper Confidence Bound (UCB) framework for both problems with $\Tilde{O}(T^{\frac{1-v}{1+v}})$ regrets, polynomially improving the dependence in terms of $T$ as compared to classical (near) optimal regrets of $\Tilde{O}(T^{\frac{1}{1+v}})$, where $T$ is the number of rounds. Finally, experiments also support our theoretical results and show the effectiveness of our proposed methods.	['Di Wang', 'Vaneet Aggarwal', 'Chaowen Guan', 'Yulian Wu']	2023-01-23	null	null	null	null	['multi-armed-bandits']	['miscellaneous']	[ 6.18323237e-02 2.34899580e-01 -4.37594861e-01 -3.16272497e-01 -1.23658955e+00 -7.17990756e-01 -1.04383327e-01 2.60345340e-01 -8.38867605e-01 1.36969864e+00 -3.85141909e-01 -7.28241146e-01 -7.27573335e-01 -1.20820749e+00 -1.07054770e+00 -9.87685621e-01 -2.50650018e-01 6.94753110e-01 -5.26912883e-02 -2.40220025e-01 3.53602171e-01 1.47750214e-01 -1.11435437e+00 -3.12763512e-01 9.61132646e-01 1.41750658e+00 -3.03733379e-01 8.24303269e-01 -6.97156787e-02 4.92913604e-01 -4.15689290e-01 -8.84247541e-01 4.56806630e-01 -7.01129019e-01 -8.57622862e-01 -4.30243194e-01 3.76847647e-02 -6.45371616e-01 -5.31774223e-01 1.62453687e+00 3.91734183e-01 1.58676222e-01 6.08047187e-01 -8.66962373e-01 -5.29377341e-01 1.02284849e+00 -5.41980743e-01 2.30636910e-01 1.59467963e-04 -1.60344824e-01 1.39195633e+00 3.49148035e-01 2.51882583e-01 9.99870479e-01 3.39166284e-01 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dea55566-9932-4a0f-9fc2-e3d21ce8bbbc	synthehicle-multi-vehicle-multi-camera	2208.14167	null	https://arxiv.org/abs/2208.14167v1	https://arxiv.org/pdf/2208.14167v1.pdf	Synthehicle: Multi-Vehicle Multi-Camera Tracking in Virtual Cities	Smart City applications such as intelligent traffic routing or accident prevention rely on computer vision methods for exact vehicle localization and tracking. Due to the scarcity of accurately labeled data, detecting and tracking vehicles in 3D from multiple cameras proves challenging to explore. We present a massive synthetic dataset for multiple vehicle tracking and segmentation in multiple overlapping and non-overlapping camera views. Unlike existing datasets, which only provide tracking ground truth for 2D bounding boxes, our dataset additionally contains perfect labels for 3D bounding boxes in camera- and world coordinates, depth estimation, and instance, semantic and panoptic segmentation. The dataset consists of 17 hours of labeled video material, recorded from 340 cameras in 64 diverse day, rain, dawn, and night scenes, making it the most extensive dataset for multi-target multi-camera tracking so far. We provide baselines for detection, vehicle re-identification, and single- and multi-camera tracking. Code and data are publicly available.	['Gerhard Rigoll', 'Stefan Hörmann', 'Johannes Gilg', 'Torben Teepe', 'Junpeng Chen', 'Fabian Herzog']	2022-08-30	null	null	null	null	['panoptic-segmentation', 'vehicle-re-identification']	['computer-vision', 'computer-vision']	[-8.69275630e-02 -7.11460710e-01 -3.04818422e-01 -3.18326414e-01 -9.85372663e-01 -1.22034717e+00 7.18332529e-01 -1.17721327e-01 -4.41032857e-01 5.27067065e-01 -4.20756668e-01 -3.21621120e-01 4.47173685e-01 -5.11983395e-01 -7.04266667e-01 -8.53575468e-01 1.11773692e-01 8.05206120e-01 7.64478207e-01 2.24266648e-01 5.28964102e-02 8.77172172e-01 -1.50796652e+00 -2.70043343e-01 2.41852000e-01 7.36934304e-01 1.34940967e-01 9.34306979e-01 8.47481564e-02 2.23550484e-01 -2.36826196e-01 -4.88391101e-01 6.25529766e-01 1.40442953e-01 -2.04916373e-01 5.56050658e-01 1.13389611e+00 -7.23335445e-01 -3.38641495e-01 1.16592860e+00 1.64970562e-01 -2.73518395e-02 4.89317715e-01 -1.82181060e+00 -1.18079238e-01 -1.44030869e-01 -9.14454579e-01 5.33402383e-01 3.70019078e-01 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f442b268-8e36-453c-a206-8e169850db89	video2stylegan-disentangling-local-and-global	2205.13996	null	https://arxiv.org/abs/2205.13996v2	https://arxiv.org/pdf/2205.13996v2.pdf	Video2StyleGAN: Disentangling Local and Global Variations in a Video	Image editing using a pretrained StyleGAN generator has emerged as a powerful paradigm for facial editing, providing disentangled controls over age, expression, illumination, etc. However, the approach cannot be directly adopted for video manipulations. We hypothesize that the main missing ingredient is the lack of fine-grained and disentangled control over face location, face pose, and local facial expressions. In this work, we demonstrate that such a fine-grained control is indeed achievable using pretrained StyleGAN by working across multiple (latent) spaces (namely, the positional space, the W+ space, and the S space) and combining the optimization results across the multiple spaces. Building on this enabling component, we introduce Video2StyleGAN that takes a target image and driving video(s) to reenact the local and global locations and expressions from the driving video in the identity of the target image. We evaluate the effectiveness of our method over multiple challenging scenarios and demonstrate clear improvements over alternative approaches.	['Peter Wonka', 'Niloy J. Mitra', 'Peihao Zhu', 'Rameen Abdal']	2022-05-27	null	null	null	null	['facial-editing']	['computer-vision']	[ 4.45978045e-01 2.49016374e-01 1.10039629e-01 -3.09243262e-01 -4.09759969e-01 -8.81191730e-01 8.41105163e-01 -6.69823170e-01 -1.82186216e-01 6.51312351e-01 4.14185107e-01 2.30170771e-01 1.39135450e-01 -3.54147643e-01 -7.40718842e-01 -7.26454377e-01 1.73252746e-01 -5.69828860e-02 -4.56798732e-01 -2.58065820e-01 7.93092698e-03 6.08442485e-01 -1.60936272e+00 -2.75199804e-02 4.57477450e-01 8.06047499e-01 -3.26640040e-01 8.09963524e-01 3.39522570e-01 6.64500058e-01 -5.60828149e-01 -4.23925191e-01 5.94583392e-01 -5.83465040e-01 -2.59370565e-01 4.60922003e-01 9.50469136e-01 -5.05470634e-01 -4.97916609e-01 7.61584699e-01 3.79894346e-01 2.69500166e-03 2.87583590e-01 -1.48414993e+00 -4.27314043e-01 2.70493388e-01 -7.19166875e-01 -3.61565381e-01 4.12831664e-01 2.30554804e-01 8.16381872e-01 -7.96342075e-01 8.58358383e-01 1.26810467e+00 3.68921340e-01 8.12947869e-01 -1.48837829e+00 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eef5a157-516b-4db7-a049-98f3ddb1698c	learning-word-embeddings-from-intrinsic-and	1608.05852	null	http://arxiv.org/abs/1608.05852v1	http://arxiv.org/pdf/1608.05852v1.pdf	Learning Word Embeddings from Intrinsic and Extrinsic Views	While word embeddings are currently predominant for natural language processing, most of existing models learn them solely from their contexts. However, these context-based word embeddings are limited since not all words' meaning can be learned based on only context. Moreover, it is also difficult to learn the representation of the rare words due to data sparsity problem. In this work, we address these issues by learning the representations of words by integrating their intrinsic (descriptive) and extrinsic (contextual) information. To prove the effectiveness of our model, we evaluate it on four tasks, including word similarity, reverse dictionaries,Wiki link prediction, and document classification. Experiment results show that our model is powerful in both word and document modeling.	['Zheng Zhang', 'Jifan Chen', 'Xuanjing Huang', 'Xipeng Qiu', 'Qi Zhang', 'Kan Chen']	2016-08-20	null	null	null	null	['learning-word-embeddings']	['methodology']	[-1.56066179e-01 -3.64845812e-01 -7.25643754e-01 -4.11807179e-01 -2.23807573e-01 -3.60447168e-01 7.23752022e-01 7.14318573e-01 -6.71324909e-01 4.79137808e-01 5.61188936e-01 -3.60712886e-01 -1.06655255e-01 -8.63727152e-01 -2.22346172e-01 -3.59850854e-01 2.01249465e-01 2.00004131e-01 6.55495003e-02 -3.78335059e-01 3.13107282e-01 -7.64541402e-02 -1.37174416e+00 5.87731265e-02 1.03987503e+00 6.86152816e-01 3.71272743e-01 1.99774995e-01 -7.69257784e-01 4.65806335e-01 -3.88580024e-01 -3.25719029e-01 -2.00116828e-01 -2.20984057e-01 -5.30450046e-01 -4.01770324e-01 1.13609955e-01 -2.30388641e-01 -6.64800823e-01 1.20183277e+00 2.54992038e-01 3.46472263e-01 9.04162765e-01 -9.23971653e-01 -1.44417584e+00 7.21686721e-01 -3.35049063e-01 2.54523277e-01 4.76369262e-01 -2.96510965e-01 1.65119731e+00 -1.23185849e+00 3.45575571e-01 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5438fbe1-6fb8-4213-adc6-6e381c13864f	robust-offline-reinforcement-learning-with	2210.10469	null	https://arxiv.org/abs/2210.10469v1	https://arxiv.org/pdf/2210.10469v1.pdf	Robust Offline Reinforcement Learning with Gradient Penalty and Constraint Relaxation	A promising paradigm for offline reinforcement learning (RL) is to constrain the learned policy to stay close to the dataset behaviors, known as policy constraint offline RL. However, existing works heavily rely on the purity of the data, exhibiting performance degradation or even catastrophic failure when learning from contaminated datasets containing impure trajectories of diverse levels. e.g., expert level, medium level, etc., while offline contaminated data logs exist commonly in the real world. To mitigate this, we first introduce gradient penalty over the learned value function to tackle the exploding Q-functions. We then relax the closeness constraints towards non-optimal actions with critic weighted constraint relaxation. Experimental results show that the proposed techniques effectively tame the non-optimal trajectories for policy constraint offline RL methods, evaluated on a set of contaminated D4RL Mujoco and Adroit datasets.	['Zhiqiang Xu', 'Peilin Zhao', 'Deheng Ye', 'Liu Liu', 'Ke Xu', 'Chengqian Gao']	2022-10-19	null	null	null	null	['d4rl']	['robots']	[-1.80983037e-01 -1.16803579e-01 -4.43313330e-01 1.50649086e-01 -9.57647800e-01 -1.02183199e+00 4.83969122e-01 1.59676418e-01 -6.66060805e-01 1.34455478e+00 -3.91551182e-02 -3.51738751e-01 -4.18918908e-01 -5.29457033e-01 -1.01982892e+00 -9.07258153e-01 -4.23527569e-01 3.96526635e-01 8.28762949e-02 1.01002585e-02 2.41983324e-01 4.81346518e-01 -1.19816196e+00 -2.95231007e-02 1.00680363e+00 8.78455937e-01 -5.97903989e-02 5.24311304e-01 2.70301282e-01 9.58440721e-01 -8.36507738e-01 -1.76566929e-01 6.44829631e-01 -1.79015145e-01 -3.28273505e-01 1.70182422e-01 -9.05473158e-02 -7.12882638e-01 -4.54528093e-01 1.36669159e+00 3.78462106e-01 3.23371589e-01 3.36101621e-01 -1.42010725e+00 -4.06541288e-01 6.37634039e-01 -5.39530396e-01 2.57473826e-01 1.92571506e-01 7.23974824e-01 6.70304239e-01 -3.59547138e-01 5.38324416e-01 1.16842794e+00 3.93958032e-01 4.97308761e-01 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-7.16283083e-01 -6.19112253e-01 5.24669230e-01 6.31854415e-01 -9.14842486e-01 -4.91697967e-01 -3.77010345e-01]	[4.110424518585205, 2.217465877532959]
7e2e2f30-57b0-4a5e-98e8-461991e4fff7	bayesian-recurrent-neural-network-models-for	1711.00636	null	http://arxiv.org/abs/1711.00636v2	http://arxiv.org/pdf/1711.00636v2.pdf	Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data	Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used in the machine learning and dynamical systems literature to represent complex dynamical or sequential relationships between variables. More recently, as deep learning models have become more common, RNNs have been used to forecast increasingly complicated systems. Dynamical spatio-temporal processes represent a class of complex systems that can potentially benefit from these types of models. Although the RNN literature is expansive and highly developed, uncertainty quantification is often ignored. Even when considered, the uncertainty is generally quantified without the use of a rigorous framework, such as a fully Bayesian setting. Here we attempt to quantify uncertainty in a more formal framework while maintaining the forecast accuracy that makes these models appealing, by presenting a Bayesian RNN model for nonlinear spatio-temporal forecasting. Additionally, we make simple modifications to the basic RNN to help accommodate the unique nature of nonlinear spatio-temporal data. The proposed model is applied to a Lorenz simulation and two real-world nonlinear spatio-temporal forecasting applications.	['Christopher K. Wikle', 'Patrick L. McDermott']	2017-11-02	null	null	null	null	['spatio-temporal-forecasting']	['time-series']	[-2.96811104e-01 -3.06006849e-01 1.64140880e-01 -3.18136871e-01 -1.23616964e-01 -4.56328899e-01 1.04420912e+00 -2.51339942e-01 -1.57533839e-01 1.01592088e+00 1.92832157e-01 -5.57369232e-01 -6.35376334e-01 -7.37089038e-01 -2.77838379e-01 -8.69259357e-01 -4.46548223e-01 2.18550995e-01 -1.43656377e-02 -2.51820505e-01 -7.58722872e-02 7.01116621e-01 -1.38328588e+00 -4.70053107e-01 6.81516886e-01 9.25455928e-01 1.08827412e-01 7.19057679e-01 1.13574438e-01 9.32222664e-01 -4.71140772e-01 3.29701696e-03 2.90899009e-01 -2.81671137e-01 -2.66615689e-01 -4.26271617e-01 -3.04704338e-01 -2.68075168e-01 -6.48414016e-01 9.20750380e-01 3.44080001e-01 6.70538604e-01 8.84577632e-01 -9.35958624e-01 -5.64922512e-01 5.18751144e-01 -1.41337842e-01 6.13291562e-01 -2.74862528e-01 1.35499075e-01 6.51717305e-01 -6.07542038e-01 3.41571085e-02 1.28677237e+00 8.94275784e-01 2.01269925e-01 -1.09281957e+00 -5.65399945e-01 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-5.60993195e-01 8.25674176e-01 -9.06175315e-01 -7.78145909e-01 -7.49657303e-02]	[6.663160800933838, 3.3277149200439453]
5b3d05fc-3e11-4832-a045-adafe6dcf888	signet-intrinsic-image-decomposition-by-a	2208.14369	null	https://arxiv.org/abs/2208.14369v1	https://arxiv.org/pdf/2208.14369v1.pdf	SIGNet: Intrinsic Image Decomposition by a Semantic and Invariant Gradient Driven Network for Indoor Scenes	Intrinsic image decomposition (IID) is an under-constrained problem. Therefore, traditional approaches use hand crafted priors to constrain the problem. However, these constraints are limited when coping with complex scenes. Deep learning-based approaches learn these constraints implicitly through the data, but they often suffer from dataset biases (due to not being able to include all possible imaging conditions). In this paper, a combination of the two is proposed. Component specific priors like semantics and invariant features are exploited to obtain semantically and physically plausible reflectance transitions. These transitions are used to steer a progressive CNN with implicit homogeneity constraints to decompose reflectance and shading maps. An ablation study is conducted showing that the use of the proposed priors and progressive CNN increase the IID performance. State of the art performance on both our proposed dataset and the standard real-world IIW dataset shows the effectiveness of the proposed method. Code is made available at https://github.com/Morpheus3000/SIGNet	['Theo Gevers', 'Arjan Gijsenij', 'Sezer Karaoglu', 'Partha Das']	2022-08-30	null	null	null	null	['intrinsic-image-decomposition']	['computer-vision']	[ 5.37667274e-01 -3.47194336e-02 3.04971263e-02 -4.97167647e-01 -3.86266679e-01 -2.55101532e-01 5.29769301e-01 -4.34907407e-01 -2.89619565e-01 5.81201911e-01 2.98571169e-01 2.03267243e-02 -8.25298205e-02 -5.68421483e-01 -6.06627643e-01 -7.15097129e-01 2.82836825e-01 1.23395883e-01 2.68934369e-01 -1.16473466e-01 2.43552938e-01 4.97160941e-01 -1.84771430e+00 1.78113624e-01 9.50722456e-01 9.46910799e-01 5.73463321e-01 3.47140759e-01 -5.71738891e-02 4.49738234e-01 -2.44826585e-01 5.38661331e-02 8.81690085e-01 -2.45672911e-01 -5.77372432e-01 3.35605085e-01 5.97912014e-01 -5.16687274e-01 -1.24419011e-01 9.54480827e-01 5.14131248e-01 3.80467653e-01 4.08259124e-01 -1.08605468e+00 -4.79149371e-01 3.06243654e-02 -8.00794005e-01 -2.20996901e-01 2.59720087e-01 2.69138843e-01 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0e6b05dd-510a-4649-8edd-dda39814ee42	are-my-deep-learning-systems-fair-an	null	null	http://proceedings.neurips.cc/paper/2021/hash/fdda6e957f1e5ee2f3b311fe4f145ae1-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/fdda6e957f1e5ee2f3b311fe4f145ae1-Paper.pdf	Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training	Deep learning (DL) systems have been gaining popularity in critical tasks such as credit evaluation and crime prediction. Such systems demand fairness. Recent work shows that DL software implementations introduce variance: identical DL training runs (i.e., identical network, data, configuration, software, and hardware) with a fixed seed produce different models. Such variance could make DL models and networks violate fairness compliance laws, resulting in negative social impact. In this paper, we conduct the first empirical study to quantify the impact of software implementation on the fairness and its variance of DL systems. Our study of 22 mitigation techniques and five baselines reveals up to 12.6% fairness variance across identical training runs with identical seeds. In addition, most debiasing algorithms have a negative impact on the model such as reducing model accuracy, increasing fairness variance, or increasing accuracy variance. Our literature survey shows that while fairness is gaining popularity in artificial intelligence (AI) related conferences, only 34.4% of the papers use multiple identical training runs to evaluate their approach, raising concerns about their results’ validity. We call for better fairness evaluation and testing protocols to improve fairness and fairness variance of DL systems as well as DL research validity and reproducibility at large.	['Sameena Shah', 'Jiahao Chen', 'YaoLiang Yu', 'Lin Tan', 'Jungwon Kim', 'Zeou Hu', 'Thibaud Lutellier', 'Hung Pham', 'Shangshu Qian']	2021-12-01	null	https://openreview.net/forum?id=kLWGdQYsmC5	https://openreview.net/pdf?id=kLWGdQYsmC5	neurips-2021-12	['crime-prediction']	['miscellaneous']	[-4.21354383e-01 -4.62966040e-02 -3.11708331e-01 -7.27519631e-01 -1.95109826e-02 -5.63765049e-01 5.22964537e-01 1.38392031e-01 -8.05242419e-01 6.70087457e-01 -4.44814004e-02 -7.91443348e-01 1.89114198e-01 -7.51952827e-01 -6.09086990e-01 -2.12510064e-01 2.67386041e-03 2.21752867e-01 2.51583429e-03 5.99017404e-02 6.28925323e-01 6.23874009e-01 -1.08011949e+00 -5.02475426e-02 9.19289649e-01 6.78070545e-01 -7.91836083e-01 6.03950202e-01 -9.79546681e-02 1.49789786e+00 -1.22041261e+00 -1.16372991e+00 5.13143480e-01 -1.29200250e-01 -7.67384529e-01 -6.51001871e-01 9.68498707e-01 -9.86541927e-01 -3.58238190e-01 1.09506714e+00 7.65734375e-01 -1.18421331e-01 4.04823989e-01 -2.06225371e+00 -7.60439157e-01 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91800dde-236a-442f-92cf-0816727f30b7	play-parametrically-conditioned-layout	2301.11529	null	https://arxiv.org/abs/2301.11529v2	https://arxiv.org/pdf/2301.11529v2.pdf	PLay: Parametrically Conditioned Layout Generation using Latent Diffusion	Layout design is an important task in various design fields, including user interface, document, and graphic design. As this task requires tedious manual effort by designers, prior works have attempted to automate this process using generative models, but commonly fell short of providing intuitive user controls and achieving design objectives. In this paper, we build a conditional latent diffusion model, PLay, that generates parametrically conditioned layouts in vector graphic space from user-specified guidelines, which are commonly used by designers for representing their design intents in current practices. Our method outperforms prior works across three datasets on metrics including FID and FD-VG, and in user study. Moreover, it brings a novel and interactive experience to professional layout design processes.	['Yang Li', 'Gang Li', 'Forrest Huang', 'Chin-Yi Cheng']	2023-01-27	null	null	null	null	['layout-design']	['computer-vision']	[-3.41323726e-02 -3.74559194e-01 -1.96543962e-01 -3.96234065e-01 -1.08602196e-01 -8.40957701e-01 5.04037559e-01 -2.45168746e-01 1.48108333e-01 3.45065087e-01 7.69786239e-01 -7.17103422e-01 -4.49272722e-01 -7.94791460e-01 -1.93048909e-01 -3.52813303e-01 4.14323628e-01 3.25552255e-01 -2.05572218e-01 -3.40846628e-02 5.68642199e-01 3.90965164e-01 -1.14050388e+00 3.12604666e-01 1.03122556e+00 4.53398615e-01 2.88267523e-01 7.09295154e-01 -5.01150727e-01 7.86623716e-01 -5.57172537e-01 -3.82632494e-01 -3.15303117e-01 -5.47147453e-01 -4.33978111e-01 1.94646940e-01 1.62577033e-02 -3.73696983e-01 -9.44859236e-02 9.00232434e-01 3.95373672e-01 8.47508535e-02 1.27115059e+00 -1.15147030e+00 -1.71352482e+00 8.26057911e-01 -5.69802046e-01 -5.03875911e-01 5.66048682e-01 2.32083425e-01 1.10574341e+00 -7.66953468e-01 6.25997841e-01 1.29299760e+00 3.33913505e-01 3.53180259e-01 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db23f98d-e5bb-439a-8177-9d818270d5f4	accidental-light-probes	2301.05211	null	https://arxiv.org/abs/2301.05211v3	https://arxiv.org/pdf/2301.05211v3.pdf	Accidental Light Probes	Recovering lighting in a scene from a single image is a fundamental problem in computer vision. While a mirror ball light probe can capture omnidirectional lighting, light probes are generally unavailable in everyday images. In this work, we study recovering lighting from accidental light probes (ALPs) -- common, shiny objects like Coke cans, which often accidentally appear in daily scenes. We propose a physically-based approach to model ALPs and estimate lighting from their appearances in single images. The main idea is to model the appearance of ALPs by photogrammetrically principled shading and to invert this process via differentiable rendering to recover incidental illumination. We demonstrate that we can put an ALP into a scene to allow high-fidelity lighting estimation. Our model can also recover lighting for existing images that happen to contain an ALP.	['Deqing Sun', 'Jiajun Wu', 'Noah Snavely', 'Richard Szeliski', 'Charles Herrmann', 'Samir Agarwala', 'Hong-Xing Yu']	2023-01-12	null	http://openaccess.thecvf.com//content/CVPR2023/html/Yu_Accidental_Light_Probes_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Yu_Accidental_Light_Probes_CVPR_2023_paper.pdf	cvpr-2023-1	['lighting-estimation']	['computer-vision']	[ 7.87578940e-01 -2.38263443e-01 5.71212888e-01 -3.26238453e-01 -3.21069986e-01 -6.75022781e-01 4.27208364e-01 -7.04099774e-01 6.59753159e-02 4.35634077e-01 -2.27878354e-02 -4.12459999e-01 2.17076346e-01 -6.85915709e-01 -1.08483577e+00 -6.24094427e-01 7.94649303e-01 1.94663793e-01 4.79153953e-02 -1.38467439e-02 1.66265756e-01 8.84790599e-01 -1.80252564e+00 6.05579428e-02 5.74817538e-01 7.20537305e-01 5.04959762e-01 7.81231642e-01 -1.09690063e-01 6.21859968e-01 -2.90611207e-01 -2.51277119e-01 7.64282942e-01 -2.82095850e-01 -2.59936780e-01 6.41534925e-01 1.01959968e+00 -7.98410535e-01 -1.74660385e-01 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