Split (2)

train · 44.6k rows

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ff09a4f5-6b79-4743-9396-9fe5101aae00	structvpr-distill-structural-knowledge-with	2212.00937	null	https://arxiv.org/abs/2212.00937v4	https://arxiv.org/pdf/2212.00937v4.pdf	StructVPR: Distill Structural Knowledge with Weighting Samples for Visual Place Recognition	Visual place recognition (VPR) is usually considered as a specific image retrieval problem. Limited by existing training frameworks, most deep learning-based works cannot extract sufficiently stable global features from RGB images and rely on a time-consuming re-ranking step to exploit spatial structural information for better performance. In this paper, we propose StructVPR, a novel training architecture for VPR, to enhance structural knowledge in RGB global features and thus improve feature stability in a constantly changing environment. Specifically, StructVPR uses segmentation images as a more definitive source of structural knowledge input into a CNN network and applies knowledge distillation to avoid online segmentation and inference of seg-branch in testing. Considering that not all samples contain high-quality and helpful knowledge, and some even hurt the performance of distillation, we partition samples and weigh each sample's distillation loss to enhance the expected knowledge precisely. Finally, StructVPR achieves impressive performance on several benchmarks using only global retrieval and even outperforms many two-stage approaches by a large margin. After adding additional re-ranking, ours achieves state-of-the-art performance while maintaining a low computational cost.	['Sanping Zhou', 'Nanning Zheng', 'Shitao Chen', 'Ruotong Wang', 'Jingwen Fu', 'Yanqing Shen']	2022-12-02	null	http://openaccess.thecvf.com//content/CVPR2023/html/Shen_StructVPR_Distill_Structural_Knowledge_With_Weighting_Samples_for_Visual_Place_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Shen_StructVPR_Distill_Structural_Knowledge_With_Weighting_Samples_for_Visual_Place_CVPR_2023_paper.pdf	cvpr-2023-1	['visual-place-recognition']	['computer-vision']	[-4.57707420e-02 -3.37864637e-01 -3.50806296e-01 -3.84050041e-01 -1.03963077e+00 -5.41287780e-01 3.57372642e-01 1.84185341e-01 -7.69540489e-01 5.53688765e-01 -2.03564540e-01 -3.33582014e-01 -2.31624603e-01 -9.59776580e-01 -1.05764377e+00 -7.29306400e-01 2.43367687e-01 3.52578729e-01 5.19301593e-01 -9.22418907e-02 2.79210269e-01 8.05381656e-01 -1.66363096e+00 9.00199115e-02 9.48251128e-01 1.25881803e+00 4.34613198e-01 2.35873610e-01 -1.26092702e-01 7.27036834e-01 -4.38344777e-01 -2.43738890e-01 4.90395188e-01 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fab69a5b-cf56-4228-add1-c481a8b062d6	network-of-steel-neural-font-style-transfer	2001.03659	null	https://arxiv.org/abs/2001.03659v1	https://arxiv.org/pdf/2001.03659v1.pdf	Network of Steel: Neural Font Style Transfer from Heavy Metal to Corporate Logos	We introduce a method for transferring style from the logos of heavy metal bands onto corporate logos using a VGG16 network. We establish the contribution of different layers and loss coefficients to the learning of style, minimization of artefacts and maintenance of readability of corporate logos. We find layers and loss coefficients that produce a good tradeoff between heavy metal style and corporate logo readability. This is the first step both towards sparse font style transfer and corporate logo decoration using generative networks. Heavy metal and corporate logos are very different artistically, in the way they emphasize emotions and readability, therefore training a model to fuse the two is an interesting problem.	['Aram Ter-Sarkisov']	2020-01-10	null	null	null	null	['font-style-transfer']	['computer-vision']	[-3.99928773e-03 2.06648991e-01 6.74295202e-02 -5.21738350e-01 2.45876476e-01 -6.66168988e-01 5.38001657e-01 -1.88814923e-01 -3.35221924e-02 7.81405866e-01 2.80518919e-01 7.78766721e-02 -2.15865038e-02 -1.03018975e+00 -4.28929150e-01 -3.73456508e-01 4.48034763e-01 2.17004672e-01 -3.17463636e-01 -4.00645763e-01 1.57704890e-01 7.69588947e-01 -1.12286282e+00 3.66274089e-01 8.01381826e-01 1.07306719e+00 -5.92185482e-02 8.34153712e-01 -1.62877068e-01 7.50203252e-01 -8.27974260e-01 -8.37591410e-01 3.83280575e-01 -6.29828334e-01 -7.01617241e-01 4.36631590e-01 7.11132824e-01 1.24591269e-01 -1.34720981e-01 9.04955268e-01 5.96982956e-01 6.43728971e-02 1.19666040e+00 -1.09742844e+00 -1.50829720e+00 5.55869043e-01 -3.81760359e-01 -3.50083411e-01 -7.02320412e-02 -5.66699579e-02 1.04816067e+00 -6.71711445e-01 6.76643848e-01 1.37131584e+00 6.66820705e-01 5.27237356e-01 -1.40208769e+00 -2.75972396e-01 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8.14032197e-01 -2.26593137e-01 -2.30612814e-01 -2.25101247e-01 5.16653419e-01]	[11.636006355285645, -0.5044199228286743]
bd8d7ec6-5e4b-42a3-adb1-8bbcccccaef9	evaluation-of-differentially-constrained	2304.05116	null	https://arxiv.org/abs/2304.05116v2	https://arxiv.org/pdf/2304.05116v2.pdf	Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction	Given their flexibility and encouraging performance, deep-learning models are becoming standard for motion prediction in autonomous driving. However, with great flexibility comes a lack of interpretability and possible violations of physical constraints. Accompanying these data-driven methods with differentially-constrained motion models to provide physically feasible trajectories is a promising future direction. The foundation for this work is a previously introduced graph-neural-network-based model, MTP-GO. The neural network learns to compute the inputs to an underlying motion model to provide physically feasible trajectories. This research investigates the performance of various motion models in combination with numerical solvers for the prediction task. The study shows that simpler models, such as low-order integrator models, are preferred over more complex, e.g., kinematic models, to achieve accurate predictions. Further, the numerical solver can have a substantial impact on performance, advising against commonly used first-order methods like Euler forward. Instead, a second-order method like Heun's can greatly improve predictions.	['Erik Frisk', 'Björn Olofsson', 'Joel Oskarsson', 'Theodor Westny']	2023-04-11	null	null	null	null	['motion-prediction', 'trajectory-prediction']	['computer-vision', 'computer-vision']	[-4.43736315e-01 6.64770603e-02 -3.86978269e-01 -7.92131871e-02 7.28664026e-02 -2.72581995e-01 6.29624367e-01 -2.12220982e-01 -3.00544918e-01 8.43662977e-01 4.12011370e-02 -8.03969562e-01 -3.17659557e-01 -6.95950925e-01 -5.42532504e-01 -6.49876595e-01 -2.02724174e-01 2.93760896e-01 2.10491315e-01 -6.09464467e-01 1.87667042e-01 9.56617236e-01 -1.53936803e+00 -2.81614065e-01 9.18781221e-01 5.79179168e-01 8.95987600e-02 7.29534507e-01 7.56141357e-03 1.06001472e+00 5.14490679e-02 -9.78208035e-02 4.44661975e-01 -3.96539241e-01 -5.26734710e-01 -2.64604181e-01 4.62201610e-02 -5.27841330e-01 -6.95556045e-01 6.31589234e-01 1.86798781e-01 8.56850982e-01 5.10225177e-01 -1.39223897e+00 -6.35126159e-02 2.82247007e-01 1.06450971e-02 1.15989156e-01 -3.86275351e-02 8.85132074e-01 7.37098873e-01 -7.48732328e-01 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80a41ec6-0aa4-4d5f-babf-a791091c5f59	fastmapsvm-classifying-complex-objects-using	2204.05112	null	https://arxiv.org/abs/2204.05112v3	https://arxiv.org/pdf/2204.05112v3.pdf	FastMapSVM: Classifying Complex Objects Using the FastMap Algorithm and Support-Vector Machines	Neural Networks and related Deep Learning methods are currently at the leading edge of technologies used for classifying objects. However, they generally demand large amounts of time and data for model training; and their learned models can sometimes be difficult to interpret. In this paper, we advance FastMapSVM -- an interpretable Machine Learning framework for classifying complex objects -- as an advantageous alternative to Neural Networks for general classification tasks. FastMapSVM extends the applicability of Support-Vector Machines (SVMs) to domains with complex objects by combining the complementary strengths of FastMap and SVMs. FastMap is an efficient linear-time algorithm that maps complex objects to points in a Euclidean space while preserving pairwise domain-specific distances between them. We demonstrate the efficiency and effectiveness of FastMapSVM in the context of classifying seismograms. We show that its performance, in terms of precision, recall, and accuracy, is comparable to that of other state-of-the-art methods. However, compared to other methods, FastMapSVM uses significantly smaller amounts of time and data for model training. It also provides a perspicuous visualization of the objects and the classification boundaries between them. We expect FastMapSVM to be viable for classification tasks in many other real-world domains.	['Nori Nakata', 'T. K. Satish Kumar', 'Ang Li', 'Kushal Sharma', 'Malcolm C. A. 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624ca608-da5c-4c54-af5b-8e3b21b32037	adversarial-learning-for-neural-dialogue	1701.06547	null	http://arxiv.org/abs/1701.06547v5	http://arxiv.org/pdf/1701.06547v5.pdf	Adversarial Learning for Neural Dialogue Generation	In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances. We cast the task as a reinforcement learning (RL) problem where we jointly train two systems, a generative model to produce response sequences, and a discriminator---analagous to the human evaluator in the Turing test--- to distinguish between the human-generated dialogues and the machine-generated ones. The outputs from the discriminator are then used as rewards for the generative model, pushing the system to generate dialogues that mostly resemble human dialogues. In addition to adversarial training we describe a model for adversarial {\em evaluation} that uses success in fooling an adversary as a dialogue evaluation metric, while avoiding a number of potential pitfalls. Experimental results on several metrics, including adversarial evaluation, demonstrate that the adversarially-trained system generates higher-quality responses than previous baselines.	['Sébastien Jean', 'Will Monroe', 'Dan Jurafsky', 'Jiwei Li', 'Alan Ritter', 'Tianlin Shi']	2017-01-23	adversarial-learning-for-neural-dialogue-1	https://aclanthology.org/D17-1230	https://aclanthology.org/D17-1230.pdf	emnlp-2017-9	['dialogue-evaluation']	['natural-language-processing']	[ 4.65729326e-01 1.03313172e+00 5.95323384e-01 -5.17493367e-01 -1.29644632e+00 -1.17903233e+00 1.03540504e+00 -4.06087309e-01 -3.67059708e-01 1.16135037e+00 4.08840746e-01 -4.37395841e-01 7.66951621e-01 -9.97489214e-01 -5.92353880e-01 -4.70475793e-01 1.99088573e-01 1.10135424e+00 -1.69296071e-01 -9.30004478e-01 4.93716300e-02 -4.79550613e-03 -7.57556915e-01 5.06610572e-01 8.57010961e-01 5.07543564e-01 -3.83992136e-01 1.49674082e+00 1.59094647e-01 1.23929775e+00 -1.49837554e+00 -9.26134288e-01 2.51304418e-01 -1.22430360e+00 -1.48542869e+00 6.26962185e-02 7.65279457e-02 -7.64414728e-01 -5.22433043e-01 8.70818675e-01 7.28697717e-01 2.69466192e-01 8.24644685e-01 -1.19787574e+00 -7.87994683e-01 8.17317128e-01 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fdd92ae5-8f41-44f3-a500-581643f567c1	fake-hilsa-fish-detection-using-machine	2201.02853	null	https://arxiv.org/abs/2201.02853v1	https://arxiv.org/pdf/2201.02853v1.pdf	Fake Hilsa Fish Detection Using Machine Vision	Hilsa is the national fish of Bangladesh. Bangladesh is earning a lot of foreign currency by exporting this fish. Unfortunately, in recent days, some unscrupulous businessmen are selling fake Hilsa fishes to gain profit. The Sardines and Sardinella are the most sold in the market as Hilsa. The government agency of Bangladesh, namely Bangladesh Food Safety Authority said that these fake Hilsa fish contain high levels of cadmium and lead which are detrimental for humans. In this research, we have proposed a method that can readily identify original Hilsa fish and fake Hilsa fish. Based on the research available on online literature, we are the first to do research on identifying original Hilsa fish. We have collected more than 16,000 images of original and counterfeit Hilsa fish. To classify these images, we have used several deep learning-based models. Then, the performance has been compared between them. Among those models, DenseNet201 achieved the highest accuracy of 97.02%.	['Zakia Zaman', 'Abdur Rahman', 'Jannatul Ferdous Ani', 'Mirajul Islam']	2022-01-08	null	null	null	null	['fish-detection']	['computer-vision']	[-5.83499312e-01 -9.47412977e-04 3.97874057e-01 -4.04582247e-02 -9.02684256e-02 -4.80845273e-01 1.54882669e-01 3.09705317e-01 -6.01205826e-01 4.69133735e-01 1.33137777e-01 5.99753857e-03 2.92040259e-01 -1.00560594e+00 -6.90715671e-01 -8.37917507e-01 -6.44796044e-02 3.04082632e-01 1.36206076e-01 -2.96730250e-01 5.72519541e-01 8.03745031e-01 -1.13592219e+00 1.01949967e-01 4.92752433e-01 5.26772082e-01 2.16321111e-01 3.28567833e-01 8.29944760e-03 6.77525759e-01 -6.82216763e-01 -8.27076912e-01 6.19167924e-01 -6.55393064e-01 -4.44549054e-01 -2.80294269e-01 3.70728016e-01 -8.65940034e-01 -3.03333730e-01 1.35839498e+00 3.44247371e-01 -4.46252465e-01 7.15781868e-01 -1.27218902e+00 -9.61756945e-01 1.03279698e+00 -6.23175502e-01 9.23857838e-02 -7.00765401e-02 3.83611530e-01 3.46709490e-01 -8.56093943e-01 9.44139585e-02 1.59922802e+00 1.07553232e+00 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dca53be6-dfc1-417b-a515-ebd31c017919	trajectory-prediction-with-vision-a-survey	2303.13354	null	https://arxiv.org/abs/2303.13354v1	https://arxiv.org/pdf/2303.13354v1.pdf	Trajectory-Prediction with Vision: A Survey	To plan a safe and efficient route, an autonomous vehicle should anticipate future trajectories of other agents around it. Trajectory prediction is an extremely challenging task which recently gained a lot of attention in the autonomous vehicle research community. Trajectory-prediction forecasts future state of all the dynamic agents in the scene given their current and past states. A good prediction model can prevent collisions on the road, and hence the ultimate goal for autonomous vehicles: Collision rate: collisions per Million miles. The objective of this paper is to provide an overview of the field trajectory-prediction. We categorize the relevant algorithms into different classes so that researchers can follow through the trends in the trajectory-prediction research field. Moreover we also touch upon the background knowledge required to formulate a trajectory-prediction problem.	['Apoorv Singh']	2023-03-15	null	null	null	null	['trajectory-prediction']	['computer-vision']	[-2.80429542e-01 1.50385439e-01 -7.28348553e-01 -3.92774403e-01 -1.06005520e-01 -4.48892385e-01 8.83441627e-01 1.05011083e-01 -3.63902390e-01 7.96037495e-01 5.25628887e-02 -6.68452084e-01 -3.58879685e-01 -1.16705918e+00 -5.25544941e-01 -5.62862754e-01 -6.13751888e-01 4.30996329e-01 6.68371499e-01 -4.66295749e-01 3.88153583e-01 1.05796278e+00 -1.77085531e+00 -1.72639325e-01 8.37439895e-01 5.00298858e-01 2.89382219e-01 7.60823429e-01 -1.52812436e-01 7.96115279e-01 5.45186251e-02 -5.46660483e-01 1.39275953e-01 -5.81669249e-02 -7.17725039e-01 -2.61315912e-01 -2.81367242e-01 -3.14615488e-01 -1.01785874e+00 8.20325375e-01 -4.00022000e-01 5.44787765e-01 9.14659917e-01 -2.14239931e+00 1.89101189e-01 3.54451567e-01 9.11519006e-02 3.89048725e-01 1.15563892e-01 2.39061713e-01 5.39780140e-01 -6.54598534e-01 8.61515939e-01 9.13125813e-01 6.16615117e-01 4.86279249e-01 -6.73721731e-01 -3.21061462e-01 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f03947bd-a371-45c0-929d-63a77d02d009	a-gating-model-for-bias-calibration-in	2203.04195	null	https://arxiv.org/abs/2203.04195v1	https://arxiv.org/pdf/2203.04195v1.pdf	A Gating Model for Bias Calibration in Generalized Zero-shot Learning	Generalized zero-shot learning (GZSL) aims at training a model that can generalize to unseen class data by only using auxiliary information. One of the main challenges in GZSL is a biased model prediction toward seen classes caused by overfitting on only available seen class data during training. To overcome this issue, we propose a two-stream autoencoder-based gating model for GZSL. Our gating model predicts whether the query data is from seen classes or unseen classes, and utilizes separate seen and unseen experts to predict the class independently from each other. This framework avoids comparing the biased prediction scores for seen classes with the prediction scores for unseen classes. In particular, we measure the distance between visual and attribute representations in the latent space and the cross-reconstruction space of the autoencoder. These distances are utilized as complementary features to characterize unseen classes at different levels of data abstraction. Also, the two-stream autoencoder works as a unified framework for the gating model and the unseen expert, which makes the proposed method computationally efficient. We validate our proposed method in four benchmark image recognition datasets. In comparison with other state-of-the-art methods, we achieve the best harmonic mean accuracy in SUN and AWA2, and the second best in CUB and AWA1. Furthermore, our base model requires at least 20% less number of model parameters than state-of-the-art methods relying on generative models.	['Ghassan AlRegib', 'Gukyeong Kwon']	2022-03-08	null	null	null	null	['generalized-zero-shot-learning', 'generalized-zero-shot-learning']	['computer-vision', 'methodology']	[ 3.75503115e-02 -7.16818795e-02 -8.33368003e-02 -3.74668032e-01 -6.39506519e-01 -2.11669490e-01 4.97547537e-01 6.45010173e-02 -2.93119013e-01 6.03927314e-01 -1.09728336e-01 1.93478942e-01 4.69388114e-03 -1.24564683e+00 -6.68904364e-01 -1.02374256e+00 3.84053022e-01 5.51608860e-01 4.47019786e-01 -2.58868397e-03 -7.30780810e-02 3.82106155e-01 -2.09372663e+00 5.18326819e-01 6.01229608e-01 1.28472507e+00 4.15368736e-01 4.54474509e-01 -1.91961244e-01 6.57194078e-01 -4.88215327e-01 -2.44342253e-01 1.15752639e-02 -4.60146636e-01 -3.33262742e-01 3.71205285e-02 4.04753745e-01 -2.02482626e-01 -1.89739883e-01 9.35879648e-01 6.11763954e-01 3.33931208e-01 8.12563002e-01 -1.23624384e+00 -7.89531887e-01 1.05761103e-01 -1.55818090e-01 1.70113370e-01 2.21517621e-04 6.16059639e-03 9.73137677e-01 -1.19102895e+00 4.33274001e-01 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58abe31f-174c-4289-94d0-83363ecb8bcf	cylinder3d-an-effective-3d-framework-for	2008.01550	null	https://arxiv.org/abs/2008.01550v1	https://arxiv.org/pdf/2008.01550v1.pdf	Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation	State-of-the-art methods for large-scale driving-scene LiDAR semantic segmentation often project and process the point clouds in the 2D space. The projection methods includes spherical projection, bird-eye view projection, etc. Although this process makes the point cloud suitable for the 2D CNN-based networks, it inevitably alters and abandons the 3D topology and geometric relations. A straightforward solution to tackle the issue of 3D-to-2D projection is to keep the 3D representation and process the points in the 3D space. In this work, we first perform an in-depth analysis for different representations and backbones in 2D and 3D spaces, and reveal the effectiveness of 3D representations and networks on LiDAR segmentation. Then, we develop a 3D cylinder partition and a 3D cylinder convolution based framework, termed as Cylinder3D, which exploits the 3D topology relations and structures of driving-scene point clouds. Moreover, a dimension-decomposition based context modeling module is introduced to explore the high-rank context information in point clouds in a progressive manner. We evaluate the proposed model on a large-scale driving-scene dataset, i.e. SematicKITTI. Our method achieves state-of-the-art performance and outperforms existing methods by 6% in terms of mIoU.	['Hui Zhou', 'Hongsheng Li', 'Xinge Zhu', 'Xiao Song', 'Zhe Wang', 'Yuexin Ma', 'Dahua Lin']	2020-08-04	null	null	null	null	['lidar-semantic-segmentation']	['computer-vision']	[-6.23972677e-02 -2.72552550e-01 7.27606118e-02 -3.74561101e-01 -1.87529042e-01 -5.19701362e-01 6.46319389e-01 -2.87885576e-01 -2.18140393e-01 3.52186672e-02 2.73540616e-03 -5.60137630e-01 -2.52761096e-01 -1.11425734e+00 -6.16037011e-01 -3.72732252e-01 2.52959460e-01 8.97776246e-01 6.29857421e-01 -1.28139213e-01 3.71513814e-01 1.14422059e+00 -1.88350272e+00 1.21354289e-01 7.64091134e-01 8.79343390e-01 2.99780875e-01 3.77150357e-01 -1.02366507e+00 -6.66956082e-02 -1.27450258e-01 -3.46658409e-01 5.37748635e-01 4.54814225e-01 -5.94683290e-01 2.17907697e-01 8.99038553e-01 -1.27565432e-02 -3.49244297e-01 1.04451203e+00 2.59661198e-01 4.71194685e-02 7.42865324e-01 -1.18892968e+00 -5.76979697e-01 3.43293846e-02 -7.88274169e-01 1.32600516e-02 -1.02326972e-02 2.40705125e-02 7.73603499e-01 -1.50911534e+00 6.59866989e-01 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a8f6dd1c-6e71-4657-bd4d-cff862980004	revbifpn-the-fully-reversible-bidirectional	2206.14098	null	https://arxiv.org/abs/2206.14098v2	https://arxiv.org/pdf/2206.14098v2.pdf	RevBiFPN: The Fully Reversible Bidirectional Feature Pyramid Network	This work introduces RevSilo, the first reversible bidirectional multi-scale feature fusion module. Like other reversible methods, RevSilo eliminates the need to store hidden activations by recomputing them. However, existing reversible methods do not apply to multi-scale feature fusion and are, therefore, not applicable to a large class of networks. Bidirectional multi-scale feature fusion promotes local and global coherence and has become a de facto design principle for networks targeting spatially sensitive tasks, e.g., HRNet (Sun et al., 2019a) and EfficientDet (Tan et al., 2020). These networks achieve state-of-the-art results across various computer vision tasks when paired with high-resolution inputs. However, training them requires substantial accelerator memory for saving large, multi-resolution activations. These memory requirements inherently cap the size of neural networks, limiting improvements that come from scale. Operating across resolution scales, RevSilo alleviates these issues. Stacking RevSilos, we create RevBiFPN, a fully reversible bidirectional feature pyramid network. RevBiFPN is competitive with networks such as EfficientNet while using up to 19.8x lesser training memory for image classification. When fine-tuned on MS COCO, RevBiFPN provides up to a 2.5% boost in AP over HRNet using fewer MACs and a 2.4x reduction in training-time memory.	['Dennis Decoste', 'Joel Hestness', 'Anshul Samar', 'Abhay Gupta', 'Vithursan Thangarasa', 'Vitaliy Chiley']	2022-06-28	null	null	null	null	['classification']	['methodology']	[ 1.93764791e-01 -3.73958200e-01 -1.32109553e-01 -3.86134297e-01 -6.64657593e-01 -3.84642720e-01 5.15977919e-01 -2.20611364e-01 -7.65891135e-01 8.73714507e-01 1.15225045e-02 -2.73791939e-01 5.42772934e-02 -1.06706214e+00 -9.26047981e-01 -6.76470459e-01 1.32939324e-01 -2.01481804e-01 6.06851101e-01 -4.09077466e-01 1.37384892e-01 1.02920091e+00 -1.60041249e+00 5.94398558e-01 2.60907918e-01 1.07933605e+00 2.95749336e-01 7.74103701e-01 1.61354959e-01 7.28354692e-01 -5.83008528e-01 -3.32801640e-02 2.86044210e-01 -1.02495737e-01 -7.14319050e-01 -7.45714545e-01 9.29559529e-01 -3.15808028e-01 -6.89304352e-01 7.46306360e-01 7.09486604e-01 2.03473523e-01 1.32405996e-01 -1.06171608e+00 -8.37200105e-01 6.86358929e-01 -6.40898049e-01 7.85511076e-01 -2.86133826e-01 1.49047812e-02 9.10045505e-01 -1.42017198e+00 5.12295723e-01 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67f4a4c7-7d35-4ee8-bf73-b5f4626ea3bb	machine-learning-and-bioinformatics-for	2208.03139	null	https://arxiv.org/abs/2208.03139v1	https://arxiv.org/pdf/2208.03139v1.pdf	Machine Learning and Bioinformatics for Diagnosis Analysis of Obesity Spectrum Disorders	Globally, the number of obese patients has doubled due to sedentary lifestyles and improper dieting. The tremendous increase altered human genetics, and health. According to the world health organization, Life expectancy dropped from 80 to 75 years, as obese people struggle with different chronic diseases. This report will address the problems of obesity in children and adults using ML datasets to feature, predict, and analyze the causes of obesity. By engaging neural ML networks, we will explore neural control using diffusion tensor imaging to consider body fats, BMI, waist \& hip ratio circumference of obese patients. To predict the present and future causes of obesity with ML, we will discuss ML techniques like decision trees, SVM, RF, GBM, LASSO, BN, and ANN and use datasets implement the stated algorithms. Different theoretical literature from experts ML \& Bioinformatics experiments will be outlined in this report while making recommendations on how to advance ML for predicting obesity and other chronic diseases.	['Amin Gasmi']	2022-08-05	null	null	null	null	['machine-learning', 'machine-learning']	['methodology', 'miscellaneous']	[ 5.99176362e-02 3.36759657e-01 -6.72948301e-01 -4.02018487e-01 3.13056231e-01 6.05378533e-03 -5.81253171e-01 3.86367261e-01 -9.78291929e-02 8.26121926e-01 4.05240387e-01 -1.81833327e-01 -3.64267945e-01 -5.37997007e-01 2.98110954e-02 -4.57376897e-01 -8.29970658e-01 4.60179210e-01 -6.24564171e-01 1.58368349e-01 1.50650442e-01 3.36021483e-02 -9.71517622e-01 -2.97786705e-02 1.42056382e+00 1.15743184e+00 -2.49428242e-01 7.04654396e-01 3.94812459e-03 6.35996699e-01 1.65379658e-01 -4.63551253e-01 3.63311857e-01 -2.97308952e-01 -2.77640998e-01 -1.65403083e-01 5.91279149e-01 -2.04253986e-01 8.69525298e-02 1.09647250e+00 9.37784910e-01 -1.25462666e-01 4.72179770e-01 -1.19591773e+00 -7.08885431e-01 8.70307028e-01 -8.48201454e-01 1.05935916e-01 1.55315772e-01 2.13319197e-01 4.77744609e-01 -3.67711872e-01 3.67690325e-01 1.10437143e+00 1.28148818e+00 8.51100326e-01 -1.40313876e+00 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-5.98390470e-04 2.48265594e-01 -5.35935342e-01 -2.77853876e-01 6.80507496e-02]	[8.200566291809082, 5.485738277435303]
0a0880ec-1030-4519-a0bc-ba0709c40d49	improving-question-answering-model-robustness	2104.08678	null	https://arxiv.org/abs/2104.08678v3	https://arxiv.org/pdf/2104.08678v3.pdf	Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation	Despite recent progress, state-of-the-art question answering models remain vulnerable to a variety of adversarial attacks. While dynamic adversarial data collection, in which a human annotator tries to write examples that fool a model-in-the-loop, can improve model robustness, this process is expensive which limits the scale of the collected data. In this work, we are the first to use synthetic adversarial data generation to make question answering models more robust to human adversaries. We develop a data generation pipeline that selects source passages, identifies candidate answers, generates questions, then finally filters or re-labels them to improve quality. Using this approach, we amplify a smaller human-written adversarial dataset to a much larger set of synthetic question-answer pairs. By incorporating our synthetic data, we improve the state-of-the-art on the AdversarialQA dataset by 3.7F1 and improve model generalisation on nine of the twelve MRQA datasets. We further conduct a novel human-in-the-loop evaluation to show that our models are considerably more robust to new human-written adversarial examples: crowdworkers can fool our model only 8.8% of the time on average, compared to 17.6% for a model trained without synthetic data.	['Douwe Kiela', 'Pontus Stenetorp', 'Sebastian Riedel', 'Robin Jia', 'Tristan Thrush', 'Max Bartolo']	2021-04-18	null	https://aclanthology.org/2021.emnlp-main.696	https://aclanthology.org/2021.emnlp-main.696.pdf	emnlp-2021-11	['answer-selection']	['natural-language-processing']	[ 3.14502478e-01 4.11212265e-01 5.97748876e-01 -3.24531734e-01 -1.51837695e+00 -1.46709859e+00 7.60885894e-01 1.04952261e-01 -5.27166963e-01 7.43296564e-01 3.46463062e-02 -5.22094429e-01 2.64434278e-01 -9.33555782e-01 -9.84472632e-01 -3.91707290e-03 4.74443406e-01 8.96991253e-01 6.22388899e-01 -8.17413449e-01 -9.34055299e-02 2.22414836e-01 -1.13165748e+00 6.40748918e-01 1.15175593e+00 5.36516786e-01 -4.43779022e-01 1.31206548e+00 -1.50766194e-01 1.29401851e+00 -1.39143014e+00 -1.08082640e+00 4.32268560e-01 -5.37755132e-01 -1.32427132e+00 -3.64842862e-01 1.01583052e+00 -4.70316380e-01 -4.53600794e-01 7.05569386e-01 7.47164965e-01 1.78758666e-01 3.30303520e-01 -1.20580554e+00 -1.14025271e+00 4.72981602e-01 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-2.49182463e-01 2.76915163e-01 3.13304454e-01 1.03822279e+00 4.98430043e-01 6.30682111e-01 4.42940891e-01 -3.37227613e-01 -1.01817775e+00 -1.17337501e+00 4.31704015e-01 9.26704824e-01 5.10860503e-01 8.00711662e-02 -5.46335995e-01 1.46333948e-01]	[11.056707382202148, 7.9833598136901855]
7b20693e-90fa-4bdf-8d14-887fe74f91eb	e2-go-motion-motion-augmented-event-stream	null	null	http://openaccess.thecvf.com//content/CVPR2022/html/Plizzari_E2GOMOTION_Motion_Augmented_Event_Stream_for_Egocentric_Action_Recognition_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Plizzari_E2GOMOTION_Motion_Augmented_Event_Stream_for_Egocentric_Action_Recognition_CVPR_2022_paper.pdf	E2(GO)MOTION: Motion Augmented Event Stream for Egocentric Action Recognition	Event cameras are novel bio-inspired sensors, which asynchronously capture pixel-level intensity changes in the form of "events". Due to their sensing mechanism, event cameras have little to no motion blur, a very high temporal resolution and require significantly less power and memory than traditional frame-based cameras. These characteristics make them a perfect fit to several real-world applications such as egocentric action recognition on wearable devices, where fast camera motion and limited power challenge traditional vision sensors. However, the ever-growing field of event-based vision has, to date, overlooked the potential of event cameras in such applications. In this paper, we show that event data is a very valuable modality for egocentric action recognition. To do so, we introduce N-EPIC-Kitchens, the first event-based camera extension of the large-scale EPIC-Kitchens dataset. In this context, we propose two strategies: (i) directly processing event-camera data with traditional video-processing architectures (E^2(GO)) and (ii) using event-data to distill optical flow information E^2(GO)MO). On our proposed benchmark, we show that event data provides a comparable performance to RGB and optical flow, yet without any additional flow computation at deploy time, and an improved performance of up to 4% with respect to RGB only information. The N-EPIC-Kitchens dataset is available at https://github.com/EgocentricVision/N-EPIC-Kitchens.	['Barbara Caputo', 'Matteo Matteucci', 'Emanuele Gusso', 'Marco Cannici', 'Gabriele Goletto', 'Mirco Planamente', 'Chiara Plizzari']	2022-01-01	null	null	null	cvpr-2022-1	['event-based-vision']	['computer-vision']	[ 2.88149834e-01 -4.83679205e-01 -1.01143055e-01 -3.77993621e-02 -1.90913215e-01 -4.85422373e-01 4.52716202e-01 -1.39111325e-01 -6.65768623e-01 5.61357915e-01 3.63517225e-01 1.06205806e-01 7.42799193e-02 -6.68397307e-01 -5.15174687e-01 -7.20201135e-01 3.47520150e-02 -2.94359654e-01 5.21535158e-01 1.40936732e-01 1.66905597e-01 4.41034704e-01 -1.65992188e+00 3.40800256e-01 3.77529204e-01 1.13239443e+00 1.35416493e-01 1.09717774e+00 2.99856365e-01 1.15381396e+00 -2.51686543e-01 -2.42511868e-01 2.12927476e-01 -6.28420830e-01 -4.79696512e-01 6.49159923e-02 6.02740824e-01 -6.79964006e-01 -9.37089562e-01 8.22560847e-01 5.27958572e-01 3.28820884e-01 3.46978121e-02 -1.35647559e+00 -2.80327439e-01 4.86901738e-02 -5.01608431e-01 7.33129621e-01 7.17285514e-01 7.17785060e-01 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b7bfa53d-d3e8-4a2a-9a86-fc67a9b6b341	multi-scale-cloud-detection-in-remote-sensing	2006.00836	null	https://arxiv.org/abs/2006.00836v1	https://arxiv.org/pdf/2006.00836v1.pdf	Multi-scale Cloud Detection in Remote Sensing Images using a Dual Convolutional Neural Network	Semantic segmentation by convolutional neural networks (CNN) has advanced the state of the art in pixel-level classification of remote sensing images. However, processing large images typically requires analyzing the image in small patches, and hence features that have large spatial extent still cause challenges in tasks such as cloud masking. To support a wider scale of spatial features while simultaneously reducing computational requirements for large satellite images, we propose an architecture of two cascaded CNN model components successively processing undersampled and full resolution images. The first component distinguishes between patches in the inner cloud area from patches at the cloud's boundary region. For the cloud-ambiguous edge patches requiring further segmentation, the framework then delegates computation to a fine-grained model component. We apply the architecture to a cloud detection dataset of complete Sentinel-2 multispectral images, approximately annotated for minimal false negatives in a land use application. On this specific task and data, we achieve a 16\% relative improvement in pixel accuracy over a CNN baseline based on patching.	['Sari Metsämäki', 'Markku Luotamo', 'Arto Klami']	2020-06-01	null	null	null	null	['cloud-detection']	['computer-vision']	[ 8.28356862e-01 1.42344996e-01 1.60840135e-02 -4.81341988e-01 -1.00826252e+00 -7.59844482e-01 1.69272944e-01 2.84456402e-01 -5.76682568e-01 4.11812067e-01 -5.14700770e-01 -6.55865967e-01 3.80063206e-02 -1.22880948e+00 -7.14629948e-01 -7.79645264e-01 -2.38242760e-01 2.60604084e-01 1.73286140e-01 -3.91235501e-02 -1.01809062e-01 8.63630772e-01 -1.77028418e+00 5.03553569e-01 1.01547384e+00 1.27105319e+00 3.74673098e-01 4.83158618e-01 -2.26530135e-01 2.59321511e-01 -2.20518142e-01 2.63459146e-01 8.02456737e-01 -5.97958006e-02 -8.30906332e-01 5.86968660e-01 1.13346219e+00 -3.73385936e-01 2.98301995e-01 1.49866951e+00 1.35886401e-01 -2.08915144e-01 3.13441426e-01 -7.84173548e-01 -5.32584079e-02 2.92333364e-01 -6.58137262e-01 2.46087208e-01 -6.85987294e-01 1.43419102e-01 1.02427125e+00 -8.62608910e-01 3.63599211e-01 8.62391055e-01 1.17319417e+00 4.98447940e-02 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5b686cff-3fbc-4bf7-83aa-bdff2dd499e3	helixfold-single-msa-free-protein-structure	2207.13921	null	https://arxiv.org/abs/2207.13921v3	https://arxiv.org/pdf/2207.13921v3.pdf	HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative	AI-based protein structure prediction pipelines, such as AlphaFold2, have achieved near-experimental accuracy. These advanced pipelines mainly rely on Multiple Sequence Alignments (MSAs) as inputs to learn the co-evolution information from the homologous sequences. Nonetheless, searching MSAs from protein databases is time-consuming, usually taking dozens of minutes. Consequently, we attempt to explore the limits of fast protein structure prediction by using only primary sequences of proteins. HelixFold-Single is proposed to combine a large-scale protein language model with the superior geometric learning capability of AlphaFold2. Our proposed method, HelixFold-Single, first pre-trains a large-scale protein language model (PLM) with thousands of millions of primary sequences utilizing the self-supervised learning paradigm, which will be used as an alternative to MSAs for learning the co-evolution information. Then, by combining the pre-trained PLM and the essential components of AlphaFold2, we obtain an end-to-end differentiable model to predict the 3D coordinates of atoms from only the primary sequence. HelixFold-Single is validated in datasets CASP14 and CAMEO, achieving competitive accuracy with the MSA-based methods on the targets with large homologous families. Furthermore, HelixFold-Single consumes much less time than the mainstream pipelines for protein structure prediction, demonstrating its potential in tasks requiring many predictions. The code of HelixFold-Single is available at https://github.com/PaddlePaddle/PaddleHelix/tree/dev/apps/protein_folding/helixfold-single, and we also provide stable web services on https://paddlehelix.baidu.com/app/drug/protein-single/forecast.	['Le Song', 'Hui Li', 'Hua Wu', 'Xiaonan Zhang', 'Yingfei Xiang', 'Dayong Lin', 'Jingzhou He', 'Lihang Liu', 'Fan Wang', 'Xiaomin Fang']	2022-07-28	null	null	null	null	['protein-language-model']	['medical']	[-3.46166943e-03 -1.35875717e-01 -1.66526049e-01 -3.22472483e-01 -9.25633132e-01 -6.10049605e-01 2.27496792e-02 3.28256935e-01 -2.41988704e-01 1.01772726e+00 -1.95414335e-01 -6.88122094e-01 3.08913022e-01 -5.10090530e-01 -1.11588132e+00 -1.01763964e+00 1.71416439e-02 7.31359541e-01 2.88942426e-01 -2.36431688e-01 2.55283356e-01 4.30743635e-01 -1.15832925e+00 5.43451488e-01 1.24767017e+00 5.61056376e-01 8.52877498e-01 5.04148006e-01 -2.03668952e-01 3.45187902e-01 9.33945328e-02 -2.34019399e-01 1.13476358e-01 -5.44364870e-01 -8.98419917e-01 -4.10930187e-01 3.80335785e-02 5.13430610e-02 7.86855593e-02 8.36748779e-01 5.16038716e-01 -3.79346073e-01 6.48860216e-01 -7.72593856e-01 -4.72506940e-01 -5.14353514e-02 -5.62579513e-01 -1.91173963e-02 4.32413250e-01 7.81183362e-01 1.06344342e+00 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f72a8cb0-6952-4c9c-90ca-e1778b869712	a-hierarchical-encoding-decoding-scheme-for	2305.08503	null	https://arxiv.org/abs/2305.08503v3	https://arxiv.org/pdf/2305.08503v3.pdf	A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document Summarization	Pre-trained language models (PLMs) have accomplished impressive achievements in abstractive single-document summarization (SDS). However, such benefits may not be readily extended to muti-document summarization (MDS), where the interactions among documents are more complex. Previous works either design new architectures or new pre-training objectives for MDS, or apply PLMs to MDS without considering the complex document interactions. While the former does not make full use of previous pre-training efforts and may not generalize well across multiple domains, the latter cannot fully attend to the intricate relationships unique to MDS tasks. In this paper, we enforce hierarchy on both the encoder and decoder and seek to make better use of a PLM to facilitate multi-document interactions for the MDS task. We test our design on 10 MDS datasets across a wide range of domains. Extensive experiments show that our proposed method can achieve consistent improvements on all these datasets, outperforming the previous best models, and even achieving better or competitive results as compared to some models with additional MDS pre-training or larger model parameters.	['Xuan-Phi Nguyen', 'Lidong Bing', 'Yang You', 'Liying Cheng', 'Chenhui Shen']	2023-05-15	null	null	null	null	['multi-document-summarization', 'document-summarization']	['natural-language-processing', 'natural-language-processing']	[ 3.61802310e-01 2.19875991e-01 -3.01272690e-01 -3.11827779e-01 -1.03236639e+00 -4.64885384e-01 9.77037549e-01 2.14389682e-01 -3.57609451e-01 8.30886066e-01 9.38318133e-01 -2.96669602e-01 -3.87145132e-02 -3.29685301e-01 -5.37874758e-01 -3.31070542e-01 -1.21739149e-01 7.43941784e-01 2.99671501e-01 -5.06458163e-01 4.99682754e-01 1.83467746e-01 -1.09217393e+00 5.43023646e-01 1.22568238e+00 2.85649389e-01 4.07192588e-01 8.09811652e-01 -3.68765622e-01 7.58719146e-01 -1.02791989e+00 -4.10160333e-01 -1.58835992e-01 -5.97280502e-01 -9.98133659e-01 1.52843803e-01 4.80007738e-01 -6.24234676e-01 -4.66571242e-01 7.44195759e-01 8.91920507e-01 1.42404279e-02 8.47349823e-01 -8.19234192e-01 -9.81289089e-01 1.03369796e+00 -6.80270493e-01 3.89846951e-01 5.00327170e-01 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ff047590-9e9f-418c-9e90-7cbbd838f3bd	twt-table-with-written-text-for-controlled	null	null	https://aclanthology.org/2021.findings-emnlp.107	https://aclanthology.org/2021.findings-emnlp.107.pdf	TWT: Table with Written Text for Controlled Data-to-Text Generation	Large pre-trained neural models have recently shown remarkable progress in text generation. In this paper, we propose to generate text conditioned on the structured data (table) and a prefix (the written text) by leveraging the pre-trained models. We present a new data-to-text dataset, Table with Written Text (TWT), by repurposing two existing datasets: ToTTo and TabFact. TWT contains both factual and logical statements that are faithful to the structured data, aiming to serve as a useful benchmark for controlled text generation. Compared with existing data-to-text task settings, TWT is more intuitive, the prefix (usually provided by the user) controls the topic of the generated text. Existing methods usually output hallucinated text that is not faithful on TWT. Therefore, we design a novel approach with table-aware attention visibility and copy mechanism over the table. Experimental results show that our approach outperforms state-of-the-art methods under both automatic and human evaluation metrics.	['Zhoujun Li', 'Jian-Guang Lou', 'Lei Fang', 'Tongliang Li']	null	null	null	null	findings-emnlp-2021-11	['data-to-text-generation']	['natural-language-processing']	[ 4.61365998e-01 7.05001950e-01 -2.79555600e-02 -2.86080062e-01 -1.00963080e+00 -3.76701862e-01 1.16861534e+00 2.71505147e-01 -2.82841548e-02 1.14943016e+00 8.27308059e-01 -2.53154814e-01 3.96817207e-01 -9.45534766e-01 -8.78510892e-01 -3.04041505e-01 4.79680002e-01 8.91660631e-01 -1.06022924e-01 -2.96034932e-01 3.96374345e-01 -2.76765227e-01 -1.49155343e+00 8.82890940e-01 1.21702969e+00 9.32069480e-01 3.49356055e-01 2.77101159e-01 -4.86702323e-01 1.10255790e+00 -1.00472212e+00 -6.10685468e-01 8.07507336e-02 -8.91519248e-01 -8.04636538e-01 1.47030354e-01 4.25459445e-01 -3.26561809e-01 -1.33888260e-01 7.55328119e-01 5.61188161e-01 1.73891947e-01 7.56559253e-01 -1.23143244e+00 -1.19391274e+00 1.46394205e+00 -1.65734395e-01 -3.75751555e-01 5.88213265e-01 2.43316650e-01 1.20219052e+00 -1.13169682e+00 9.71678615e-01 1.39090109e+00 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106ed455-644a-40b4-948b-037935e7d763	ct-multi-task-learning-with-a-large-image	2304.02649	null	https://arxiv.org/abs/2304.02649v1	https://arxiv.org/pdf/2304.02649v1.pdf	CT Multi-Task Learning with a Large Image-Text (LIT) Model	Large language models (LLM) not only empower multiple language tasks but also serve as a general interface across different spaces. Up to now, it has not been demonstrated yet how to effectively translate the successes of LLMs in the computer vision field to the medical imaging field which involves high-dimensional and multi-modal medical images. In this paper, we report a feasibility study of building a multi-task CT large image-text (LIT) model for lung cancer diagnosis by combining an LLM and a large image model (LIM). Specifically, the LLM and LIM are used as encoders to perceive multi-modal information under task-specific text prompts, which synergizes multi-source information and task-specific and patient-specific priors for optimized diagnostic performance. The key components of our LIT model and associated techniques are evaluated with an emphasis on 3D lung CT analysis. Our initial results show that the LIT model performs multiple medical tasks well, including lung segmentation, lung nodule detection, and lung cancer classification. Active efforts are in progress to develop large image-language models for superior medical imaging in diverse applications and optimal patient outcomes.	['Ge Wang', 'Chuang Niu']	2023-04-03	null	null	null	null	['lung-cancer-diagnosis', 'lung-nodule-detection']	['medical', 'medical']	[ 4.65632439e-01 3.13324958e-01 -3.97934675e-01 -3.27525496e-01 -1.61116850e+00 -2.50516027e-01 4.42769676e-01 2.07338959e-01 -4.57779855e-01 1.05771750e-01 5.63186288e-01 -5.86767554e-01 5.99135347e-02 -3.50557268e-01 -3.93852174e-01 -5.95273018e-01 1.28897056e-01 9.65091646e-01 3.13806415e-01 4.61589873e-01 -2.87165821e-01 3.84546757e-01 -7.54259348e-01 8.84175479e-01 5.60046375e-01 5.11859655e-01 9.53985512e-01 1.22160935e+00 -1.38547167e-01 1.25164926e+00 -2.54422754e-01 -1.12661406e-01 -1.02966510e-01 -3.59311193e-01 -1.05630982e+00 4.68839079e-01 5.07371902e-01 -4.15741324e-01 -1.88096285e-01 7.20047951e-01 8.46813440e-01 -3.82815033e-01 7.18730092e-01 -6.89731538e-01 -3.89273137e-01 3.35269690e-01 -5.02571166e-01 5.03602982e-01 4.91851233e-02 3.24937582e-01 6.98438585e-01 -6.32072687e-01 4.87295330e-01 1.26055062e+00 7.33427584e-01 5.77899575e-01 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-2.30128407e-01 3.10610831e-01 4.47553873e-01 -5.49529307e-03 1.63715273e-01 2.39790648e-01]	[15.00354290008545, -1.768291711807251]
5bdbc9aa-7b4d-4f2a-b87f-e0539b3a6957	on-the-calibration-and-uncertainty-of-neural-1	null	null	https://aclanthology.org/2021.eacl-main.12	https://aclanthology.org/2021.eacl-main.12.pdf	On the Calibration and Uncertainty of Neural Learning to Rank Models for Conversational Search	According to the Probability Ranking Principle (PRP), ranking documents in decreasing order of their probability of relevance leads to an optimal document ranking for ad-hoc retrieval. The PRP holds when two conditions are met: [C1] the models are well calibrated, and, [C2] the probabilities of relevance are reported with certainty. We know however that deep neural networks (DNNs) are often not well calibrated and have several sources of uncertainty, and thus [C1] and [C2] might not be satisfied by neural rankers. Given the success of neural Learning to Rank (LTR) approaches{---}and here, especially BERT-based approaches{---}we first analyze under which circumstances deterministic neural rankers are calibrated for conversational search problems. Then, motivated by our findings we use two techniques to model the uncertainty of neural rankers leading to the proposed stochastic rankers, which output a predictive distribution of relevance as opposed to point estimates. Our experimental results on the ad-hoc retrieval task of conversation response ranking reveal that (i) BERT-based rankers are not robustly calibrated and that stochastic BERT-based rankers yield better calibration; and (ii) uncertainty estimation is beneficial for both risk-aware neural ranking, i.e. taking into account the uncertainty when ranking documents, and for predicting unanswerable conversational contexts.	['Claudia Hauff', 'Gustavo Penha']	2021-04-01	null	null	null	eacl-2021-2	['conversational-search']	['natural-language-processing']	[ 2.42129311e-01 3.47961575e-01 -1.96500823e-01 -6.19754255e-01 -1.27154601e+00 -6.49173677e-01 6.98697209e-01 1.26837060e-01 -3.31734657e-01 9.34717774e-01 4.69969839e-01 -2.97251761e-01 -9.21884596e-01 -6.76879823e-01 -8.10531855e-01 -4.48226362e-01 -2.97238708e-01 1.10570955e+00 1.04922362e-01 -2.89556593e-01 5.15205204e-01 2.42844120e-01 -1.68881738e+00 4.81070638e-01 9.50199008e-01 1.43681991e+00 5.00759222e-02 7.70253778e-01 -9.55824107e-02 8.63210261e-01 -7.90946305e-01 -7.15842605e-01 1.28984332e-01 -1.02266915e-01 -1.09688509e+00 -8.49447012e-01 2.80255198e-01 -7.83248842e-01 -4.17987853e-01 1.04902065e+00 4.31750298e-01 4.06003892e-01 1.06074202e+00 -1.13916576e+00 -7.13761091e-01 1.27953267e+00 -4.87753376e-03 1.31273687e-01 3.36676389e-01 -3.32252055e-01 1.55762088e+00 -4.58814502e-01 3.19379866e-01 1.47364700e+00 4.14119005e-01 5.60668290e-01 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594f0984-b37c-4951-86c8-978c75799152	urbanir-large-scale-urban-scene-inverse	2306.09349	null	https://arxiv.org/abs/2306.09349v2	https://arxiv.org/pdf/2306.09349v2.pdf	UrbanIR: Large-Scale Urban Scene Inverse Rendering from a Single Video	We show how to build a model that allows realistic, free-viewpoint renderings of a scene under novel lighting conditions from video. Our method -- UrbanIR: Urban Scene Inverse Rendering -- computes an inverse graphics representation from the video. UrbanIR jointly infers shape, albedo, visibility, and sun and sky illumination from a single video of unbounded outdoor scenes with unknown lighting. UrbanIR uses videos from cameras mounted on cars (in contrast to many views of the same points in typical NeRF-style estimation). As a result, standard methods produce poor geometry estimates (for example, roofs), and there are numerous ''floaters''. Errors in inverse graphics inference can result in strong rendering artifacts. UrbanIR uses novel losses to control these and other sources of error. UrbanIR uses a novel loss to make very good estimates of shadow volumes in the original scene. The resulting representations facilitate controllable editing, delivering photorealistic free-viewpoint renderings of relit scenes and inserted objects. Qualitative evaluation demonstrates strong improvements over the state-of-the-art.	['Shenlong Wang', 'Anand Bhattad', 'Jia-Bin Huang', 'David Forsyth', 'Yi-Ting Chen', 'Bohan Liu', 'Zhi-Hao Lin']	2023-06-15	null	null	null	null	['inverse-rendering']	['computer-vision']	[ 4.37105477e-01 2.13610716e-02 8.08399975e-01 -4.89415199e-01 -5.79723239e-01 -8.21472466e-01 6.31408572e-01 -6.21284544e-01 8.40547606e-02 5.11519790e-01 1.67223915e-01 -1.86112985e-01 5.04607022e-01 -7.91341424e-01 -1.06404305e+00 -4.53009039e-01 8.83581340e-02 4.28326279e-01 6.22650683e-01 -3.97489876e-01 3.04491818e-01 6.38763487e-01 -1.91376245e+00 5.41033447e-01 4.42319930e-01 5.60850441e-01 3.66640508e-01 1.37055135e+00 -2.05674395e-02 1.02215183e+00 -5.81514239e-01 -2.48799652e-01 4.61686134e-01 -2.48559102e-01 -1.38286620e-01 4.80681479e-01 1.00604987e+00 -9.92362499e-01 -3.62788320e-01 6.56715274e-01 1.53050065e-01 1.85523733e-01 3.53868783e-01 -1.20509446e+00 -5.77372074e-01 -3.02246988e-01 -6.58079207e-01 -1.32114679e-01 1.04842675e+00 2.01364473e-01 6.47644341e-01 -1.10311615e+00 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b23200e4-e0f8-45f9-a0a3-fc8f74946715	a-modular-multimodal-architecture-for-gaze	null	null	https://openaccess.thecvf.com/content/CVPR2022W/GAZE/html/Gupta_A_Modular_Multimodal_Architecture_for_Gaze_Target_Prediction_Application_to_CVPRW_2022_paper.html	https://openaccess.thecvf.com/content/CVPR2022W/GAZE/papers/Gupta_A_Modular_Multimodal_Architecture_for_Gaze_Target_Prediction_Application_to_CVPRW_2022_paper.pdf	A Modular Multimodal Architecture for Gaze Target Prediction: Application to Privacy-Sensitive Settings	Predicting where a person is looking is a complex task, requiring to understand not only the person’s gaze and scene content, but also the 3D scene structure and the person’s situation (are they manipulating? interacting or observing others? attentive?) to detect obstructions in the line of sight or apply attention priors that humans typically have when observing others. In this paper, we hypothesize that identifying and leveraging such priors can be better achieved through the exploitation of explicitly derived multimodal cues such as depth and pose. We thus propose a modular multimodal architecture allowing to combine these cues using an attention mechanism. The architecture can naturally be exploited in privacy-sensitive situations such as surveillance and health, where personally identifiable information cannot be released. We perform extensive experiments on the GazeFollow and VideoAttentionTarget public datasets, obtaining state-of-the-art performance and demonstrating very competitive results in the privacy setting case.	['Jean-Marc Odobez', 'Samy Tafasca', 'Anshul Gupta']	2022-06-20	null	null	null	ieee-cvf-conference-on-computer-vision-and-8	['gaze-target-estimation']	['computer-vision']	[ 4.84986424e-01 2.66011536e-01 1.15797535e-01 -8.13192844e-01 -4.05298591e-01 -8.02294672e-01 6.31669283e-01 3.00223589e-01 -7.31319129e-01 4.47074652e-01 4.79056478e-01 -3.72320674e-02 -1.31661698e-01 -2.02071220e-01 -6.39224768e-01 -5.25895059e-01 -1.47339955e-01 2.03373313e-01 2.40532402e-02 -9.17941630e-02 2.42655367e-01 4.30273265e-01 -1.84917521e+00 2.12304085e-01 3.64729106e-01 1.00976121e+00 -1.49940744e-01 1.02108836e+00 4.60774064e-01 7.74544299e-01 -3.74234796e-01 -4.03321564e-01 1.92352399e-01 -1.73895001e-01 -7.47865558e-01 4.65787023e-01 9.74242270e-01 -5.95836997e-01 -6.00536279e-02 8.71285617e-01 3.19953620e-01 2.52276480e-01 4.08715189e-01 -1.12191391e+00 -1.61277220e-01 -1.91376492e-01 -3.74914885e-01 3.38185519e-01 1.24824965e+00 4.54065800e-01 8.12827289e-01 -2.21405774e-01 5.12551427e-01 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aabe377b-1bf3-49cf-b0f0-2a222007ccad	judging-llm-as-a-judge-with-mt-bench-and	2306.05685	null	https://arxiv.org/abs/2306.05685v1	https://arxiv.org/pdf/2306.05685v1.pdf	Judging LLM-as-a-judge with MT-Bench and Chatbot Arena	Evaluating large language model (LLM) based chat assistants is challenging due to their broad capabilities and the inadequacy of existing benchmarks in measuring human preferences. To address this, we explore using strong LLMs as judges to evaluate these models on more open-ended questions. We examine the usage and limitations of LLM-as-a-judge, such as position and verbosity biases and limited reasoning ability, and propose solutions to migrate some of them. We then verify the agreement between LLM judges and human preferences by introducing two benchmarks: MT-bench, a multi-turn question set; and Chatbot Arena, a crowdsourced battle platform. Our results reveal that strong LLM judges like GPT-4 can match both controlled and crowdsourced human preferences well, achieving over 80\% agreement, the same level of agreement between humans. Hence, LLM-as-a-judge is a scalable and explainable way to approximate human preferences, which are otherwise very expensive to obtain. Additionally, we show our benchmark and traditional benchmarks complement each other by evaluating several variants of LLaMA/Vicuna. We will publicly release 80 MT-bench questions, 3K expert votes, and 30K conversations with human preferences from Chatbot Arena.	['Ion Stoica', 'Joseph E. Gonzalez', 'Hao Zhang', 'Eric. P Xing', 'Dacheng Li', 'Zhuohan Li', 'Zi Lin', 'Yonghao Zhuang', 'Zhanghao Wu', 'Siyuan Zhuang', 'Ying Sheng', 'Wei-Lin Chiang', 'Lianmin Zheng']	2023-06-09	null	null	null	null	['chatbot', 'chatbot']	['methodology', 'natural-language-processing']	[-5.35821557e-01 4.16251153e-01 -2.04326719e-01 -8.72847795e-01 -1.29081416e+00 -8.36317778e-01 5.99654675e-01 -7.82661587e-02 -7.29634702e-01 1.04182076e+00 7.67883003e-01 -3.74041378e-01 -8.84978101e-02 -2.71998316e-01 -5.72947934e-02 -8.38178694e-02 4.19684589e-01 1.18133712e+00 1.74316198e-01 -6.48327768e-01 3.31914514e-01 -1.74519405e-01 -8.43183458e-01 8.71392965e-01 9.59695458e-01 6.32717431e-01 -1.47830874e-01 9.90921974e-01 -5.08969016e-02 1.62155628e+00 -8.93732190e-01 -1.14998925e+00 2.17860207e-01 -1.24765217e-01 -1.66118896e+00 -4.88201171e-01 5.03200531e-01 -5.48820317e-01 -8.60071629e-02 4.76036698e-01 9.29288685e-01 2.29440004e-01 6.02718949e-01 -1.47729397e+00 -6.97913110e-01 1.13342714e+00 -1.46949086e-02 -1.00231774e-01 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a2202b6c-3198-4aea-a926-f120ed8d73a4	covering-uncommon-ground-gap-focused-question	2307.03319	null	https://arxiv.org/abs/2307.03319v1	https://arxiv.org/pdf/2307.03319v1.pdf	Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment	Human communication often involves information gaps between the interlocutors. For example, in an educational dialogue, a student often provides an answer that is incomplete, and there is a gap between this answer and the perfect one expected by the teacher. Successful dialogue then hinges on the teacher asking about this gap in an effective manner, thus creating a rich and interactive educational experience. We focus on the problem of generating such gap-focused questions (GFQs) automatically. We define the task, highlight key desired aspects of a good GFQ, and propose a model that satisfies these. Finally, we provide an evaluation by human annotators of our generated questions compared against human generated ones, demonstrating competitive performance.	['Amir Globerson', 'Reut Tsarfaty', 'Gal Elidan', 'Lidan Hackmon', 'Roee Engelberg', 'Alexandre Djerbetian', 'Roni Rabin']	2023-07-06	null	null	null	null	['question-generation']	['natural-language-processing']	[ 1.55307734e-02 8.45293581e-01 2.99339861e-01 -4.06960338e-01 -1.18152225e+00 -1.00144517e+00 4.29326415e-01 7.03849792e-01 -1.08727269e-01 1.07812846e+00 5.01220405e-01 -4.56441551e-01 -1.07436672e-01 -7.20273614e-01 -5.17713606e-01 -9.87844989e-02 3.90845925e-01 8.40398490e-01 3.49282742e-01 -6.69777095e-01 2.52467901e-01 -1.79063007e-01 -1.38505578e+00 3.87003154e-01 1.40147269e+00 5.73317945e-01 3.41479629e-01 8.55845690e-01 -6.13798201e-01 1.20715070e+00 -1.21654582e+00 -9.17473793e-01 -2.80086398e-01 -1.06168211e+00 -1.64673853e+00 1.03543639e-01 5.25630534e-01 -2.90295839e-01 8.71504396e-02 1.10988045e+00 2.36906901e-01 3.17870378e-01 4.77262974e-01 -1.24747264e+00 -4.97115284e-01 1.16968513e+00 2.21610889e-01 -2.02575028e-01 1.33567309e+00 -8.86078402e-02 1.10823584e+00 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b9b39425-06a3-45f2-bc59-b13e1a53212e	bidirectional-attention-as-a-mixture-of	2307.04057	null	https://arxiv.org/abs/2307.04057v1	https://arxiv.org/pdf/2307.04057v1.pdf	Bidirectional Attention as a Mixture of Continuous Word Experts	Bidirectional attention $\unicode{x2013}$ composed of self-attention with positional encodings and the masked language model (MLM) objective $\unicode{x2013}$ has emerged as a key component of modern large language models (LLMs). Despite its empirical success, few studies have examined its statistical underpinnings: What statistical model is bidirectional attention implicitly fitting? What sets it apart from its non-attention predecessors? We explore these questions in this paper. The key observation is that fitting a single-layer single-head bidirectional attention, upon reparameterization, is equivalent to fitting a continuous bag of words (CBOW) model with mixture-of-experts (MoE) weights. Further, bidirectional attention with multiple heads and multiple layers is equivalent to stacked MoEs and a mixture of MoEs, respectively. This statistical viewpoint reveals the distinct use of MoE in bidirectional attention, which aligns with its practical effectiveness in handling heterogeneous data. It also suggests an immediate extension to categorical tabular data, if we view each word location in a sentence as a tabular feature. Across empirical studies, we find that this extension outperforms existing tabular extensions of transformers in out-of-distribution (OOD) generalization. Finally, this statistical perspective of bidirectional attention enables us to theoretically characterize when linear word analogies are present in its word embeddings. These analyses show that bidirectional attention can require much stronger assumptions to exhibit linear word analogies than its non-attention predecessors.	['Yixin Wang', 'Kevin Christian Wibisono']	2023-07-08	null	null	null	null	['word-embeddings', 'language-modelling']	['methodology', 'natural-language-processing']	[ 3.63313481e-02 -3.06792073e-02 -4.03773695e-01 -3.06656837e-01 -8.39908659e-01 -6.11942530e-01 7.26918936e-01 3.35638136e-01 -5.40678859e-01 5.05482614e-01 6.37014031e-01 -8.11246514e-01 -3.90032113e-01 -6.48760259e-01 -9.30451453e-01 -6.73371792e-01 -1.02005051e-02 4.18028802e-01 -2.00599760e-01 -3.82519603e-01 2.92309523e-01 1.67229041e-01 -1.40061247e+00 1.35348305e-01 7.32706428e-01 8.70396614e-01 4.13908392e-01 5.44920564e-01 -4.25251693e-01 5.27924657e-01 -5.33060431e-01 -7.03962088e-01 1.45935610e-01 -6.44087186e-03 -7.78104782e-01 -1.55778423e-01 7.48748064e-01 -1.01516359e-01 -2.62715757e-01 9.39301312e-01 3.84124964e-01 2.18307123e-01 8.93193841e-01 -1.08113885e+00 -1.69509685e+00 8.67194772e-01 -6.76079035e-01 5.27038157e-01 1.45570606e-01 9.91282463e-02 1.80455959e+00 -1.13728666e+00 2.01533780e-01 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-1.20278156e+00 -6.32606566e-01 4.92744654e-01 5.98814845e-01 -7.96034992e-01 -6.26682520e-01 -3.88611495e-01]	[10.645732879638672, 8.843655586242676]
c2971fba-52d5-4811-b066-a02b0598221d	automatic-tracking-of-the-muscle-tendon	2005.02071	null	https://arxiv.org/abs/2005.02071v1	https://arxiv.org/pdf/2005.02071v1.pdf	Automatic Tracking of the Muscle Tendon Junction in Healthy and Impaired Subjects using Deep Learning	Recording muscle tendon junction displacements during movement, allows separate investigation of the muscle and tendon behaviour, respectively. In order to provide a fully-automatic tracking method, we employ a novel deep learning approach to detect the position of the muscle tendon junction in ultrasound images. We utilize the attention mechanism to enable the network to focus on relevant regions and to obtain a better interpretation of the results. Our data set consists of a large cohort of 79 healthy subjects and 28 subjects with movement limitations performing passive full range of motion and maximum contraction movements. Our trained network shows robust detection of the muscle tendon junction on a diverse data set of varying quality with a mean absolute error of 2.55$\pm$1 mm. We show that our approach can be applied for various subjects and can be operated in real-time. The complete software package is available for open-source use via: https://github.com/luuleitner/deepMTJ	['Christian Baumgartner', 'Jörg Schröttner', 'Andreas Konrad', 'Robert Jarolim', 'Markus Tilp', 'Christoph Leitner', 'Annika Kruse']	2020-05-05	null	null	null	null	['muscle-tendon-junction-identification']	['medical']	[ 5.56510575e-02 1.98437080e-01 -1.51046231e-01 1.00553177e-01 -1.12520087e+00 -5.07430613e-01 -5.61543815e-02 -3.72161150e-01 -4.79243666e-01 3.85742545e-01 2.95289326e-02 3.53700779e-02 2.03449819e-02 -1.49691328e-01 -6.52434170e-01 -8.11806381e-01 -4.81163979e-01 1.77141592e-01 4.69584793e-01 1.33906439e-01 9.58958343e-02 3.76574069e-01 -8.43230426e-01 7.05132931e-02 3.22061032e-01 6.70103371e-01 5.32743514e-01 7.28295982e-01 8.09451818e-01 1.98587835e-01 -5.13256073e-01 8.77719000e-02 4.05436128e-01 -3.41208845e-01 -6.88338578e-01 1.69882029e-02 5.74454486e-01 -5.00696361e-01 -4.38680172e-01 8.84813011e-01 1.17952001e+00 1.08841576e-01 3.15033287e-01 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9566d001-09ec-440f-93f2-0c9d426da914	iterative-self-learning-for-enhanced-back	2011.07403	null	https://arxiv.org/abs/2011.07403v3	https://arxiv.org/pdf/2011.07403v3.pdf	A Hybrid Approach for Improved Low Resource Neural Machine Translation using Monolingual Data	Many language pairs are low resource, meaning the amount and/or quality of available parallel data is not sufficient to train a neural machine translation (NMT) model which can reach an acceptable standard of accuracy. Many works have explored using the readily available monolingual data in either or both of the languages to improve the standard of translation models in low, and even high, resource languages. One of the most successful of such works is the back-translation that utilizes the translations of the target language monolingual data to increase the amount of the training data. The quality of the backward model which is trained on the available parallel data has been shown to determine the performance of the back-translation approach. Despite this, only the forward model is improved on the monolingual target data in standard back-translation. A previous study proposed an iterative back-translation approach for improving both models over several iterations. But unlike in the traditional back-translation, it relied on both the target and source monolingual data. This work, therefore, proposes a novel approach that enables both the backward and forward models to benefit from the monolingual target data through a hybrid of self-learning and back-translation respectively. Experimental results have shown the superiority of the proposed approach over the traditional back-translation method on English-German low resource neural machine translation. We also proposed an iterative self-learning approach that outperforms the iterative back-translation while also relying only on the monolingual target data and require the training of less models.	['Ismaila Idris Sinan', 'Habeebah Adamu Kakudi', 'Abubakar Isa', 'Bashir Shehu Galadanci', 'Idris Abdulmumin']	2020-11-14	null	null	null	null	['low-resource-neural-machine-translation']	['natural-language-processing']	[ 5.44182621e-02 -2.28170097e-01 -3.85269374e-01 -3.23094308e-01 -9.32894826e-01 -6.32206857e-01 9.82982039e-01 -2.18508899e-01 -7.48464167e-01 1.24174345e+00 2.91568059e-02 -7.61600375e-01 4.32031453e-01 -6.10457122e-01 -9.39011872e-01 -3.61082882e-01 5.24709523e-01 8.58617902e-01 -1.17388126e-02 -7.55591810e-01 1.17889687e-01 1.38836220e-01 -1.09052277e+00 4.36276883e-01 1.19128358e+00 3.11986625e-01 7.33472347e-01 3.47478539e-01 -4.83501256e-01 2.15508536e-01 -2.83739984e-01 -4.37921405e-01 6.30978465e-01 -9.12964463e-01 -7.64966428e-01 -3.78134817e-01 1.88408762e-01 -1.38416052e-01 2.19188794e-01 9.59802926e-01 6.99818730e-01 -1.92642987e-01 2.95088321e-01 -7.87032545e-01 -8.26373696e-01 8.91995192e-01 -4.70226020e-01 3.24965835e-01 3.61407429e-01 -8.55422318e-02 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d5a8015e-2d7d-4486-bf43-fcb6832b5b7d	tripinet-tripartite-progressive-integration	2212.12841	null	https://arxiv.org/abs/2212.12841v1	https://arxiv.org/pdf/2212.12841v1.pdf	TriPINet: Tripartite Progressive Integration Network for Image Manipulation Localization	Image manipulation localization aims at distinguishing forged regions from the whole test image. Although many outstanding prior arts have been proposed for this task, there are still two issues that need to be further studied: 1) how to fuse diverse types of features with forgery clues; 2) how to progressively integrate multistage features for better localization performance. In this paper, we propose a tripartite progressive integration network (TriPINet) for end-to-end image manipulation localization. First, we extract both visual perception information, e.g., RGB input images, and visual imperceptible features, e.g., frequency and noise traces for forensic feature learning. Second, we develop a guided cross-modality dual-attention (gCMDA) module to fuse different types of forged clues. Third, we design a set of progressive integration squeeze-and-excitation (PI-SE) modules to improve localization performance by appropriately incorporating multiscale features in the decoder. Extensive experiments are conducted to compare our method with state-of-the-art image forensics approaches. The proposed TriPINet obtains competitive results on several benchmark datasets.	['Xiao Jin', 'Jing Xu', 'Wei-Yun Liang']	2022-12-25	null	null	null	null	['image-manipulation', 'image-forensics']	['computer-vision', 'computer-vision']	[ 3.24979544e-01 -6.92410886e-01 1.54324383e-01 -1.25340372e-01 -1.16961157e+00 -4.42110151e-01 3.38505208e-01 -1.12822361e-01 -2.83391833e-01 2.39660695e-01 1.41974032e-01 -1.17248751e-01 1.50739960e-02 -3.65482658e-01 -7.69678593e-01 -7.63558745e-01 2.25969642e-01 -5.39807498e-01 3.56858850e-01 -1.37363255e-01 8.07357848e-01 4.11476165e-01 -1.19080722e+00 5.26116133e-01 8.38505685e-01 1.12839234e+00 3.32201838e-01 4.90025580e-01 1.73422307e-01 1.02149272e+00 -6.44498289e-01 -5.30783653e-01 2.00826883e-01 -5.33260405e-01 -4.51359093e-01 2.13480696e-01 2.58011311e-01 -6.68150902e-01 -5.18192708e-01 1.36033678e+00 7.47072399e-01 2.11404815e-01 5.04610062e-01 -1.16670156e+00 -1.20506680e+00 3.29988927e-01 -1.14057934e+00 8.28853786e-01 3.14769804e-01 7.23708928e-01 5.61042547e-01 -1.15670753e+00 4.04829085e-01 1.35324919e+00 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bb4e9b23-8446-41f5-8f18-50edef151c42	learning-event-representations-in-image	1910.03483	null	https://arxiv.org/abs/1910.03483v3	https://arxiv.org/pdf/1910.03483v3.pdf	Learning event representations for temporal segmentation of image sequences by dynamic graph embedding	Recently, self-supervised learning has proved to be effective to learn representations of events suitable for temporal segmentation in image sequences, where events are understood as sets of temporally adjacent images that are semantically perceived as a whole. However, although this approach does not require expensive manual annotations, it is data hungry and suffers from domain adaptation problems. As an alternative, in this work, we propose a novel approach for learning event representations named Dynamic Graph Embedding (DGE). The assumption underlying our model is that a sequence of images can be represented by a graph that encodes both semantic and temporal similarity. The key novelty of DGE is to learn jointly the graph and its graph embedding. At its core, DGE works by iterating over two steps: 1) updating the graph representing the semantic and temporal similarity of the data based on the current data representation, and 2) updating the data representation to take into account the current data graph structure. The main advantage of DGE over state-of-the-art self-supervised approaches is that it does not require any training set, but instead learns iteratively from the data itself a low-dimensional embedding that reflects their temporal and semantic similarity. Experimental results on two benchmark datasets of real image sequences captured at regular time intervals demonstrate that the proposed DGE leads to event representations effective for temporal segmentation. In particular, it achieves robust temporal segmentation on the EDUBSeg and EDUBSeg-Desc benchmark datasets, outperforming the state of the art. Additional experiments on two Human Motion Segmentation benchmark datasets demonstrate the generalization capabilities of the proposed DGE.	['Herwig Wendt', 'Mariella Dimiccoli']	2019-10-08	null	null	null	null	['motion-segmentation', 'dynamic-graph-embedding']	['computer-vision', 'graphs']	[ 2.84899443e-01 2.82202996e-02 -2.71951109e-01 -3.45924824e-01 -2.35217616e-01 -4.30612504e-01 7.09584236e-01 5.56173384e-01 -4.74637181e-01 2.88193971e-01 7.17089102e-02 7.47787356e-02 -1.95903927e-01 -8.43373001e-01 -6.38099194e-01 -8.50015283e-01 -2.60964602e-01 2.57720709e-01 6.62085176e-01 -7.81119149e-03 -2.07351409e-02 3.27097923e-01 -1.61573863e+00 -1.89606156e-02 7.57530272e-01 1.05090928e+00 2.59417146e-01 3.70572835e-01 -2.09392503e-01 7.31455922e-01 -3.25525224e-01 -1.13406315e-01 -1.60702430e-02 -6.15859807e-01 -8.94911170e-01 5.65093875e-01 2.38911614e-01 1.10291325e-01 -5.38689137e-01 8.81669641e-01 2.27442399e-01 5.74612737e-01 5.20223320e-01 -1.47684658e+00 -5.32993138e-01 3.33234131e-01 -3.82535428e-01 3.90352815e-01 3.63614768e-01 1.95584428e-02 1.02153814e+00 -5.60480893e-01 9.17242229e-01 1.08619905e+00 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-1.42757386e-01 -3.88333946e-01 -5.36612511e-01 1.01848602e+00 2.62905270e-01 -9.01049256e-01 -3.25056285e-01 5.10815501e-01]	[8.519264221191406, 0.627875566482544]
24245bc8-4156-41b7-9b28-47491c3b506f	deep-graph-similarity-learning-a-survey	1912.11615	null	https://arxiv.org/abs/1912.11615v2	https://arxiv.org/pdf/1912.11615v2.pdf	Deep Graph Similarity Learning: A Survey	In many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, such as classification, clustering, and similarity search. Recently, there has been an increasing interest in deep graph similarity learning, where the key idea is to learn a deep learning model that maps input graphs to a target space such that the distance in the target space approximates the structural distance in the input space. Here, we provide a comprehensive review of the existing literature of deep graph similarity learning. We propose a systematic taxonomy for the methods and applications. Finally, we discuss the challenges and future directions for this problem.	['Theodore L. Willke', 'Philip S. Yu', 'Guixiang Ma', 'Nesreen K. Ahmed']	2019-12-25	null	null	null	null	['graph-similarity']	['graphs']	[-7.08919019e-02 1.35176592e-02 -1.29273087e-01 -5.69312811e-01 -2.90954471e-01 -3.87536287e-01 4.55185443e-01 6.89570487e-01 -1.23864651e-01 1.13634855e-01 1.92932189e-01 -1.59077317e-01 -5.00558853e-01 -1.11662459e+00 -3.59080911e-01 -5.26478112e-01 -1.49862558e-01 4.11792994e-01 8.36986527e-02 -7.32818171e-02 3.10519695e-01 4.35635686e-01 -1.16970932e+00 -8.14558342e-02 8.74278963e-01 1.02722168e+00 1.94223881e-01 3.64881009e-01 -2.93997377e-01 6.08359098e-01 -5.72011530e-01 -6.01903260e-01 1.91720322e-01 -6.10760987e-01 -1.09248114e+00 -5.80629185e-02 6.06739104e-01 1.63587496e-01 -1.09610081e+00 1.47462773e+00 4.47694540e-01 5.43927491e-01 7.09786952e-01 -1.57066643e+00 -1.11342943e+00 3.95191073e-01 -4.08053488e-01 3.56384337e-01 1.97878957e-01 -1.71748593e-01 1.24602902e+00 -3.29687327e-01 3.68743002e-01 1.20429289e+00 6.42320454e-01 1.02331728e-01 -8.85403574e-01 -5.07869244e-01 1.41812742e-01 5.82534194e-01 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1875ca1b-68a6-4718-94cf-25b7fa2a125b	dino-detr-with-improved-denoising-anchor-1	2203.03605	null	https://arxiv.org/abs/2203.03605v4	https://arxiv.org/pdf/2203.03605v4.pdf	DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection	We present DINO (\textbf{D}ETR with \textbf{I}mproved de\textbf{N}oising anch\textbf{O}r boxes), a state-of-the-art end-to-end object detector. % in this paper. DINO improves over previous DETR-like models in performance and efficiency by using a contrastive way for denoising training, a mixed query selection method for anchor initialization, and a look forward twice scheme for box prediction. DINO achieves $49.4$AP in $12$ epochs and $51.3$AP in $24$ epochs on COCO with a ResNet-50 backbone and multi-scale features, yielding a significant improvement of $\textbf{+6.0}$\textbf{AP} and $\textbf{+2.7}$\textbf{AP}, respectively, compared to DN-DETR, the previous best DETR-like model. DINO scales well in both model size and data size. Without bells and whistles, after pre-training on the Objects365 dataset with a SwinL backbone, DINO obtains the best results on both COCO \texttt{val2017} ($\textbf{63.2}$\textbf{AP}) and \texttt{test-dev} (\textbf{$\textbf{63.3}$AP}). Compared to other models on the leaderboard, DINO significantly reduces its model size and pre-training data size while achieving better results. Our code will be available at \url{https://github.com/IDEACVR/DINO}.	['Heung-Yeung Shum', 'Lionel M. Ni', 'Jun Zhu', 'Hang Su', 'Lei Zhang', 'Shilong Liu', 'Feng Li', 'Hao Zhang']	2022-03-07	dino-detr-with-improved-denoising-anchor	null	null	null	['real-time-object-detection']	['computer-vision']	[-2.75166035e-01 -1.26498267e-01 1.85315181e-02 -3.67215663e-01 -1.35991204e+00 -7.02542663e-01 2.07588851e-01 9.87604037e-02 -8.36773217e-01 5.30820668e-01 -3.03042889e-01 -3.31780761e-01 -1.18858740e-01 -6.91948652e-01 -1.12468088e+00 -5.28412819e-01 -1.77885190e-01 5.69621503e-01 5.41672111e-01 -2.26874456e-01 -5.09778000e-02 2.44182527e-01 -1.39447665e+00 3.42176825e-01 5.36245346e-01 1.60613155e+00 2.24622145e-01 6.92707896e-01 9.09012780e-02 7.43664324e-01 -8.40193927e-01 -8.98594916e-01 4.11161602e-01 -2.82564051e-02 -4.86596286e-01 -4.15668011e-01 8.44130278e-01 -3.21955740e-01 -5.59215844e-01 1.09400451e+00 1.01397717e+00 2.68670768e-01 5.47576189e-01 -9.25985456e-01 -6.12156332e-01 7.00931132e-01 -8.89702559e-01 6.12874389e-01 -1.33537233e-01 2.35444248e-01 1.27843189e+00 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52530c1d-42ee-4524-bfad-a2d51017dff3	fade-fusing-the-assets-of-decoder-and-encoder	2207.10392	null	https://arxiv.org/abs/2207.10392v2	https://arxiv.org/pdf/2207.10392v2.pdf	FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic Upsampling	We consider the problem of task-agnostic feature upsampling in dense prediction where an upsampling operator is required to facilitate both region-sensitive tasks like semantic segmentation and detail-sensitive tasks such as image matting. Existing upsampling operators often can work well in either type of the tasks, but not both. In this work, we present FADE, a novel, plug-and-play, and task-agnostic upsampling operator. FADE benefits from three design choices: i) considering encoder and decoder features jointly in upsampling kernel generation; ii) an efficient semi-shift convolutional operator that enables granular control over how each feature point contributes to upsampling kernels; iii) a decoder-dependent gating mechanism for enhanced detail delineation. We first study the upsampling properties of FADE on toy data and then evaluate it on large-scale semantic segmentation and image matting. In particular, FADE reveals its effectiveness and task-agnostic characteristic by consistently outperforming recent dynamic upsampling operators in different tasks. It also generalizes well across convolutional and transformer architectures with little computational overhead. Our work additionally provides thoughtful insights on what makes for task-agnostic upsampling. Code is available at: http://lnkiy.in/fade_in	['Zhiguo Cao', 'Hongtao Fu', 'Wenze Liu', 'Hao Lu']	2022-07-21	null	null	null	null	['image-matting']	['computer-vision']	[ 4.25993085e-01 4.78051603e-02 -1.80349618e-01 -6.03628755e-01 -1.04694700e+00 -3.81075263e-01 6.21737361e-01 -1.38366267e-01 -2.56497771e-01 5.56109190e-01 4.33079094e-01 -3.63592446e-01 4.93029356e-02 -7.71014810e-01 -9.31210399e-01 -5.21597922e-01 1.08627388e-02 2.51397133e-01 5.71867526e-01 -2.56709784e-01 1.40070185e-01 4.51743782e-01 -1.52775049e+00 7.41084278e-01 9.67093706e-01 1.14004493e+00 6.20356083e-01 7.14472234e-01 -1.02926739e-01 7.80460835e-01 -2.26670951e-01 -3.31976891e-01 5.19648015e-01 -2.16083258e-01 -1.03668642e+00 2.99504906e-01 8.47254097e-01 -4.11092043e-01 -2.43245244e-01 7.10670769e-01 4.78584409e-01 1.16424039e-02 3.71984839e-01 -1.03787315e+00 -5.95164716e-01 8.37364852e-01 -7.42852032e-01 5.57714045e-01 -1.95086315e-01 5.24582505e-01 9.92628932e-01 -8.68114591e-01 6.00859880e-01 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47e69895-d6db-4cd8-b1c9-d8b8b4d58c91	coral-a-context-aware-croatian-abusive	2211.06053	null	https://arxiv.org/abs/2211.06053v1	https://arxiv.org/pdf/2211.06053v1.pdf	CoRAL: a Context-aware Croatian Abusive Language Dataset	In light of unprecedented increases in the popularity of the internet and social media, comment moderation has never been a more relevant task. Semi-automated comment moderation systems greatly aid human moderators by either automatically classifying the examples or allowing the moderators to prioritize which comments to consider first. However, the concept of inappropriate content is often subjective, and such content can be conveyed in many subtle and indirect ways. In this work, we propose CoRAL -- a language and culturally aware Croatian Abusive dataset covering phenomena of implicitness and reliance on local and global context. We show experimentally that current models degrade when comments are not explicit and further degrade when language skill and context knowledge are required to interpret the comment.	['Matthew Purver', 'Mladen Karan', 'Ravi Shekhar']	2022-11-11	null	null	null	null	['abusive-language']	['natural-language-processing']	[ 2.85757277e-02 -1.46064591e-02 -4.75212932e-01 -3.99456382e-01 -5.53391159e-01 -8.82320344e-01 8.79347920e-01 6.52087808e-01 -4.90569443e-01 9.38925982e-01 9.15892303e-01 -6.21834636e-01 2.69842356e-01 -2.69290119e-01 5.35795651e-02 -2.67603725e-01 2.15032086e-01 2.88542390e-01 -1.51136704e-02 -4.58060950e-01 5.76775730e-01 -4.56994474e-02 -1.30895102e+00 3.92975748e-01 1.15501952e+00 4.51584756e-01 -3.09282273e-01 5.50491452e-01 -2.25734815e-01 1.02082574e+00 -9.09089625e-01 -4.84440327e-01 1.00802509e-02 -7.67121911e-01 -5.48750877e-01 9.31888074e-02 7.09826827e-01 -3.44664842e-01 1.38056204e-01 1.10081446e+00 4.66112792e-01 -8.67246762e-02 7.43774652e-01 -8.65788460e-01 -7.59702384e-01 1.22872651e+00 -5.27196765e-01 5.01567185e-01 5.24834633e-01 2.63861157e-02 1.24412298e+00 -9.86165941e-01 9.67941523e-01 1.25088346e+00 6.69992507e-01 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c80bc69f-253a-47ea-9e0e-29e54cbf7102	advhat-real-world-adversarial-attack-on	1908.08705	null	https://arxiv.org/abs/1908.08705v1	https://arxiv.org/pdf/1908.08705v1.pdf	AdvHat: Real-world adversarial attack on ArcFace Face ID system	In this paper we propose a novel easily reproducible technique to attack the best public Face ID system ArcFace in different shooting conditions. To create an attack, we print the rectangular paper sticker on a common color printer and put it on the hat. The adversarial sticker is prepared with a novel algorithm for off-plane transformations of the image which imitates sticker location on the hat. Such an approach confuses the state-of-the-art public Face ID model LResNet100E-IR, ArcFace@ms1m-refine-v2 and is transferable to other Face ID models.	['Stepan Komkov', 'Aleksandr Petiushko']	2019-08-23	null	null	null	null	['real-world-adversarial-attack']	['adversarial']	[ 1.05069749e-01 4.92059082e-01 2.82028437e-01 -1.31164908e-01 -3.78134400e-01 -1.17021835e+00 6.31004214e-01 -1.17649710e+00 -1.41058475e-01 2.37882182e-01 -5.25667071e-01 -2.17241600e-01 1.26425400e-01 -4.81930465e-01 -8.83814871e-01 -5.36199152e-01 1.43099189e-01 8.03477347e-01 1.45319238e-01 -4.72856820e-01 9.28145051e-02 1.13330460e+00 -1.27275217e+00 2.80232698e-01 1.71121165e-01 8.61070514e-01 -5.09291828e-01 8.69186163e-01 2.16305122e-01 3.92822981e-01 -7.68735290e-01 -1.21400464e+00 9.96696174e-01 -1.71788916e-01 -9.21457231e-01 -4.59655702e-01 1.19318128e+00 -4.73239988e-01 -3.81691575e-01 1.22548068e+00 4.12250400e-01 -3.92513305e-01 8.23349059e-01 -1.75323486e+00 -1.06244683e+00 3.02869260e-01 -8.63143682e-01 -6.10189438e-02 5.39177358e-01 2.08354786e-01 2.51813829e-01 -6.23826385e-01 1.01699114e+00 1.83212161e+00 8.23814392e-01 1.17630410e+00 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-5.19774556e-01 2.80464053e-01 4.19840962e-01 -7.27682471e-01 -5.51765919e-01 2.18911260e-01]	[12.83804988861084, 1.0357459783554077]
69b0d43c-98e2-4670-99e7-7dc6f5a958ec	learning-position-and-target-consistency-for	2104.04329	null	https://arxiv.org/abs/2104.04329v1	https://arxiv.org/pdf/2104.04329v1.pdf	Learning Position and Target Consistency for Memory-based Video Object Segmentation	This paper studies the problem of semi-supervised video object segmentation(VOS). Multiple works have shown that memory-based approaches can be effective for video object segmentation. They are mostly based on pixel-level matching, both spatially and temporally. The main shortcoming of memory-based approaches is that they do not take into account the sequential order among frames and do not exploit object-level knowledge from the target. To address this limitation, we propose to Learn position and target Consistency framework for Memory-based video object segmentation, termed as LCM. It applies the memory mechanism to retrieve pixels globally, and meanwhile learns position consistency for more reliable segmentation. The learned location response promotes a better discrimination between target and distractors. Besides, LCM introduces an object-level relationship from the target to maintain target consistency, making LCM more robust to error drifting. Experiments show that our LCM achieves state-of-the-art performance on both DAVIS and Youtube-VOS benchmark. And we rank the 1st in the DAVIS 2020 challenge semi-supervised VOS task.	['Rong Jin', 'Yinghui Xu', 'Pan Pan', 'Bang Zhang', 'Peng Zhang', 'Li Hu']	2021-04-09	null	http://openaccess.thecvf.com//content/CVPR2021/html/Hu_Learning_Position_and_Target_Consistency_for_Memory-Based_Video_Object_Segmentation_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Hu_Learning_Position_and_Target_Consistency_for_Memory-Based_Video_Object_Segmentation_CVPR_2021_paper.pdf	cvpr-2021-1	['one-shot-visual-object-segmentation']	['computer-vision']	[ 2.06286330e-02 -3.87484372e-01 -7.28749037e-01 -4.10780132e-01 -7.56411612e-01 -2.91634381e-01 3.04679066e-01 -4.00096439e-02 -6.19293511e-01 4.28268492e-01 -2.04197943e-01 2.31160760e-01 2.33981997e-01 -6.32911026e-01 -1.01703346e+00 -7.18402326e-01 2.22420767e-01 3.63617331e-01 1.24142945e+00 2.05962792e-01 3.87348056e-01 2.05009177e-01 -1.51443207e+00 4.15894955e-01 8.63914967e-01 1.25256026e+00 7.61968613e-01 3.89162809e-01 -2.87653774e-01 8.25080752e-01 -5.36944270e-01 2.34159455e-01 3.92221332e-01 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b07dd5c6-5153-4b7b-a9b6-ac1c1fa209ef	deep-sr-itm-joint-learning-of-super	1904.11176	null	https://arxiv.org/abs/1904.11176v3	https://arxiv.org/pdf/1904.11176v3.pdf	Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR Applications	Recent modern displays are now able to render high dynamic range (HDR), high resolution (HR) videos of up to 8K UHD (Ultra High Definition). Consequently, UHD HDR broadcasting and streaming have emerged as high quality premium services. However, due to the lack of original UHD HDR video content, appropriate conversion technologies are urgently needed to transform the legacy low resolution (LR) standard dynamic range (SDR) videos into UHD HDR versions. In this paper, we propose a joint super-resolution (SR) and inverse tone-mapping (ITM) framework, called Deep SR-ITM, which learns the direct mapping from LR SDR video to their HR HDR version. Joint SR and ITM is an intricate task, where high frequency details must be restored for SR, jointly with the local contrast, for ITM. Our network is able to restore fine details by decomposing the input image and focusing on the separate base (low frequency) and detail (high frequency) layers. Moreover, the proposed modulation blocks apply location-variant operations to enhance local contrast. The Deep SR-ITM shows good subjective quality with increased contrast and details, outperforming the previous joint SR-ITM method.	['Soo Ye Kim', 'Jihyong Oh', 'Munchurl Kim']	2019-04-25	deep-sr-itm-joint-learning-of-super-1	http://openaccess.thecvf.com/content_ICCV_2019/html/Kim_Deep_SR-ITM_Joint_Learning_of_Super-Resolution_and_Inverse_Tone-Mapping_for_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Kim_Deep_SR-ITM_Joint_Learning_of_Super-Resolution_and_Inverse_Tone-Mapping_for_ICCV_2019_paper.pdf	iccv-2019-10	['tone-mapping', 'inverse-tone-mapping']	['computer-vision', 'computer-vision']	[ 6.73604071e-01 -1.92416951e-01 -5.45336641e-02 -1.89214602e-01 -9.55557585e-01 -1.67926505e-01 5.35037279e-01 -6.88167334e-01 -4.69997227e-02 9.08547938e-01 4.92794275e-01 -1.06403746e-01 -1.51247904e-01 -9.42265093e-01 -6.49201334e-01 -8.24222267e-01 -4.71915752e-02 -3.41550469e-01 4.23556149e-01 -5.01567841e-01 3.01744826e-02 3.89118522e-01 -2.00719905e+00 6.07134163e-01 8.65250051e-01 1.15051579e+00 6.48081243e-01 1.06731141e+00 2.54534543e-01 1.08039165e+00 -4.51668113e-01 8.58092383e-02 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bda2f6d1-c3ff-4a8b-b9c9-a12c52bf9406	contrastive-learning-with-adversarial-2	2012.07280	null	https://arxiv.org/abs/2012.07280v6	https://arxiv.org/pdf/2012.07280v6.pdf	Contrastive Learning with Adversarial Perturbations for Conditional Text Generation	Recently, sequence-to-sequence (seq2seq) models with the Transformer architecture have achieved remarkable performance on various conditional text generation tasks, such as machine translation. However, most of them are trained with teacher forcing with the ground truth label given at each time step, without being exposed to incorrectly generated tokens during training, which hurts its generalization to unseen inputs, that is known as the "exposure bias" problem. In this work, we propose to mitigate the conditional text generation problem by contrasting positive pairs with negative pairs, such that the model is exposed to various valid or incorrect perturbations of the inputs, for improved generalization. However, training the model with naive contrastive learning framework using random non-target sequences as negative examples is suboptimal, since they are easily distinguishable from the correct output, especially so with models pretrained with large text corpora. Also, generating positive examples requires domain-specific augmentation heuristics which may not generalize over diverse domains. To tackle this problem, we propose a principled method to generate positive and negative samples for contrastive learning of seq2seq models. Specifically, we generate negative examples by adding small perturbations to the input sequence to minimize its conditional likelihood, and positive examples by adding large perturbations while enforcing it to have a high conditional likelihood. Such "hard" positive and negative pairs generated using our method guides the model to better distinguish correct outputs from incorrect ones. We empirically show that our proposed method significantly improves the generalization of the seq2seq on three text generation tasks - machine translation, text summarization, and question generation.	['Sung Ju Hwang', 'Dong Bok Lee', 'Seanie Lee']	2020-12-14	contrastive-learning-with-adversarial	https://openreview.net/forum?id=Wga_hrCa3P3	https://openreview.net/pdf?id=Wga_hrCa3P3	iclr-2021-1	['conditional-text-generation']	['natural-language-processing']	[ 9.34103787e-01 4.29184586e-01 -6.86851144e-02 -2.87264735e-01 -1.02243972e+00 -8.44387472e-01 8.29596698e-01 -2.15748157e-02 -3.93868238e-01 1.23293567e+00 2.65969753e-01 -4.00455415e-01 4.93429363e-01 -8.55779469e-01 -8.65270853e-01 -6.87635958e-01 5.15260398e-01 7.01336324e-01 -5.62108085e-02 -4.63920325e-01 1.66423559e-01 -1.54955655e-01 -1.20834017e+00 5.84603131e-01 1.43309677e+00 5.61630189e-01 3.57415110e-01 6.53286457e-01 -2.45411158e-01 6.52446985e-01 -1.10729730e+00 -6.46329761e-01 1.07380740e-01 -1.03399539e+00 -9.26376224e-01 -5.70825711e-02 3.78885210e-01 -1.75020069e-01 1.48124322e-01 1.16967165e+00 5.45095444e-01 1.85293272e-01 8.85131240e-01 -1.07908297e+00 -9.84280646e-01 7.55354047e-01 -3.60154390e-01 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f3ff4346-c043-4b92-b112-8013f3075e1e	sound-explanation-for-trustworthy-machine	2306.06134	null	https://arxiv.org/abs/2306.06134v1	https://arxiv.org/pdf/2306.06134v1.pdf	Sound Explanation for Trustworthy Machine Learning	We take a formal approach to the explainability problem of machine learning systems. We argue against the practice of interpreting black-box models via attributing scores to input components due to inherently conflicting goals of attribution-based interpretation. We prove that no attribution algorithm satisfies specificity, additivity, completeness, and baseline invariance. We then formalize the concept, sound explanation, that has been informally adopted in prior work. A sound explanation entails providing sufficient information to causally explain the predictions made by a system. Finally, we present the application of feature selection as a sound explanation for cancer prediction models to cultivate trust among clinicians.	['Martin Rinard', 'Limor Appelbaum', 'Pasapol Saowakon', 'Kai Jia']	2023-06-08	null	null	null	null	['specificity']	['natural-language-processing']	[ 7.65123904e-01 9.92244363e-01 -7.17835605e-01 -7.37368047e-01 -1.80849805e-01 -2.97636420e-01 6.42098188e-01 4.02863443e-01 1.02859832e-01 8.65725100e-01 3.89133990e-01 -9.01574612e-01 -6.52856350e-01 -3.13865721e-01 -5.52466750e-01 -2.64482021e-01 8.68834481e-02 3.48696679e-01 -3.66502017e-01 3.47706452e-02 3.71666998e-01 4.33888644e-01 -1.16099513e+00 4.47072357e-01 7.46797144e-01 4.92350996e-01 -3.33194166e-01 8.26131701e-01 2.19336852e-01 1.39160407e+00 -3.02884072e-01 -7.17828035e-01 -1.93822712e-01 -9.70988929e-01 -9.69477475e-01 1.17130943e-01 1.00283459e-01 -2.12857649e-01 -5.93988560e-02 8.45040202e-01 -2.78280973e-01 -2.96180010e-01 1.10293484e+00 -2.07210159e+00 -1.23271728e+00 8.98908734e-01 -3.56976278e-02 -6.25500828e-02 4.02139872e-01 -2.83669475e-02 1.43993890e+00 -5.64867437e-01 3.28474194e-01 1.18216884e+00 7.27058768e-01 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d443e7e9-fb5f-4a20-a98b-d762d39dfdc5	cognition-guided-human-object-relationship	null	null	https://ieeexplore.ieee.org/document/10112623	https://ieeexplore.ieee.org/document/10112623	Cognition Guided Human-Object Relationship Detection	Human-object relationship detection reveals the fine-grained relationship between humans and objects, helping the comprehensive understanding of videos. Previous human-object relationship detection approaches are mainly developed with object features and relation features without exploring the specific information of humans. In this paper, we propose a novel Relation-Pose Transformer (RPT) for human-object relationship detection. Inspired by the coordination of eye-head-body movements in cognitive science, we employ the head pose to find those crucial objects that humans focus on and use the body pose with skeleton information to represent multiple actions. Then, we utilize the spatial encoder to capture spatial contextualized information of the relation pair, which integrates the relation features and pose features. Next, the temporal decoder aims to model the temporal dependency of the relationship. Finally, we adopt multiple classifiers to predict different types of relationships. Extensive experiments on the benchmark Action Genome validate the effectiveness of our proposed method and show the state-of-the-art performance compared with related methods.	['Xiaochun Cao', 'Lei Zhang', 'Xuan Zhang', 'Pengwen Dai', 'Zhitao Zeng']	2023-05-06	null	null	null	journal-2023-5	['human-object-interaction-detection']	['computer-vision']	[-1.52126579e-02 -2.47655258e-01 -2.95073807e-01 -4.73018795e-01 -1.70334324e-01 3.86134349e-02 6.74459517e-01 -7.73189515e-02 -4.23839331e-01 3.47098261e-01 5.21180391e-01 3.82387251e-01 -2.88057089e-01 -4.38686132e-01 -4.92985845e-01 -5.33910394e-01 5.28396256e-02 1.62438855e-01 8.07555974e-01 -2.42522657e-01 2.10429758e-01 3.27385515e-01 -1.66435277e+00 3.39479536e-01 3.64660949e-01 1.20208347e+00 4.69440311e-01 3.29508960e-01 4.15842414e-01 1.07560766e+00 -4.07756716e-01 -1.88812599e-01 -4.65240367e-02 -5.32870650e-01 -6.61325276e-01 3.19708169e-01 2.57339597e-01 -5.32773912e-01 -6.88792408e-01 8.27258050e-01 5.98320246e-01 3.22421044e-01 4.25032377e-01 -1.45654237e+00 -3.27383161e-01 2.96134025e-01 -9.64604855e-01 7.13836193e-01 8.38778555e-01 1.62206948e-01 8.22695613e-01 -9.14771795e-01 6.10101402e-01 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b96e74f0-59e3-40a1-b68f-0679fc4e540c	improving-abstractive-dialogue-summarization-2	null	null	https://aclanthology.org/2021.findings-emnlp.97	https://aclanthology.org/2021.findings-emnlp.97.pdf	Improving Abstractive Dialogue Summarization with Hierarchical Pretraining and Topic Segment	With the increasing abundance of meeting transcripts, meeting summary has attracted more and more attention from researchers. The unsupervised pre-training method based on transformer structure combined with fine-tuning of downstream tasks has achieved great success in the field of text summarization. However, the semantic structure and style of meeting transcripts are quite different from that of articles. In this work, we propose a hierarchical transformer encoder-decoder network with multi-task pre-training. Specifically, we mask key sentences at the word-level encoder and generate them at the decoder. Besides, we randomly mask some of the role alignments in the input text and force the model to recover the original role tags to complete the alignments. In addition, we introduce a topic segmentation mechanism to further improve the quality of the generated summaries. The experimental results show that our model is superior to the previous methods in meeting summary datasets AMI and ICSI.	['Ting Liu', 'Yuzhuo Fu', 'Hao liu', 'MengNan Qi']	null	null	null	null	findings-emnlp-2021-11	['unsupervised-pre-training']	['methodology']	[ 5.14991462e-01 3.63233417e-01 -8.70237872e-02 -5.17983377e-01 -1.18905210e+00 -4.80472744e-01 4.47851717e-01 2.08222419e-01 -1.08209431e-01 8.48109245e-01 1.14565098e+00 -6.64584562e-02 2.27607071e-01 -4.95218068e-01 -5.36720514e-01 -5.17199039e-01 4.99329716e-01 4.81626719e-01 1.03843458e-01 -1.50375500e-01 5.01993597e-01 -4.50535983e-01 -1.00361705e+00 6.76112711e-01 1.38307452e+00 3.72396410e-01 6.06377780e-01 5.32512307e-01 -3.67535859e-01 6.48621440e-01 -1.07398939e+00 -3.44987065e-01 -2.87165821e-01 -8.25134099e-01 -7.67788708e-01 3.93836856e-01 5.57818189e-02 -2.56450623e-01 -3.91417146e-01 8.67377162e-01 7.60169864e-01 -3.44041102e-02 7.12208092e-01 -6.91707850e-01 -5.24189115e-01 1.26917112e+00 -6.94026291e-01 1.39490858e-01 3.64671588e-01 -3.36971253e-01 1.20953262e+00 -6.21467233e-01 3.82015675e-01 1.24310935e+00 2.39332512e-01 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db594b5a-8e01-4953-98a0-b77da383d65d	numerical-gaussian-process-kalman-filtering-1	2105.02079	null	https://arxiv.org/abs/2105.02079v1	https://arxiv.org/pdf/2105.02079v1.pdf	Numerical Gaussian process Kalman filtering for spatiotemporal systems	We present a novel Kalman filter for spatiotemporal systems called the numerical Gaussian process Kalman filter (GPKF). Numerical Gaussian processes have recently been introduced as a physics informed machine learning method for simulating time-dependent partial differential equations without the need for spatial discretization. We bring numerical GPs into probabilistic state space form. This model is linear and its states are Gaussian distributed. These properties enable us to embed the numerical GP state space model into the recursive Kalman filter algorithm. We showcase the method using two case studies.	['Steffen Waldherr', 'Armin Küper']	2021-05-05	null	null	null	null	['physics-informed-machine-learning']	['graphs']	[-5.37320256e-01 1.32005647e-01 1.31746426e-01 1.30079210e-01 -6.28154397e-01 -4.43298191e-01 1.15565336e+00 -1.10756211e-01 -4.01287317e-01 1.16437459e+00 -1.55165106e-01 -5.45198858e-01 -3.14008713e-01 -7.59453893e-01 -4.29287225e-01 -1.07666850e+00 -3.41049075e-01 5.55715203e-01 3.84177178e-01 2.63379484e-01 1.17397591e-01 8.63454878e-01 -8.76511276e-01 -7.00965405e-01 7.26344824e-01 6.41493976e-01 6.02257438e-02 1.22504795e+00 1.73524559e-01 8.21596265e-01 -3.06954622e-01 1.73206672e-01 1.56490326e-01 -1.53122321e-01 -2.92891830e-01 -1.76155984e-01 -4.67371225e-01 -2.03112215e-01 -1.23151696e+00 8.72083604e-01 2.63293028e-01 7.61912346e-01 1.18125856e+00 -1.33124018e+00 -2.92325139e-01 2.98104018e-01 -6.67145988e-03 3.04046184e-01 1.20662361e-01 3.49084437e-01 2.35077634e-01 -6.26911998e-01 3.87894213e-01 1.31312525e+00 9.58326280e-01 3.30828100e-01 -1.27995849e+00 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7e6c45f1-b6aa-4ca8-bf94-5ed8ec136695	learning-from-pseudo-lesion-a-self-supervised	2106.12313	null	https://arxiv.org/abs/2106.12313v1	https://arxiv.org/pdf/2106.12313v1.pdf	Learning from Pseudo Lesion: A Self-supervised Framework for COVID-19 Diagnosis	The Coronavirus disease 2019 (COVID-19) has rapidly spread all over the world since its first report in December 2019 and thoracic computed tomography (CT) has become one of the main tools for its diagnosis. In recent years, deep learning-based approaches have shown impressive performance in myriad image recognition tasks. However, they usually require a large number of annotated data for training. Inspired by Ground Glass Opacity (GGO), a common finding in COIVD-19 patient's CT scans, we proposed in this paper a novel self-supervised pretraining method based on pseudo lesions generation and restoration for COVID-19 diagnosis. We used Perlin noise, a gradient noise based mathematical model, to generate lesion-like patterns, which were then randomly pasted to the lung regions of normal CT images to generate pseudo COVID-19 images. The pairs of normal and pseudo COVID-19 images were then used to train an encoder-decoder architecture based U-Net for image restoration, which does not require any labelled data. The pretrained encoder was then fine-tuned using labelled data for COVID-19 diagnosis task. Two public COVID-19 diagnosis datasets made up of CT images were employed for evaluation. Comprehensive experimental results demonstrated that the proposed self-supervised learning approach could extract better feature representation for COVID-19 diagnosis and the accuracy of the proposed method outperformed the supervised model pretrained on large scale images by 6.57% and 3.03% on SARS-CoV-2 dataset and Jinan COVID-19 dataset, respectively.	['Linlin Shen', 'Xuechen Li', 'Zhihao Jin', 'Zhongliang Li']	2021-06-23	null	null	null	null	['covid-19-detection']	['medical']	[ 4.31061834e-01 -3.71724010e-01 2.25855231e-01 -3.23614150e-01 -6.79857194e-01 -2.95269608e-01 3.97694796e-01 -2.38056388e-02 -4.47252423e-01 7.26208091e-01 3.32641974e-02 -3.21005881e-01 -1.82176717e-02 -8.48402619e-01 -8.17689776e-01 -7.15198278e-01 -4.24333960e-02 8.07740390e-01 1.62353545e-01 1.28426895e-01 -3.29666287e-01 4.60197330e-01 -1.14823329e+00 4.00436252e-01 8.11696470e-01 9.15884137e-01 7.59271026e-01 9.58453417e-01 1.45618930e-01 7.71282732e-01 -4.86318439e-01 -4.46481593e-02 1.94729701e-01 -5.41597128e-01 -6.39559746e-01 1.24892049e-01 2.03705981e-01 -5.66668749e-01 -3.52618277e-01 7.57537723e-01 6.53574288e-01 -1.75507754e-01 1.00699019e+00 -8.71487439e-01 -6.90899551e-01 -5.31657934e-02 -4.91007209e-01 6.92718387e-01 -4.82351407e-02 2.55257100e-01 5.12327254e-01 -6.73476338e-01 9.45903182e-01 1.05990338e+00 7.02628791e-01 6.14556789e-01 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9454d648-453d-454d-b666-be5fe5b0dbb4	a-nonlinear-acceleration-method-for-iterative	1906.01595	null	https://arxiv.org/abs/1906.01595v1	https://arxiv.org/pdf/1906.01595v1.pdf	A Nonlinear Acceleration Method for Iterative Algorithms	Iterative methods have led to better understanding and solving problems such as missing sampling, deconvolution, inverse systems, impulsive and Salt and Pepper noise removal problems. However, the challenges such as the speed of convergence and or the accuracy of the answer still remain. In order to improve the existing iterative algorithms, a non-linear method is discussed in this paper. The mentioned method is analyzed from different aspects, including its convergence and its ability to accelerate recursive algorithms. We show that this method is capable of improving Iterative Method (IM) as a non-uniform sampling reconstruction algorithm and some iterative sparse recovery algorithms such as Iterative Reweighted Least Squares (IRLS), Iterative Method with Adaptive Thresholding (IMAT), Smoothed l0 (SL0) and Alternating Direction Method of Multipliers (ADMM) for solving LASSO problems family (including Lasso itself, Lasso-LSQR and group-Lasso). It is also capable of both accelerating and stabilizing the well-known Chebyshev Acceleration (CA) method. Furthermore, the proposed algorithm can extend the stability range by reducing the sensitivity of iterative algorithms to the changes of adaptation rate.	['Farokh Marvasti', 'Mahdi Shamsi', 'Mahmoud Ghandi']	2019-06-04	null	null	null	null	['salt-and-pepper-noise-removal']	['computer-vision']	[ 3.65529627e-01 -4.06790078e-01 9.24915969e-02 -2.05753580e-01 -7.53380835e-01 -3.86543006e-01 1.69508845e-01 -2.72060990e-01 -1.45639107e-01 1.09203780e+00 3.98783177e-01 -1.47800490e-01 -4.90367740e-01 -2.68845111e-01 -4.49587703e-01 -8.52926254e-01 -2.53377676e-01 2.33094379e-01 -2.37923548e-01 -2.68138468e-01 5.80976844e-01 4.77462947e-01 -1.14765060e+00 1.43440319e-02 1.24843121e+00 9.49220419e-01 1.46953404e-01 5.09961903e-01 -6.66572340e-03 8.27720344e-01 1.93388700e-01 2.97306776e-01 4.04420614e-01 -7.36463308e-01 -5.04848123e-01 1.35564953e-01 3.04040968e-01 -1.96246997e-01 5.82872927e-02 1.12624121e+00 7.97547460e-01 3.87437820e-01 5.48325360e-01 -9.04920697e-01 -5.30554831e-01 5.98981142e-01 -9.63023782e-01 1.10068828e-01 3.63670856e-01 -3.82270366e-01 3.71870339e-01 -1.57968819e+00 5.83045006e-01 1.13400996e+00 1.43567824e+00 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af98bff6-1adc-41a3-a968-e6614f2a6525	cross-lingual-cross-corpus-speech-emotion	2003.07996	null	https://arxiv.org/abs/2003.07996v1	https://arxiv.org/pdf/2003.07996v1.pdf	Cross Lingual Cross Corpus Speech Emotion Recognition	The majority of existing speech emotion recognition models are trained and evaluated on a single corpus and a single language setting. These systems do not perform as well when applied in a cross-corpus and cross-language scenario. This paper presents results for speech emotion recognition for 4 languages in both single corpus and cross corpus setting. Additionally, since multi-task learning (MTL) with gender, naturalness and arousal as auxiliary tasks has shown to enhance the generalisation capabilities of the emotion models, this paper introduces language ID as another auxiliary task in MTL framework to explore the role of spoken language on emotion recognition which has not been studied yet.	['Shivali Goel', 'Homayoon Beigi']	2020-03-18	null	null	null	null	['cross-corpus']	['computer-vision']	[-2.81024516e-01 1.54329604e-02 1.41800996e-02 -8.17157984e-01 -4.68039453e-01 -5.15322387e-01 1.06904554e+00 2.91658700e-01 -7.44002819e-01 7.28019834e-01 2.07598925e-01 -1.77384347e-01 1.63730234e-01 7.87743181e-02 -8.47083628e-02 -3.55933547e-01 -6.15334362e-02 3.72102439e-01 -2.37168401e-01 -4.31495219e-01 1.74087547e-02 3.41709614e-01 -1.47954345e+00 5.62803149e-01 5.77897072e-01 7.71254659e-01 -5.38125783e-02 9.75780487e-01 -3.76138836e-01 7.80654669e-01 -1.03472900e+00 -4.14399117e-01 -6.20903112e-02 -2.32930034e-01 -8.71081710e-01 1.13330126e-01 2.08640933e-01 4.44188446e-01 2.59807587e-01 5.64538121e-01 9.30699885e-01 3.54718715e-01 6.83173418e-01 -1.53226328e+00 -4.50023234e-01 5.15995860e-01 -1.74767315e-01 3.77109379e-01 4.82498527e-01 -5.45461178e-01 7.74305761e-01 -8.85480940e-01 3.30761313e-01 1.55923700e+00 4.24148172e-01 7.57165074e-01 -9.69678283e-01 -7.55190551e-01 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6671fdb6-7e97-4f33-86fd-4eb0b301223a	composing-text-and-image-for-image-retrieval	1812.07119	null	http://arxiv.org/abs/1812.07119v1	http://arxiv.org/pdf/1812.07119v1.pdf	Composing Text and Image for Image Retrieval - An Empirical Odyssey	In this paper, we study the task of image retrieval, where the input query is specified in the form of an image plus some text that describes desired modifications to the input image. For example, we may present an image of the Eiffel tower, and ask the system to find images which are visually similar but are modified in small ways, such as being taken at nighttime instead of during the day. To tackle this task, we learn a similarity metric between a target image and a source image plus source text, an embedding and composing function such that target image feature is close to the source image plus text composition feature. We propose a new way to combine image and text using such function that is designed for the retrieval task. We show this outperforms existing approaches on 3 different datasets, namely Fashion-200k, MIT-States and a new synthetic dataset we create based on CLEVR. We also show that our approach can be used to classify input queries, in addition to image retrieval.	['Li Fei-Fei', 'Li-Jia Li', 'Lu Jiang', 'Nam Vo', 'Kevin Murphy', 'James Hays', 'Chen Sun']	2018-12-18	composing-text-and-image-for-image-retrieval-1	http://openaccess.thecvf.com/content_CVPR_2019/html/Vo_Composing_Text_and_Image_for_Image_Retrieval_-_an_Empirical_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Vo_Composing_Text_and_Image_for_Image_Retrieval_-_an_Empirical_CVPR_2019_paper.pdf	cvpr-2019-6	['multi-modal']	['miscellaneous']	[ 6.31381929e-01 -3.83079231e-01 1.83635518e-01 -6.14614964e-01 -6.88476205e-01 -1.04246283e+00 9.58136618e-01 3.53739083e-01 -7.03705609e-01 2.57505924e-01 3.72429006e-02 5.94612546e-02 -8.17928016e-02 -6.97135150e-01 -9.52896357e-01 -3.71915519e-01 2.54053503e-01 5.00327170e-01 3.76627803e-01 -3.58188063e-01 3.88952106e-01 4.42987680e-01 -1.81702626e+00 3.21060896e-01 1.35277972e-01 8.66774619e-01 6.40760660e-01 8.63267362e-01 -3.85942012e-02 4.16493326e-01 -7.10357666e-01 -4.34012771e-01 5.93856990e-01 -4.19210523e-01 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0ade2d7f-cafb-43db-a7b9-88afb53a1c43	laeo-net-revisiting-people-looking-at-each-1	1906.05261	null	https://arxiv.org/abs/1906.05261v1	https://arxiv.org/pdf/1906.05261v1.pdf	LAEO-Net: revisiting people Looking At Each Other in videos	Capturing the `mutual gaze' of people is essential for understanding and interpreting the social interactions between them. To this end, this paper addresses the problem of detecting people Looking At Each Other (LAEO) in video sequences. For this purpose, we propose LAEO-Net, a new deep CNN for determining LAEO in videos. In contrast to previous works, LAEO-Net takes spatio-temporal tracks as input and reasons about the whole track. It consists of three branches, one for each character's tracked head and one for their relative position. Moreover, we introduce two new LAEO datasets: UCO-LAEO and AVA-LAEO. A thorough experimental evaluation demonstrates the ability of LAEONet to successfully determine if two people are LAEO and the temporal window where it happens. Our model achieves state-of-the-art results on the existing TVHID-LAEO video dataset, significantly outperforming previous approaches. Finally, we apply LAEO-Net to social network analysis, where we automatically infer the social relationship between pairs of people based on the frequency and duration that they LAEO.	['Pablo Medina-Suarez', 'Vicky Kalogeiton', 'Manuel J. Marin-Jimenez', 'Andrew Zisserman']	2019-06-12	laeo-net-revisiting-people-looking-at-each	http://openaccess.thecvf.com/content_CVPR_2019/html/Marin-Jimenez_LAEO-Net_Revisiting_People_Looking_at_Each_Other_in_Videos_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Marin-Jimenez_LAEO-Net_Revisiting_People_Looking_at_Each_Other_in_Videos_CVPR_2019_paper.pdf	cvpr-2019-6	['mutual-gaze']	['computer-vision']	[-3.46094102e-01 -2.63805419e-01 -8.79932716e-02 -4.38412219e-01 5.51371872e-02 -5.83161175e-01 6.74752474e-01 1.01272315e-01 -4.53976482e-01 3.34586710e-01 3.71014893e-01 7.74244368e-02 -1.28379241e-02 -6.57389045e-01 -7.10661948e-01 -4.82835859e-01 -5.23802400e-01 5.38023233e-01 3.30418587e-01 -1.82456166e-01 -6.77186176e-02 2.24804848e-01 -1.48463643e+00 3.78518969e-01 1.11838743e-01 9.37737942e-01 -3.31264228e-01 1.13391662e+00 5.06807387e-01 1.30955827e+00 -6.97935462e-01 -5.40808380e-01 -9.63216349e-02 -4.18739527e-01 -6.70788884e-01 2.44312212e-01 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db0291b5-5598-43bf-be42-53fb002ef872	beyond-visual-attractiveness-physically	2103.12926	null	https://arxiv.org/abs/2103.12926v1	https://arxiv.org/pdf/2103.12926v1.pdf	Beyond Visual Attractiveness: Physically Plausible Single Image HDR Reconstruction for Spherical Panoramas	HDR reconstruction is an important task in computer vision with many industrial needs. The traditional approaches merge multiple exposure shots to generate HDRs that correspond to the physical quantity of illuminance of the scene. However, the tedious capturing process makes such multi-shot approaches inconvenient in practice. In contrast, recent single-shot methods predict a visually appealing HDR from a single LDR image through deep learning. But it is not clear whether the previously mentioned physical properties would still hold, without training the network to explicitly model them. In this paper, we introduce the physical illuminance constraints to our single-shot HDR reconstruction framework, with a focus on spherical panoramas. By the proposed physical regularization, our method can generate HDRs which are not only visually appealing but also physically plausible. For evaluation, we collect a large dataset of LDR and HDR images with ground truth illuminance measures. Extensive experiments show that our HDR images not only maintain high visual quality but also top all baseline methods in illuminance prediction accuracy.	['Gang Hua', 'Ying Wu', 'Haoxiang Li', 'Hao Kang', 'Yue Liu', 'Li Guan', 'Wei Wei']	2021-03-24	null	null	null	null	['single-shot-hdr-reconstruction', 'hdr-reconstruction']	['computer-vision', 'computer-vision']	[ 2.56694496e-01 -2.10890517e-01 1.69006526e-01 -2.11978242e-01 -6.15159631e-01 -7.60762393e-02 5.50158858e-01 -5.65390348e-01 3.88603926e-01 6.81780100e-01 1.55252576e-01 -4.02906463e-02 -2.43535396e-02 -8.80472422e-01 -8.61581802e-01 -8.66489291e-01 4.44905579e-01 9.18952562e-03 2.61365268e-02 -2.71869630e-01 2.34083515e-02 5.49046159e-01 -1.87930822e+00 1.99530725e-04 1.07681608e+00 1.06598926e+00 7.08632290e-01 8.51810038e-01 2.21469760e-01 1.34122634e+00 -3.45842451e-01 -3.51876527e-01 6.38226688e-01 -5.49398422e-01 -4.12110150e-01 4.61868137e-01 6.76751554e-01 -9.47520018e-01 -8.82883072e-01 9.05779481e-01 6.19721055e-01 5.27787983e-01 7.03689575e-01 -1.09118497e+00 -1.23402941e+00 1.73510821e-03 -8.50118577e-01 -3.05609226e-01 3.88110787e-01 6.37585700e-01 1.05777967e+00 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ef5be60b-884c-4e62-9b36-1c61a79d424c	sentence-level-event-detection-without	2306.14176	null	https://arxiv.org/abs/2306.14176v1	https://arxiv.org/pdf/2306.14176v1.pdf	Sentence-level Event Detection without Triggers via Prompt Learning and Machine Reading Comprehension	The traditional way of sentence-level event detection involves two important subtasks: trigger identification and trigger classifications, where the identified event trigger words are used to classify event types from sentences. However, trigger classification highly depends on abundant annotated trigger words and the accuracy of trigger identification. In a real scenario, annotating trigger words is time-consuming and laborious. For this reason, we propose a trigger-free event detection model, which transforms event detection into a two-tower model based on machine reading comprehension and prompt learning. Compared to existing trigger-based and trigger-free methods, experimental studies on two event detection benchmark datasets (ACE2005 and MAVEN) have shown that the proposed approach can achieve competitive performance.	['Hai-Lin Liu', 'Zicheng Cai', 'Huangxu Sheng', 'Lei Chen', 'Tongtao Ling']	2023-06-25	null	null	null	null	['reading-comprehension', 'machine-reading-comprehension']	['natural-language-processing', 'natural-language-processing']	[ 3.83881062e-01 -2.71809489e-01 -8.28672647e-02 -3.29208702e-01 -1.01601326e+00 -7.25145280e-01 6.89652741e-01 9.32080090e-01 -6.61579013e-01 6.45240784e-01 5.02142847e-01 -3.84255379e-01 9.01480839e-02 -8.45320523e-01 -2.17707589e-01 -2.49700040e-01 -5.44043370e-02 5.70848547e-02 7.45751798e-01 -4.38970327e-02 3.16255480e-01 1.22942127e-01 -1.46498334e+00 6.83670700e-01 8.19868505e-01 1.08100140e+00 2.31373802e-01 5.76855183e-01 -4.10527796e-01 1.32013047e+00 -7.53553271e-01 1.70921803e-01 -1.21402428e-01 -7.69664526e-01 -9.98337865e-01 -3.10590059e-01 -5.58542192e-01 -1.55110464e-01 -2.42737234e-01 7.26224661e-01 5.64408600e-01 -4.19562217e-03 4.71085340e-01 -8.60815346e-01 2.91991174e-01 9.99967456e-01 -3.11494708e-01 1.09134698e+00 9.58627939e-01 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b6b42e8e-565d-4ff1-9e9b-b01d5e72f054	action-recognition-with-trajectory-pooled	1505.04868	null	http://arxiv.org/abs/1505.04868v1	http://arxiv.org/pdf/1505.04868v1.pdf	Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors	Visual features are of vital importance for human action understanding in videos. This paper presents a new video representation, called trajectory-pooled deep-convolutional descriptor (TDD), which shares the merits of both hand-crafted features and deep-learned features. Specifically, we utilize deep architectures to learn discriminative convolutional feature maps, and conduct trajectory-constrained pooling to aggregate these convolutional features into effective descriptors. To enhance the robustness of TDDs, we design two normalization methods to transform convolutional feature maps, namely spatiotemporal normalization and channel normalization. The advantages of our features come from (i) TDDs are automatically learned and contain high discriminative capacity compared with those hand-crafted features; (ii) TDDs take account of the intrinsic characteristics of temporal dimension and introduce the strategies of trajectory-constrained sampling and pooling for aggregating deep-learned features. We conduct experiments on two challenging datasets: HMDB51 and UCF101. Experimental results show that TDDs outperform previous hand-crafted features and deep-learned features. Our method also achieves superior performance to the state of the art on these datasets (HMDB51 65.9%, UCF101 91.5%).	['Yu Qiao', 'Limin Wang', 'Xiaoou Tang']	2015-05-19	action-recognition-with-trajectory-pooled-1	http://openaccess.thecvf.com/content_cvpr_2015/html/Wang_Action_Recognition_With_2015_CVPR_paper.html	http://openaccess.thecvf.com/content_cvpr_2015/papers/Wang_Action_Recognition_With_2015_CVPR_paper.pdf	cvpr-2015-6	['action-understanding', 'activity-recognition-in-videos']	['computer-vision', 'computer-vision']	[-5.84065802e-02 -6.72756016e-01 -4.04075682e-01 -3.33308488e-01 -7.09136426e-01 -3.15410107e-01 8.18044484e-01 -1.89746007e-01 -6.21681094e-01 5.03443182e-01 7.21053004e-01 1.42256677e-01 -2.60007650e-01 -7.20544100e-01 -6.29498541e-01 -7.61116803e-01 -4.67202812e-01 -4.16140765e-01 4.41523224e-01 -8.66283011e-03 6.56071082e-02 6.69880450e-01 -1.46041536e+00 3.70189548e-01 6.43767536e-01 1.48378003e+00 6.71906248e-02 3.43503505e-01 1.94668576e-01 9.85278368e-01 -4.57500786e-01 -5.10574505e-02 3.63962352e-01 -4.18256700e-01 -6.23999298e-01 1.54662415e-01 2.24950314e-01 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f5401d82-3832-48df-b8de-ef03453dc7f5	learning-to-stabilize-high-dimensional	2306.08722	null	https://arxiv.org/abs/2306.08722v1	https://arxiv.org/pdf/2306.08722v1.pdf	Learning to Stabilize High-dimensional Unknown Systems Using Lyapunov-guided Exploration	Designing stabilizing controllers is a fundamental challenge in autonomous systems, particularly for high-dimensional, nonlinear systems that cannot be accurately modeled using differential equations. Lyapunov theory offers a robust solution for stabilizing control systems, but current methods relying on Lyapunov functions require access to complete model information or samples of system executions throughout the entire state space. Consequently, these methods are impractical for high-dimensional systems. In this paper, we introduce a novel framework, LYGE, for learning stabilizing controllers specifically tailored to high-dimensional, unknown systems. Our approach employs Lyapunov theory to iteratively guide the search for samples during exploration while simultaneously learning the local system dynamics, control policy, and Lyapunov functions. We provide a theoretical analysis of our framework and demonstrate its scalability on highly complex systems, including a high-fidelity F-16 jet aircraft model from the Air Force featuring a 16-dimensional state space and a 4-dimensional input space. Experimental results indicate that, compared to prior works in reinforcement learning, imitation learning, and neural certificates, LYGE can reduce the distance to the goal by approximately $50\%$ while requiring only $5\%$ to $32\%$ of the samples. Furthermore, we demonstrate that our algorithm can be readily extended to learn controllers guided by alternative control certificate functions for unknown systems.	['Chuchu Fan', 'Songyuan Zhang']	2023-06-14	null	null	null	null	['imitation-learning']	['methodology']	[-1.63403839e-01 6.87193573e-02 -3.18125784e-01 4.38681632e-01 -8.32175970e-01 -8.73180211e-01 2.25543320e-01 -2.50619709e-01 -2.06825480e-01 1.16181362e+00 -4.71258134e-01 -7.94394016e-01 -1.65180668e-01 -3.77684921e-01 -8.24873090e-01 -6.80081844e-01 -4.81834978e-01 1.75366491e-01 -1.42732456e-01 -3.70045185e-01 5.99509962e-02 5.95613480e-01 -1.18992114e+00 -5.90059340e-01 6.96345568e-01 8.34410906e-01 -1.03808865e-02 8.08604956e-01 6.84745073e-01 4.95962948e-01 -4.43070859e-01 6.63358569e-01 6.97188914e-01 -3.68738174e-01 -3.53860259e-01 1.41334489e-01 2.47213647e-01 -5.25982797e-01 -5.90590179e-01 1.04576099e+00 4.25373435e-01 2.92217255e-01 5.56993604e-01 -1.42499554e+00 -1.57542244e-01 2.81234473e-01 8.14913064e-02 -1.23610683e-01 -3.21997628e-02 8.43564153e-01 5.85138679e-01 -6.97567642e-01 3.78325224e-01 1.23825085e+00 7.72880614e-01 7.81521142e-01 -1.27119815e+00 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c97e2c39-f72a-4df6-9fea-b9bc9f0d574c	panoptic-narrative-grounding-1	null	null	http://openaccess.thecvf.com//content/ICCV2021/html/Gonzalez_Panoptic_Narrative_Grounding_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Gonzalez_Panoptic_Narrative_Grounding_ICCV_2021_paper.pdf	Panoptic Narrative Grounding	This paper proposes Panoptic Narrative Grounding, a spatially fine and general formulation of the natural language visual grounding problem. We establish an experimental framework for the study of this new task, including new ground truth and metrics, and we propose a strong baseline method to serve as stepping stone for future work. We exploit the intrinsic semantic richness in an image by including panoptic categories, and we approach visual grounding at a fine-grained level by using segmentations. In terms of ground truth, we propose an algorithm to automatically transfer Localized Narratives annotations to specific regions in the panoptic segmentations of the MS COCO dataset. To guarantee the quality of our annotations, we take advantage of the semantic structure contained in WordNet to exclusively incorporate noun phrases that are grounded to a meaningfully related panoptic segmentation region. The proposed baseline achieves a performance of 55.4 absolute Average Recall points. This result is a suitable foundation to push the envelope further in the development of methods for Panoptic Narrative Grounding.	['Pablo Arbelaez', 'Jordi Pont-Tuset', 'Jose Hernandez', 'Isabela Hernandez', 'Nicolas Ayobi', 'Cristina Gonzalez']	2021-01-01	null	null	null	iccv-2021-1	['natural-language-visual-grounding']	['reasoning']	[ 2.31077433e-01 3.50487471e-01 -4.93911564e-01 -2.63666868e-01 -6.79538548e-01 -1.02747583e+00 1.11009455e+00 3.79643112e-01 -2.14699090e-01 4.00045604e-01 7.03038871e-01 -1.59297168e-01 1.47751674e-01 -1.01724267e+00 -6.36265337e-01 -4.50536191e-01 2.15718001e-02 3.72101277e-01 3.27539593e-01 -3.24329585e-01 3.80469739e-01 2.16264248e-01 -1.29616964e+00 7.46560514e-01 6.96791649e-01 6.63949430e-01 3.65656734e-01 3.59263152e-01 -3.23672980e-01 5.61063886e-01 -5.16027808e-01 -6.67476416e-01 2.47302771e-01 -5.83588898e-01 -1.01201046e+00 4.93151426e-01 7.90264189e-01 1.92301590e-02 -3.05904865e-01 1.21022916e+00 -5.61243258e-02 2.76633054e-01 6.85568213e-01 -1.26790857e+00 -7.41321564e-01 6.75459921e-01 -7.64419913e-01 1.39855668e-01 2.20100179e-01 7.05433041e-02 1.74490583e+00 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ba42c49d-664e-4acb-9db9-05a1f5ff7fe8	context-aware-automatic-music-transcription	2203.16294	null	https://arxiv.org/abs/2203.16294v2	https://arxiv.org/pdf/2203.16294v2.pdf	Acoustics-specific Piano Velocity Estimation	Motivated by the state-of-art psychological research, we note that a piano performance transcribed with existing Automatic Music Transcription (AMT) methods cannot be successfully resynthesized without affecting the artistic content of the performance. This is due to 1) the different mappings between MIDI parameters used by different instruments, and 2) the fact that musicians adapt their way of playing to the surrounding acoustic environment. To face this issue, we propose a methodology to build acoustics-specific AMT systems that are able to model the adaptations that musicians apply to convey their interpretation. Specifically, we train models tailored for virtual instruments in a modular architecture that takes as input an audio recording and the relative aligned music score, and outputs the acoustics-specific velocities of each note. We test different model shapes and show that the proposed methodology generally outperforms the usual AMT pipeline which does not consider specificities of the instrument and of the acoustic environment. Interestingly, such a methodology is extensible in a straightforward way since only slight efforts are required to train models for the inference of other piano parameters, such as pedaling.	['Federico Avanzini', 'Stavros Ntalampiras', 'Federico Simonetta']	2022-03-30	null	null	null	null	['music-transcription']	['music']	[ 3.35283130e-01 8.65470842e-02 3.98507595e-01 -1.48501888e-01 -5.70225716e-01 -1.02473092e+00 6.38799787e-01 2.59133335e-02 -3.68319035e-01 3.31464231e-01 2.68797874e-01 1.42804623e-01 -3.24098855e-01 -4.35699910e-01 -8.14040124e-01 -5.74968636e-01 2.08753183e-01 7.75936663e-01 2.56171048e-01 -3.90564501e-01 4.75074977e-01 4.52722818e-01 -1.74492097e+00 2.80351728e-01 3.31987619e-01 6.26954734e-01 3.42099249e-01 1.21852708e+00 6.87255859e-02 4.01108295e-01 -1.02003312e+00 -4.84199464e-01 1.72041446e-01 -8.62100542e-01 -5.73346913e-01 -1.28827617e-01 3.61385971e-01 7.38553032e-02 1.03443399e-01 7.91609645e-01 6.27970755e-01 2.88699597e-01 7.35523999e-01 -6.92593515e-01 -1.44888565e-01 1.18186867e+00 9.03200265e-03 -1.10997491e-01 3.07914495e-01 1.32073238e-01 1.06036413e+00 -5.82998633e-01 6.20469093e-01 1.03605294e+00 8.53528142e-01 3.45403135e-01 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1c48b7d5-79ce-459f-a15b-d1b513992cc6	on-the-f-differential-privacy-guarantees-of	2302.09624	null	https://arxiv.org/abs/2302.09624v1	https://arxiv.org/pdf/2302.09624v1.pdf	On the $f$-Differential Privacy Guarantees of Discrete-Valued Mechanisms	We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability. The commonly adopted compression schemes introduce information loss into local data while improving communication efficiency, and it remains an open question whether such discrete-valued mechanisms provide any privacy protection. Considering that differential privacy has become the gold standard for privacy measures due to its simple implementation and rigorous theoretical foundation, in this paper, we study the privacy guarantees of discrete-valued mechanisms with finite output space in the lens of $f$-differential privacy (DP). By interpreting the privacy leakage as a hypothesis testing problem, we derive the closed-form expression of the tradeoff between type I and type II error rates, based on which the $f$-DP guarantees of a variety of discrete-valued mechanisms, including binomial mechanisms, sign-based methods, and ternary-based compressors, are characterized. We further investigate the Byzantine resilience of binomial mechanisms and ternary compressors and characterize the tradeoff among differential privacy, Byzantine resilience, and communication efficiency. Finally, we discuss the application of the proposed method to differentially private stochastic gradient descent in federated learning.	['Huaiyu Dai', 'Tony Quek', 'Zhaoyang Zhang', 'Caijun Zhong', 'Zhonggen Su', 'Richeng Jin']	2023-02-19	null	null	null	null	['open-question']	['natural-language-processing']	[-4.99164201e-02 -1.76996961e-01 -1.27053082e-01 -4.36145246e-01 -7.32603848e-01 -8.10257494e-01 1.90202683e-01 5.31427264e-01 -6.48192286e-01 5.44555008e-01 1.80585951e-01 -5.41458130e-01 -4.00625169e-01 -9.46051180e-01 -7.03768432e-01 -1.00378418e+00 -6.17446184e-01 -2.62795895e-01 -4.07925904e-01 -3.15380022e-02 1.67428330e-01 5.93417525e-01 -1.24248397e+00 1.84756681e-01 7.24020243e-01 1.41119969e+00 -4.18464929e-01 4.64679360e-01 -2.48798560e-02 5.56454480e-01 -6.71656013e-01 -9.96885836e-01 8.61888111e-01 -4.68777269e-01 -5.53697586e-01 -3.55363309e-01 1.86704963e-01 -4.82754529e-01 -6.80143595e-01 1.29844105e+00 5.01225650e-01 -5.22127822e-02 2.61502862e-01 -1.52733076e+00 -4.67028677e-01 7.51786292e-01 -3.92021567e-01 2.71900333e-02 1.23096950e-01 -1.10015966e-01 9.84619200e-01 -4.74433273e-01 4.54521000e-01 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1.37286222e+00 1.53515548e-01 -1.64385331e+00 -1.02182642e-01 -1.14083374e+00 3.88463140e-01 4.83546168e-01 2.21501544e-01 -8.80040705e-01 -6.10480011e-01]	[5.908627033233643, 6.59383487701416]
49cb9884-1633-45fb-9557-6c266c4031bc	towards-discovering-the-effectiveness-of	null	null	http://openaccess.thecvf.com//content/CVPR2022/html/Tang_Towards_Discovering_the_Effectiveness_of_Moderately_Confident_Samples_for_Semi-Supervised_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Tang_Towards_Discovering_the_Effectiveness_of_Moderately_Confident_Samples_for_Semi-Supervised_CVPR_2022_paper.pdf	Towards Discovering the Effectiveness of Moderately Confident Samples for Semi-Supervised Learning	Semi-supervised learning (SSL) has been studied for a long time to solve vision tasks in data-efficient application scenarios. SSL aims to learn a good classification model using a few labeled data together with large-scale unlabeled data. Recent advances achieve the goal by combining multiple SSL techniques, e.g., self-training and consistency regularization. From unlabeled samples, they usually adopt a confidence filter (CF) to select reliable ones with high prediction confidence. In this work, we study whether the moderately confident samples are useless and how to select the useful ones to improve model optimization. To answer these problems, we propose a novel Taylor expansion inspired filtration (TEIF) framework, which admits the samples of moderate confidence with similar feature or gradient to the respective one averaged over the labeled and highly confident unlabeled data. It can produce a stable and new information induced network update, leading to better generalization. Two novel filters are derived from this framework and can be naturally explained in two perspectives. One is gradient synchronization filter (GSF), which strengthens the optimization dynamic of fully-supervised learning; it selects the samples whose gradients are similar to class-wise majority gradients. The other is prototype proximity filter (PPF), which involves more prototypical samples in training to learn better semantic representations; it selects the samples near class-wise prototypes. They can be integrated into SSL methods with CF. We use the state-of-the-art FixMatch as the baseline. Experiments on popular SSL benchmarks show that we achieve the new state of the art.	['Kui Jia', 'Hui Tang']	2022-01-01	null	null	null	cvpr-2022-1	['semi-supervised-image-classification']	['computer-vision']	[-1.47994235e-02 -2.42366325e-02 -4.91848856e-01 -7.24989831e-01 -4.69132572e-01 1.41868768e-02 5.76275826e-01 2.67492354e-01 -5.42318881e-01 8.75283778e-01 9.94636491e-02 3.70888561e-01 -2.96459258e-01 -5.54053664e-01 -7.16820240e-01 -9.61029708e-01 2.07315102e-01 4.69628066e-01 6.19176269e-01 -1.00615598e-01 3.19610447e-01 2.54869342e-01 -1.69580114e+00 1.88984916e-01 1.33900130e+00 1.28421795e+00 4.60063696e-01 -1.64683983e-01 -3.61964434e-01 8.76532435e-01 -1.76778868e-01 1.32694095e-02 2.16515958e-01 -3.41774344e-01 -6.63562596e-01 1.33493587e-01 4.73864973e-01 1.04797579e-01 7.33837783e-02 1.32957613e+00 3.27764362e-01 1.77326798e-01 6.63444817e-01 -1.15005052e+00 -7.39789128e-01 6.30538225e-01 -6.07182562e-01 4.86238487e-02 -7.42666647e-02 -7.63612837e-02 6.11550152e-01 -1.29169643e+00 6.44761920e-01 1.39870930e+00 6.33932531e-01 6.98200881e-01 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c44b9681-a602-49af-b99d-8a57dc76e8a0	variational-leakage-the-role-of-information	2106.02818	null	https://arxiv.org/abs/2106.02818v2	https://arxiv.org/pdf/2106.02818v2.pdf	Variational Leakage: The Role of Information Complexity in Privacy Leakage	We study the role of information complexity in privacy leakage about an attribute of an adversary's interest, which is not known a priori to the system designer. Considering the supervised representation learning setup and using neural networks to parameterize the variational bounds of information quantities, we study the impact of the following factors on the amount of information leakage: information complexity regularizer weight, latent space dimension, the cardinalities of the known utility and unknown sensitive attribute sets, the correlation between utility and sensitive attributes, and a potential bias in a sensitive attribute of adversary's interest. We conduct extensive experiments on Colored-MNIST and CelebA datasets to evaluate the effect of information complexity on the amount of intrinsic leakage.	['Slava Voloshynovskiy', 'Deniz Gündüz', 'Behrooz Razeghi', 'Amir Ahooye Atashin']	2021-06-05	null	null	null	null	['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning']	['methodology', 'natural-language-processing']	[ 2.86236405e-01 1.10485375e-01 -3.02926511e-01 -3.35422724e-01 -8.64173114e-01 -8.55439126e-01 3.09608519e-01 5.80279171e-01 -7.17758656e-01 4.69001770e-01 3.59908074e-01 -4.84286934e-01 -2.72259861e-01 -9.31266546e-01 -6.54655337e-01 -1.02873051e+00 -2.63975173e-01 9.46239829e-02 -1.91125631e-01 1.30908087e-01 -4.94457781e-02 4.21787173e-01 -7.42453218e-01 1.69418931e-01 5.79335093e-01 1.14692867e+00 -5.39931536e-01 2.16097236e-01 3.18582058e-01 5.54715514e-01 -5.39932966e-01 -6.28139257e-01 8.54024291e-01 -2.45665386e-01 -6.12403333e-01 1.03369042e-01 -4.06251252e-02 -2.45845169e-01 -5.38693964e-01 1.49378467e+00 4.76612806e-01 -2.61949711e-02 7.03127742e-01 -1.11687338e+00 -4.35621977e-01 8.80883217e-01 -3.75458688e-01 4.11305130e-01 -1.44671276e-01 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8fd554a2-25b7-4819-a75c-99ab5747a7b5	vietnamese-capitalization-and-punctuation	2207.01312	null	https://arxiv.org/abs/2207.01312v1	https://arxiv.org/pdf/2207.01312v1.pdf	Vietnamese Capitalization and Punctuation Recovery Models	Despite the rise of recent performant methods in Automatic Speech Recognition (ASR), such methods do not ensure proper casing and punctuation for their outputs. This problem has a significant impact on the comprehension of both Natural Language Processing (NLP) algorithms and human to process. Capitalization and punctuation restoration is imperative in pre-processing pipelines for raw textual inputs. For low resource languages like Vietnamese, public datasets for this task are scarce. In this paper, we contribute a public dataset for capitalization and punctuation recovery for Vietnamese; and propose a joint model for both tasks named JointCapPunc. Experimental results on the Vietnamese dataset show the effectiveness of our joint model compare to single model and previous joint learning model. We publicly release our dataset and the implementation of our model at https://github.com/anhtunguyen98/JointCapPunc	['Ta Duc Huy', 'Nguyen Anh Tu', 'Hoang Thi Thu Uyen']	2022-07-04	null	null	null	null	['punctuation-restoration']	['natural-language-processing']	[ 2.06060022e-01 -2.46178564e-02 1.06756583e-01 -3.70586544e-01 -1.34563470e+00 -8.41474354e-01 6.22399807e-01 2.14012653e-01 -7.93127358e-01 6.81214452e-01 6.89579010e-01 -6.66293979e-01 5.02597988e-01 -2.55855411e-01 -7.81273425e-01 -2.81859875e-01 4.91227120e-01 3.99136871e-01 8.50988179e-02 -1.49936408e-01 2.53827512e-01 3.71638477e-01 -8.46689105e-01 6.96739793e-01 8.42426836e-01 4.08199698e-01 3.98763925e-01 9.24195826e-01 -2.20629454e-01 7.71743357e-01 -9.26276624e-01 -6.85037076e-01 2.24241272e-01 -1.54282600e-01 -1.15274644e+00 -1.56649724e-01 1.47629991e-01 -7.67171830e-02 -3.53550941e-01 1.07514274e+00 5.75639188e-01 1.06537178e-01 5.78367829e-01 -9.37069595e-01 -8.47461581e-01 1.34390330e+00 -2.57756144e-01 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77e64891-53b2-4f4f-aafd-36f2cacf4316	supervised-learning-and-anti-learning-of	1307.1599	null	http://arxiv.org/abs/1307.1599v1	http://arxiv.org/pdf/1307.1599v1.pdf	Supervised Learning and Anti-learning of Colorectal Cancer Classes and Survival Rates from Cellular Biology Parameters	In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. Attempts are made to learn relationships between attributes (physical and immunological) and the resulting tumour stage and survival. Results for conventional machine learning approaches can be considered poor, especially for predicting tumour stages for the most important types of cancer. This poor performance is further investigated and compared with a synthetic, dataset based on the logical exclusive-OR function and it is shown that there is a significant level of 'anti-learning' present in all supervised methods used and this can be explained by the highly dimensional, complex and sparsely representative dataset. For predicting the stage of cancer from the immunological attributes, anti-learning approaches outperform a range of popular algorithms.	['Lindy Durrant', 'John Scholefield', 'Guoping Qiu', 'Uwe Aickelin', 'Chris Roadknight']	2013-07-05	null	null	null	null	['tumour-classification']	['medical']	[ 4.59837765e-01 8.02017301e-02 -5.81795692e-01 -1.22098401e-01 -4.42167044e-01 -1.30764604e-01 8.11515570e-01 7.94680536e-01 -4.51002836e-01 1.11272264e+00 2.80586451e-01 -3.90556902e-01 -6.88971341e-01 -6.82012856e-01 -1.15678415e-01 -1.14238036e+00 -4.06869829e-01 7.60749400e-01 -2.35284597e-01 -2.63222635e-01 2.74688035e-01 7.60784030e-01 -1.38585126e+00 4.99506146e-01 3.95487666e-01 8.58087003e-01 -9.30826291e-02 1.03550875e+00 -1.97639808e-01 7.98733413e-01 -5.51424265e-01 2.75065117e-02 -8.89528766e-02 -4.09410655e-01 -7.53386438e-01 -1.45597339e-01 -3.20704162e-01 4.09082741e-01 -1.53273687e-01 3.96306336e-01 4.56304818e-01 -4.77891654e-01 1.29157901e+00 -9.86444890e-01 4.51673120e-02 -1.49117440e-01 -3.27271342e-01 4.28768694e-02 6.52615964e-01 -1.12999678e-01 5.36735535e-01 -5.34443974e-01 5.14034986e-01 8.98532510e-01 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84c100bc-db3c-4694-8d61-5c32a2811b55	a-brief-review-of-real-world-color-image	1809.03298	null	http://arxiv.org/abs/1809.03298v1	http://arxiv.org/pdf/1809.03298v1.pdf	A Brief Review of Real-World Color Image Denoising	Filtering real-world color images is challenging due to the complexity of noise that can not be formulated as a certain distribution. However, the rapid development of camera lens pos- es greater demands on image denoising in terms of both efficiency and effectiveness. Currently, the most widely accepted framework employs the combination of transform domain techniques and nonlocal similarity characteristics of natural images. Based on this framework, many competitive methods model the correlation of R, G, B channels with pre-defined or adaptively learned transforms. In this chapter, a brief review of related methods and publicly available datasets is presented, moreover, a new dataset that includes more natural outdoor scenes is introduced. Extensive experiments are performed and discussion on visual effect enhancement is included.	['Zhaoming Kong', 'Xiaowei Yang']	2018-09-10	null	null	null	null	['color-image-denoising']	['computer-vision']	[ 3.87322396e-01 -1.02332830e+00 2.33217180e-01 -4.14221019e-01 -5.86510122e-01 -3.47628444e-01 1.96592540e-01 -2.14301750e-01 -5.66722989e-01 7.24903643e-01 -8.21831226e-02 2.78400421e-01 -3.18442196e-01 -6.10789061e-01 -2.58553773e-01 -1.07851994e+00 1.22294411e-01 -5.01258194e-01 3.79504859e-01 -3.21037620e-01 3.89905602e-01 4.85127658e-01 -1.56839621e+00 2.32159987e-01 1.18100405e+00 1.11572123e+00 4.50076520e-01 6.24952435e-01 6.26335517e-02 4.73949641e-01 -4.73484039e-01 -5.17898262e-01 5.70192754e-01 -7.54783809e-01 -2.04917982e-01 3.02306712e-01 5.24735153e-01 -4.24057931e-01 -4.56124574e-01 1.57843530e+00 8.00590277e-01 3.73865604e-01 6.50258780e-01 -9.01624680e-01 -8.58951807e-01 7.71903172e-02 -6.16030395e-01 4.91276532e-01 1.08452879e-01 -1.17682487e-01 6.18627310e-01 -9.14910555e-01 3.55130017e-01 1.05434549e+00 7.70727038e-01 2.08697721e-01 -1.16947603e+00 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79f3f876-013f-48b4-93cb-3a7aef3faf03	carl-d-a-vision-benchmark-suite-and-large	null	null	https://www.sciencedirect.com/science/article/abs/pii/S0923596522000224	https://www.sciencedirect.com/science/article/abs/pii/S0923596522000224	CARL-D: A vision benchmark suite and large scale dataset for vehicle detection and scene segmentation	Vision-based object detection and scene understanding are becoming key features of environment perception and autonomous driving. In the past couple of years, numerous large-scale datasets for visual object detection and semantic understanding have been released which has enormously benefited the environment perception in self-driving cars. However, these datasets like Kitti and Cityscapes only focus on well-organized and urban road traffic scenarios of European countries while ignoring the dense and unpattern traffic conditions of sub-continent countries like Pakistan, India, Bangladesh, and Sri Lanka. Consequently, the environment perception system developed on these datasets cannot efficiently assist self-driving cars in traffic scenarios of sub-continent countries. To this end, we present CARL-D, a large-scale dataset and benchmark suite for developing 2D object detection and instance/pixel-level segmentation methods for self-driving cars. CARL-D comprises large-scale stereo vision-based driving videos captured from more than 100 cities of Pakistan, including motorways, dense, and unpattern traffic scenarios of urban, rural, and hilly areas. As a benchmark selection, 15,000 suitable images are labeled for 2D object detection and recognition. Whereas the semantic segmentation benchmark contains 2500 images with pixel-level high-quality fine annotations and 5000 coarse-annotated images which could help in enabling the deep neural networks to leverage the weakly labeled data. Alongside the dataset, we also present transfer learning-based 2D vehicle detection and scene segmentation methods to evaluate the performance of existing state-of-the-art deep neural networks on our dataset. Lastly, an extensive experimental evaluation along with the comparative study has been carried out, demonstrating the upper edge of our dataset in terms of interclass diversity, scene variability, and annotations richness. The proposed benchmark suite is available at https://carl-dataset.github.io/index/.	['Faisal Riaz', 'Muhammad Atif Butt']	2022-02-17	null	null	null	signal-processing-image-communication-2022-2	['scene-segmentation']	['computer-vision']	[ 1.29041644e-02 -2.65578777e-01 -2.39095002e-01 -4.11179185e-01 -4.19627696e-01 -4.50576663e-01 6.62180841e-01 -2.84602493e-01 -4.39396799e-01 3.98891002e-01 -3.38431239e-01 -6.48798466e-01 3.76596987e-01 -9.89198565e-01 -6.46284461e-01 -5.18898129e-01 -4.19007391e-02 4.13951039e-01 6.50339842e-01 -5.34602940e-01 7.66132548e-02 6.14070714e-01 -1.97465014e+00 2.34396048e-02 9.68625486e-01 1.01773059e+00 6.52250171e-01 5.92881560e-01 -1.38789669e-01 3.32360744e-01 -1.30059078e-01 -3.34966272e-01 6.56195641e-01 2.17475489e-01 -3.34582329e-01 2.99669802e-01 8.15518081e-01 -4.23407316e-01 -5.52908003e-01 1.16364169e+00 3.98950607e-01 3.68320234e-02 6.64454401e-01 -1.48989487e+00 -6.04453981e-01 -1.21954970e-01 -6.25871181e-01 4.80743945e-01 -6.66083395e-02 6.42249405e-01 5.40935814e-01 -9.62339342e-01 3.91428590e-01 1.32244146e+00 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6e76abba-6046-4c3c-8f73-9c9c41309653	domain-sensitive-temporal-tagging-by-jannik	null	null	https://aclanthology.org/J18-2006	https://aclanthology.org/J18-2006.pdf	Domain-Sensitive Temporal Tagging By Jannik Str\"otgen, Michael Gertz	null	['Ruihong Huang']	2018-06-01	null	null	null	cl-2018-6	['temporal-tagging']	['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 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 -2.09758401e-01 9.22211111e-01 5.37373662e-01 4.49454933e-02 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118938aa-fa68-4769-a05e-d49311109d99	generative-scene-graph-networks	null	null	https://openreview.net/forum?id=RmcPm9m3tnk	https://openreview.net/pdf?id=RmcPm9m3tnk	Generative Scene Graph Networks	Human perception excels at building compositional hierarchies of parts and objects from unlabeled scenes that help systematic generalization. Yet most work on generative scene modeling either ignores the part-whole relationship or assumes access to predefined parts labels. In this paper, we propose Generative Scene Graph Networks (GSGNs), the first deep generative model that learns to discover the primitive parts and infer the part-whole relationship jointly from multi-object scenes without supervision and in an end-to-end trainable way. We formulate GSGN as a variational autoencoder in which the latent representation is a tree-structured probabilistic scene graph. The leaf nodes in the latent tree correspond to primitive parts, and the edges represent the symbolic pose variables required for recursively composing the parts into whole objects and then the full scene. This allows novel objects and scenes to be generated both by sampling from the prior and by manual configuration of the pose variables, as we do with graphics engines. We evaluate GSGN on datasets of scenes containing multiple compositional objects, including a challenging compositional CLEVR dataset that we have developed. We show that GSGN is able to infer the latent scene graph, generalize out of the training regime, and improve data efficiency in downstream tasks.	['Sungjin Ahn', 'Donghun Lee', 'Zhuo Zhi', 'Fei Deng']	2021-01-01	null	null	null	iclr-2021-1	['systematic-generalization']	['reasoning']	[ 2.68462628e-01 5.20418823e-01 2.56757081e-01 -5.52866399e-01 -3.45132083e-01 -7.34865904e-01 9.00018215e-01 -1.91272944e-01 7.09539577e-02 3.76645893e-01 1.45850018e-01 -6.30263537e-02 -6.24874420e-02 -1.07500494e+00 -1.10732913e+00 -7.83893108e-01 2.03137144e-01 1.20874286e+00 2.68656939e-01 5.16914390e-02 -1.31418377e-01 5.56507170e-01 -1.76384997e+00 4.20322001e-01 4.79227960e-01 6.50382042e-01 4.85863209e-01 8.01136196e-01 -1.96507230e-01 8.35752904e-01 -4.10047233e-01 -5.09692907e-01 2.78961807e-01 -4.14509416e-01 -8.25239480e-01 7.16066957e-01 7.23708153e-01 -4.13542271e-01 -2.64034063e-01 8.13979805e-01 2.02106517e-02 2.06596211e-01 8.33092511e-01 -1.37746477e+00 -7.88200676e-01 8.35379064e-01 -1.11426301e-01 -3.22159111e-01 -7.40670338e-02 1.63083106e-01 1.25025463e+00 -7.29949951e-01 9.52387393e-01 1.41185796e+00 3.55681449e-01 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c83fd9a9-3beb-4cea-8795-3dbac6645cdc	airloop-lifelong-loop-closure-detection	2109.08975	null	https://arxiv.org/abs/2109.08975v3	https://arxiv.org/pdf/2109.08975v3.pdf	AirLoop: Lifelong Loop Closure Detection	Loop closure detection is an important building block that ensures the accuracy and robustness of simultaneous localization and mapping (SLAM) systems. Due to their generalization ability, CNN-based approaches have received increasing attention. Although they normally benefit from training on datasets that are diverse and reflective of the environments, new environments often emerge after the model is deployed. It is therefore desirable to incorporate the data newly collected during operation for incremental learning. Nevertheless, simply finetuning the model on new data is infeasible since it may cause the model's performance on previously learned data to degrade over time, which is also known as the problem of catastrophic forgetting. In this paper, we present AirLoop, a method that leverages techniques from lifelong learning to minimize forgetting when training loop closure detection models incrementally. We experimentally demonstrate the effectiveness of AirLoop on TartanAir, Nordland, and RobotCar datasets. To the best of our knowledge, AirLoop is one of the first works to achieve lifelong learning of deep loop closure detectors.	['Sebastian Scherer', 'Chen Wang', 'Dasong Gao']	2021-09-18	null	null	null	null	['loop-closure-detection']	['computer-vision']	[-5.10906167e-02 -1.81954309e-01 -2.03158021e-01 -4.34591204e-01 -3.87294948e-01 -4.49060857e-01 4.58284348e-01 4.27960515e-01 -6.39333963e-01 6.52432740e-01 -7.48369545e-02 -3.74703288e-01 -1.60463657e-02 -5.82548916e-01 -1.12301004e+00 -2.93506682e-01 -2.94973582e-01 2.52071738e-01 4.33900535e-01 -3.31549227e-01 3.66186708e-01 6.88819051e-01 -1.60560310e+00 -2.80071706e-01 9.38614011e-01 7.68550694e-01 5.31179667e-01 4.86673772e-01 2.11151410e-02 7.66843736e-01 -3.06697875e-01 2.19043747e-01 2.63101131e-01 -2.30216049e-03 -5.26516616e-01 -2.21188292e-01 3.62395763e-01 -4.72633541e-01 -5.87886691e-01 7.13467717e-01 2.69407302e-01 3.83759618e-01 1.17566481e-01 -1.26253784e+00 -1.89983755e-01 3.59488547e-01 -3.23157132e-01 2.40778267e-01 1.59142941e-01 2.22352117e-01 5.94913006e-01 -1.03034866e+00 5.34251690e-01 8.49812210e-01 1.14385414e+00 3.61510336e-01 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6fdbd961-49cb-43f3-ac55-be9ddeb1700a	a-diversity-promoting-objective-function-for	1510.03055	null	http://arxiv.org/abs/1510.03055v3	http://arxiv.org/pdf/1510.03055v3.pdf	A Diversity-Promoting Objective Function for Neural Conversation Models	Sequence-to-sequence neural network models for generation of conversational responses tend to generate safe, commonplace responses (e.g., "I don't know") regardless of the input. We suggest that the traditional objective function, i.e., the likelihood of output (response) given input (message) is unsuited to response generation tasks. Instead we propose using Maximum Mutual Information (MMI) as the objective function in neural models. Experimental results demonstrate that the proposed MMI models produce more diverse, interesting, and appropriate responses, yielding substantive gains in BLEU scores on two conversational datasets and in human evaluations.	['Chris Brockett', 'Jianfeng Gao', 'Michel Galley', 'Jiwei Li', 'Bill Dolan']	2015-10-11	a-diversity-promoting-objective-function-for-1	https://aclanthology.org/N16-1014	https://aclanthology.org/N16-1014.pdf	naacl-2016-6	['conversational-response-generation']	['natural-language-processing']	[ 4.12331611e-01 3.86006743e-01 5.01208790e-02 -9.50100899e-01 -7.84155786e-01 -4.32429612e-01 7.20366776e-01 -1.34265020e-01 -3.23726565e-01 1.31584775e+00 6.56527400e-01 -4.56990302e-01 8.27616081e-02 -8.24605107e-01 -2.91447312e-01 -3.50938529e-01 6.28688276e-01 4.39526320e-01 -4.30478752e-01 -5.54834008e-01 5.66220820e-01 -7.26223141e-02 -1.07288504e+00 7.94577658e-01 8.81568253e-01 6.29818022e-01 1.55952334e-01 9.45521474e-01 -4.40597713e-01 1.14364636e+00 -1.09726477e+00 -8.03292990e-01 -2.11418942e-01 -1.21016204e+00 -1.38698614e+00 -3.72308344e-01 -1.02609478e-01 -3.26511115e-01 -4.22676325e-01 7.51708150e-01 5.36665201e-01 7.78503418e-01 9.32972372e-01 -1.19864058e+00 -1.07808948e+00 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b7b70597-40b9-4a31-8f0c-c9c3cc3d4b8f	mind-the-retrosynthesis-gap-bridging-the	2212.11809	null	https://arxiv.org/abs/2212.11809v1	https://arxiv.org/pdf/2212.11809v1.pdf	Mind the Retrosynthesis Gap: Bridging the divide between Single-step and Multi-step Retrosynthesis Prediction	Retrosynthesis is the task of breaking down a chemical compound recursively step-by-step into molecular precursors until a set of commercially available molecules is found. Consequently, the goal is to provide a valid synthesis route for a molecule. As more single-step models develop, we see increasing accuracy in the prediction of molecular disconnections, potentially improving the creation of synthetic paths. Multi-step approaches repeatedly apply the chemical information stored in single-step retrosynthesis models. However, this connection is not reflected in contemporary research, fixing either the single-step model or the multi-step algorithm in the process. In this work, we establish a bridge between both tasks by benchmarking the performance and transfer of different single-step retrosynthesis models to the multi-step domain by leveraging two common search algorithms, Monte Carlo Tree Search and Retro*. We show that models designed for single-step retrosynthesis, when extended to multi-step, can have a tremendous impact on the route finding capabilities of current multi-step methods, improving performance by up to +30% compared to the most widely used model. Furthermore, we observe no clear link between contemporary single-step and multi-step evaluation metrics, showing that single-step models need to be developed and tested for the multi-step domain and not as an isolated task to find synthesis routes for molecules of interest.	['Igor Tetko', 'Mike Preuss', 'Jonas Verhoeven', 'Samuel Genheden', 'Paula Torren-Peraire', 'Alan Kai Hassen']	2022-12-12	null	null	null	null	['retrosynthesis']	['medical']	[ 4.99505311e-01 -2.12211117e-01 -4.53061491e-01 2.48442873e-01 -5.44850111e-01 -1.38753617e+00 7.30347097e-01 7.54327893e-01 -4.45760250e-01 9.62693632e-01 -1.60423890e-01 -7.24549472e-01 -1.13524571e-02 -1.00764716e+00 -5.89802921e-01 -5.09439766e-01 -4.24600281e-02 5.57831228e-01 5.68939984e-01 -4.68450904e-01 6.03770077e-01 9.92982745e-01 -1.31989861e+00 1.34784505e-01 7.78854966e-01 5.83136141e-01 2.30996668e-01 6.34040475e-01 -1.92016378e-01 1.60342753e-01 -4.10314202e-01 -4.99223322e-01 2.84411937e-01 -7.50809193e-01 -8.54338765e-01 -6.24281406e-01 1.64276883e-02 2.63440818e-01 3.52571279e-01 7.74719000e-01 5.61922431e-01 2.49391213e-01 6.16162539e-01 -8.12615573e-01 1.80137500e-01 7.62304902e-01 -1.33655325e-01 -3.84345055e-02 5.93417108e-01 1.77089274e-01 1.06248724e+00 -9.47959542e-01 9.97702539e-01 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a040f8cb-8cf8-4ebf-89dc-172beeb62737	productgraphsleepnet-sleep-staging-using	2212.04881	null	https://arxiv.org/abs/2212.04881v1	https://arxiv.org/pdf/2212.04881v1.pdf	ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregation	The classification of sleep stages plays a crucial role in understanding and diagnosing sleep pathophysiology. Sleep stage scoring relies heavily on visual inspection by an expert that is time consuming and subjective procedure. Recently, deep learning neural network approaches have been leveraged to develop a generalized automated sleep staging and account for shifts in distributions that may be caused by inherent inter/intra-subject variability, heterogeneity across datasets, and different recording environments. However, these networks ignore the connections among brain regions, and disregard the sequential connections between temporally adjacent sleep epochs. To address these issues, this work proposes an adaptive product graph learning-based graph convolutional network, named ProductGraphSleepNet, for learning joint spatio-temporal graphs along with a bidirectional gated recurrent unit and a modified graph attention network to capture the attentive dynamics of sleep stage transitions. Evaluation on two public databases: the Montreal Archive of Sleep Studies (MASS) SS3; and the SleepEDF, which contain full night polysomnography recordings of 62 and 20 healthy subjects, respectively, demonstrates performance comparable to the state-of-the-art (Accuracy: 0.867;0.838, F1-score: 0.818;0.774 and Kappa: 0.802;0.775, on each database respectively). More importantly, the proposed network makes it possible for clinicians to comprehend and interpret the learned connectivity graphs for sleep stages.	['Gari Clifford', 'Sepideh Hajipour Sardouie', 'Samaneh Nasiri', 'Aref Einizade']	2022-12-09	null	null	null	null	['sleep-staging']	['medical']	[-1.94994017e-01 -1.19218148e-01 -7.83809349e-02 -4.34300363e-01 1.67903025e-02 -4.63642627e-01 8.19323212e-02 2.25125179e-01 -5.17319560e-01 7.00171173e-01 1.48755118e-01 -3.08035284e-01 -2.57279873e-01 -2.86237061e-01 -4.14300524e-02 -5.26877105e-01 -5.58351398e-01 3.26723248e-01 2.62249351e-01 -5.98881245e-02 -4.02476713e-02 2.73987949e-01 -1.13692486e+00 -7.65661523e-02 9.71713841e-01 9.84098852e-01 1.69646785e-01 7.03522325e-01 1.31055862e-01 5.15622556e-01 -5.68937421e-01 -2.49730781e-01 -7.26207495e-02 -6.13225162e-01 -4.99508828e-01 3.93019877e-02 1.26212627e-01 2.34997034e-01 -4.77140158e-01 1.13848519e+00 4.96508032e-01 4.15129662e-01 5.43693066e-01 -1.16927040e+00 -7.40674555e-01 1.90234080e-01 -4.88433212e-01 1.29140437e+00 1.02686167e-01 2.61626810e-01 1.06195116e+00 -2.96699256e-01 1.64746851e-01 5.35121202e-01 8.60499263e-01 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3902424a-db7d-44bc-8e8f-5bc331caa0ce	ecnu-a-combination-method-and-multiple	null	null	https://aclanthology.org/S14-2041	https://aclanthology.org/S14-2041.pdf	ECNU: A Combination Method and Multiple Features for Aspect Extraction and Sentiment Polarity Classification	null	['Zhihua Zhang', 'Man Lan', 'Fangxi Zhang']	2014-08-01	null	null	null	semeval-2014-8	['aspect-extraction']	['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 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 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bdd770e8-b49f-4237-9cf8-dac19943e9bc	medical-supervised-masked-autoencoders	2305.05871	null	https://arxiv.org/abs/2305.05871v1	https://arxiv.org/pdf/2305.05871v1.pdf	Medical supervised masked autoencoders: Crafting a better masking strategy and efficient fine-tuning schedule for medical image classification	Masked autoencoders (MAEs) have displayed significant potential in the classification and semantic segmentation of medical images in the last year. Due to the high similarity of human tissues, even slight changes in medical images may represent diseased tissues, necessitating fine-grained inspection to pinpoint diseased tissues. The random masking strategy of MAEs is likely to result in areas of lesions being overlooked by the model. At the same time, inconsistencies between the pre-training and fine-tuning phases impede the performance and efficiency of MAE in medical image classification. To address these issues, we propose a medical supervised masked autoencoder (MSMAE) in this paper. In the pre-training phase, MSMAE precisely masks medical images via the attention maps obtained from supervised training, contributing to the representation learning of human tissue in the lesion area. During the fine-tuning phase, MSMAE is also driven by attention to the accurate masking of medical images. This improves the computational efficiency of the MSMAE while increasing the difficulty of fine-tuning, which indirectly improves the quality of MSMAE medical diagnosis. Extensive experiments demonstrate that MSMAE achieves state-of-the-art performance in case with three official medical datasets for various diseases. Meanwhile, transfer learning for MSMAE also demonstrates the great potential of our approach for medical semantic segmentation tasks. Moreover, the MSMAE accelerates the inference time in the fine-tuning phase by 11.2% and reduces the number of floating-point operations (FLOPs) by 74.08% compared to a traditional MAE.	['Binling Nie', 'Xuesong Yin', 'Yuanqi Chang', 'Shujian Guo', 'Jiawei Mao']	2023-05-10	null	null	null	null	['medical-diagnosis']	['medical']	[ 4.91158962e-01 2.82314181e-01 2.77898461e-01 -1.72614560e-01 -5.00161052e-01 -1.45332366e-02 2.30812848e-01 4.67505716e-02 -6.61734402e-01 4.11978543e-01 -8.70115981e-02 -1.36040434e-01 -1.18817426e-01 -9.99906123e-01 -6.69151902e-01 -9.26971436e-01 2.19164476e-01 3.89763594e-01 3.34894866e-01 -2.25519165e-02 -1.08065158e-01 3.28617901e-01 -1.33651721e+00 5.05449653e-01 1.10434854e+00 9.58387554e-01 6.60205543e-01 4.77979034e-01 -2.29502797e-01 5.08475602e-01 -7.17691898e-01 -4.00227338e-01 5.04767634e-02 -1.86189547e-01 -8.31859946e-01 3.81513566e-01 9.02883802e-03 -2.65697479e-01 -2.27177501e-01 1.32555699e+00 4.56426054e-01 -7.36068636e-02 6.25721812e-01 -8.52180064e-01 -4.69527185e-01 5.06631911e-01 -7.24579990e-01 2.04036817e-01 -4.80170190e-01 1.98401451e-01 5.16928196e-01 -6.94083154e-01 3.03815991e-01 9.79201078e-01 7.22134113e-01 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30a1362f-962c-48f6-9730-36e68bec04e9	tollywood-emotions-annotation-of-valence	2303.09364	null	https://arxiv.org/abs/2303.09364v1	https://arxiv.org/pdf/2303.09364v1.pdf	Tollywood Emotions: Annotation of Valence-Arousal in Telugu Song Lyrics	Emotion recognition from a given music track has heavily relied on acoustic features, social tags, and metadata but is seldom focused on lyrics. There are no datasets of Indian language songs that contain both valence and arousal manual ratings of lyrics. We present a new manually annotated dataset of Telugu songs' lyrics collected from Spotify with valence and arousal annotated on a discrete scale. A fairly high inter-annotator agreement was observed for both valence and arousal. Subsequently, we create two music emotion recognition models by using two classification techniques to identify valence, arousal and respective emotion quadrant from lyrics. Support vector machine (SVM) with term frequency-inverse document frequency (TF-IDF) features and fine-tuning the pre-trained XLMRoBERTa (XLM-R) model were used for valence, arousal and quadrant classification tasks. Fine-tuned XLMRoBERTa performs better than the SVM by improving macro-averaged F1-scores of 54.69%, 67.61%, 34.13% to 77.90%, 80.71% and 58.33% for valence, arousal and quadrant classifications, respectively, on 10-fold cross-validation. In addition, we compare our lyrics annotations with Spotify's annotations of valence and energy (same as arousal), which are based on entire music tracks. The implications of our findings are discussed. Finally, we make the dataset publicly available with lyrics, annotations and Spotify IDs.	['Vinoo Alluri', 'BV Koushik', 'B Manikanta Gupta', 'R Guru Ravi Shanker']	2023-03-16	null	null	null	null	['music-emotion-recognition', 'xlm-r']	['music', 'natural-language-processing']	[ 4.80632298e-02 -4.61850345e-01 -6.53486624e-02 -4.59978580e-01 -1.04347873e+00 -1.21320975e+00 3.11606705e-01 1.27755404e-01 -1.92883492e-01 5.64680099e-01 4.84441787e-01 4.92599398e-01 -2.17108339e-01 -2.66262621e-01 -6.76445812e-02 -7.38070071e-01 -2.43829079e-02 9.76365954e-02 -4.17963654e-01 -1.06058121e-01 4.61948156e-01 3.59562822e-02 -1.89673352e+00 6.75907791e-01 2.49901444e-01 1.52788830e+00 -8.18597823e-02 9.62607145e-01 3.78955603e-02 3.85913163e-01 -8.78852844e-01 -2.06972986e-01 -5.56117706e-02 -5.36836982e-01 -5.36905348e-01 -3.47419828e-01 3.69429737e-01 5.92364490e-01 3.29132557e-01 6.46461368e-01 7.08287001e-01 1.76281899e-01 7.62721956e-01 -1.18828201e+00 -4.28941995e-01 6.85999036e-01 -5.42564273e-01 2.27662474e-01 5.57611525e-01 -1.88251868e-01 1.45876122e+00 -7.86632776e-01 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a88440f7-1e0a-4daa-8f1d-e9c76bbe1cf9	deep-learning-based-automatic-detection-of	2009.13580	null	https://arxiv.org/abs/2009.13580v1	https://arxiv.org/pdf/2009.13580v1.pdf	Deep Learning-Based Automatic Detection of Poorly Positioned Mammograms to Minimize Patient Return Visits for Repeat Imaging: A Real-World Application	Screening mammograms are a routine imaging exam performed to detect breast cancer in its early stages to reduce morbidity and mortality attributed to this disease. In order to maximize the efficacy of breast cancer screening programs, proper mammographic positioning is paramount. Proper positioning ensures adequate visualization of breast tissue and is necessary for effective breast cancer detection. Therefore, breast-imaging radiologists must assess each mammogram for the adequacy of positioning before providing a final interpretation of the examination; this often necessitates return patient visits for additional imaging. In this paper, we propose a deep learning-algorithm method that mimics and automates this decision-making process to identify poorly positioned mammograms. Our objective for this algorithm is to assist mammography technologists in recognizing inadequately positioned mammograms real-time, improve the quality of mammographic positioning and performance, and ultimately reducing repeat visits for patients with initially inadequate imaging. The proposed model showed a true positive rate for detecting correct positioning of 91.35% in the mediolateral oblique view and 95.11% in the craniocaudal view. In addition to these results, we also present an automatically generated report which can aid the mammography technologist in taking corrective measures during the patient visit.	['Richard D. White', 'Mona G. Flores', 'Barbaros Selnur Erdal', 'Sarah Bonnet', 'Jeffrey Hawley', 'Clayton Taylor', 'Vikash Gupta', 'Luciano M. Prevedello']	2020-09-28	null	null	null	null	['breast-cancer-detection', 'breast-cancer-detection']	['knowledge-base', 'medical']	[ 3.85969400e-01 4.60975498e-01 -3.30126822e-01 -6.95404708e-01 -6.32592559e-01 -2.92425036e-01 -2.54309714e-01 6.11326635e-01 -3.21640760e-01 2.07510069e-01 -4.06648703e-02 -1.15091670e+00 -1.52176976e-01 -1.03532481e+00 -6.70410037e-01 -5.52044690e-01 -9.06935856e-02 6.96814299e-01 2.56114542e-01 3.34122390e-01 8.13793316e-02 7.41253614e-01 -8.04754376e-01 3.76098573e-01 5.59366286e-01 9.04061615e-01 4.80942607e-01 1.03032064e+00 1.12046637e-01 7.49649346e-01 -1.21215776e-01 -5.06386876e-01 1.21881396e-01 -6.34272516e-01 -3.87281686e-01 3.56384277e-01 2.99740791e-01 -9.57455099e-01 -9.68474969e-02 9.51505244e-01 4.96632427e-01 -6.19057357e-01 5.67464888e-01 -3.19703966e-01 -3.00220758e-01 6.83169007e-01 -7.43137956e-01 5.11157811e-01 -8.95496085e-02 -7.18059018e-02 2.53195107e-01 -5.80292761e-01 -3.62581350e-02 4.77657288e-01 7.04371274e-01 4.90394503e-01 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-3.16838294e-01 -7.66850859e-02 3.16805333e-01 -1.97916552e-01 1.31572023e-01 1.14021480e-01]	[15.181807518005371, -2.5384581089019775]
062e074e-f962-42b4-b6f7-c60eacb83176	automatic-classification-of-defective	1807.02894	null	http://arxiv.org/abs/1807.02894v3	http://arxiv.org/pdf/1807.02894v3.pdf	Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images	Electroluminescence (EL) imaging is a useful modality for the inspection of photovoltaic (PV) modules. EL images provide high spatial resolution, which makes it possible to detect even finest defects on the surface of PV modules. However, the analysis of EL images is typically a manual process that is expensive, time-consuming, and requires expert knowledge of many different types of defects. In this work, we investigate two approaches for automatic detection of such defects in a single image of a PV cell. The approaches differ in their hardware requirements, which are dictated by their respective application scenarios. The more hardware-efficient approach is based on hand-crafted features that are classified in a Support Vector Machine (SVM). To obtain a strong performance, we investigate and compare various processing variants. The more hardware-demanding approach uses an end-to-end deep Convolutional Neural Network (CNN) that runs on a Graphics Processing Unit (GPU). Both approaches are trained on 1,968 cells extracted from high resolution EL intensity images of mono- and polycrystalline PV modules. The CNN is more accurate, and reaches an average accuracy of 88.42%. The SVM achieves a slightly lower average accuracy of 82.44%, but can run on arbitrary hardware. Both automated approaches make continuous, highly accurate monitoring of PV cells feasible.	['Florian Gallwitz', 'Claudia Buerhop-Lutz', 'Vincent Christlein', 'Sergiu Deitsch', 'Stephan Berger', 'Christian Riess', 'Andreas Maier']	2018-07-08	null	null	null	null	['anomaly-classification']	['computer-vision']	[ 4.27602142e-01 -5.09348989e-01 3.06684017e-01 3.82959023e-02 -6.05699241e-01 -5.54619372e-01 1.97055250e-01 3.69154334e-01 -3.33303332e-01 7.10729599e-01 -8.11237156e-01 -3.19739997e-01 3.98408294e-01 -9.51986909e-01 -5.93914509e-01 -1.13931894e+00 4.32126522e-01 2.42125168e-01 4.02590930e-01 3.39256614e-01 3.60757232e-01 9.35271323e-01 -1.88871884e+00 3.25473487e-01 1.14797723e+00 1.47914863e+00 6.11422718e-01 8.65387619e-01 -3.41653898e-02 4.71910715e-01 -7.52835631e-01 -4.09936830e-02 -6.98821917e-02 -1.92649826e-01 -1.68615162e-01 7.41974339e-02 4.32544470e-01 -1.00634217e-01 1.56928509e-01 1.02783024e+00 5.35980880e-01 -7.50337601e-01 8.83516014e-01 -1.04199541e+00 -3.15105706e-01 -2.84108341e-01 -3.59424591e-01 2.02856243e-01 3.23653787e-01 3.07427645e-01 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58a1db5c-47bb-4a5b-9914-eb77d9cfb6f9	a-survey-on-extreme-multi-label-learning	2210.03968	null	https://arxiv.org/abs/2210.03968v1	https://arxiv.org/pdf/2210.03968v1.pdf	A Survey on Extreme Multi-label Learning	Multi-label learning has attracted significant attention from both academic and industry field in recent decades. Although existing multi-label learning algorithms achieved good performance in various tasks, they implicitly assume the size of target label space is not huge, which can be restrictive for real-world scenarios. Moreover, it is infeasible to directly adapt them to extremely large label space because of the compute and memory overhead. Therefore, eXtreme Multi-label Learning (XML) is becoming an important task and many effective approaches are proposed. To fully understand XML, we conduct a survey study in this paper. We first clarify a formal definition for XML from the perspective of supervised learning. Then, based on different model architectures and challenges of the problem, we provide a thorough discussion of the advantages and disadvantages of each category of methods. For the benefit of conducting empirical studies, we collect abundant resources regarding XML, including code implementations, and useful tools. Lastly, we propose possible research directions in XML, such as new evaluation metrics, the tail label problem, and weakly supervised XML.	['Min-Ling Zhang', 'Yu-Feng Li', 'Jiang-Xin Shi', 'Zhen Mao', 'Tong Wei']	2022-10-08	null	null	null	null	['multi-label-learning']	['methodology']	[ 3.82703632e-01 -1.31745636e-01 -6.75270021e-01 -7.37440109e-01 -1.05865717e+00 -6.34379327e-01 1.60161659e-01 4.49497163e-01 -5.21337450e-01 6.98669016e-01 -1.45458385e-01 -1.78189352e-01 -2.83081234e-01 -5.46415627e-01 -3.02562535e-01 -6.93895459e-01 1.32400030e-02 3.30531925e-01 2.44502723e-01 2.43381605e-01 1.80237740e-01 -3.20962109e-02 -1.61455095e+00 3.26406926e-01 7.04762220e-01 1.07237005e+00 6.09436817e-02 1.63078859e-01 -4.33917880e-01 7.17700303e-01 -5.53219438e-01 -6.16717577e-01 -2.38398928e-02 -3.39344323e-01 -1.08486378e+00 7.69270509e-02 4.52617586e-01 -6.74037188e-02 1.52503699e-01 1.36056638e+00 5.31103671e-01 -5.12952283e-02 6.64926291e-01 -1.57471716e+00 -2.91434824e-01 1.10952532e+00 -9.12622392e-01 -2.48066798e-01 2.11992294e-01 -6.09672785e-01 1.31470132e+00 -5.51659167e-01 2.98247069e-01 1.04045534e+00 7.26787269e-01 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-8.18437696e-01 -8.99846792e-01 -8.35026741e-01 3.92428249e-01 -9.82126445e-02 -6.90866053e-01 -2.59466052e-01 1.02529489e-02]	[9.56558609008789, 4.322548866271973]
389acba7-2849-44e2-b385-ec2176b3f5e5	fully-scalable-gaussian-processes-using	1807.02537	null	http://arxiv.org/abs/1807.02537v2	http://arxiv.org/pdf/1807.02537v2.pdf	Fully Scalable Gaussian Processes using Subspace Inducing Inputs	We introduce fully scalable Gaussian processes, an implementation scheme that tackles the problem of treating a high number of training instances together with high dimensional input data. Our key idea is a representation trick over the inducing variables called subspace inducing inputs. This is combined with certain matrix-preconditioning based parametrizations of the variational distributions that lead to simplified and numerically stable variational lower bounds. Our illustrative applications are based on challenging extreme multi-label classification problems with the extra burden of the very large number of class labels. We demonstrate the usefulness of our approach by presenting predictive performances together with low computational times in datasets with extremely large number of instances and input dimensions.	['Petros Dellaportas', 'Aristeidis Panos', 'Michalis K. Titsias']	2018-07-06	null	null	null	null	['extreme-multi-label-classification']	['methodology']	[ 2.74337113e-01 -4.97962534e-02 8.38997141e-02 -2.19537526e-01 -1.27007365e+00 -6.30597532e-01 7.76148736e-01 1.41528636e-01 -3.25078875e-01 8.21849108e-01 -6.15266673e-02 -1.06004685e-01 -3.60769570e-01 -4.78012532e-01 -4.28232193e-01 -1.25655961e+00 3.16808343e-01 9.90368009e-01 -3.38877141e-01 8.38463083e-02 1.34167552e-01 4.46350038e-01 -1.54290009e+00 9.19111669e-02 8.40932071e-01 8.30008447e-01 -2.24370822e-01 7.56596744e-01 3.49508598e-02 3.70252341e-01 -1.93719327e-01 -4.82182592e-01 3.08653146e-01 -6.39962852e-02 -7.10124969e-01 3.47027868e-01 3.06035966e-01 2.85889775e-01 1.52427763e-01 1.05701423e+00 6.49125338e-01 5.70345402e-01 1.44545984e+00 -1.46578181e+00 -4.37659562e-01 4.28691395e-02 -7.28021264e-01 -1.27049297e-01 -1.17086601e-02 -1.29210189e-01 1.04324245e+00 -1.21926713e+00 5.73003829e-01 1.33276689e+00 7.80533254e-01 4.73961949e-01 -1.88280487e+00 -5.47509007e-02 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d90f3b7e-1380-46f5-8049-302159419699	classifying-topics-in-speech-when-all-you	1908.11425	null	https://arxiv.org/abs/1908.11425v2	https://arxiv.org/pdf/1908.11425v2.pdf	Cross-lingual topic prediction for speech using translations	Given a large amount of unannotated speech in a low-resource language, can we classify the speech utterances by topic? We consider this question in the setting where a small amount of speech in the low-resource language is paired with text translations in a high-resource language. We develop an effective cross-lingual topic classifier by training on just 20 hours of translated speech, using a recent model for direct speech-to-text translation. While the translations are poor, they are still good enough to correctly classify the topic of 1-minute speech segments over 70% of the time - a 20% improvement over a majority-class baseline. Such a system could be useful for humanitarian applications like crisis response, where incoming speech in a foreign low-resource language must be quickly assessed for further action.	['Sharon Goldwater', 'Sameer Bansal', 'Adam Lopez', 'Herman Kamper']	2019-08-29	null	null	null	null	['speech-to-text-translation']	['natural-language-processing']	[ 2.34280288e-01 5.36464989e-01 -3.29942524e-01 -4.75168645e-01 -1.92097712e+00 -8.66067111e-01 8.23722124e-01 9.28018708e-03 -5.66865504e-01 7.36195147e-01 7.60755241e-01 -8.23627174e-01 6.39330149e-01 -4.21728730e-01 -6.18208826e-01 -5.20228386e-01 2.45184392e-01 1.14398491e+00 1.59720495e-01 -4.75370258e-01 -5.07460177e-01 1.14460252e-02 -8.90685737e-01 5.94169080e-01 7.62140989e-01 6.23290002e-01 4.06158715e-01 6.72621548e-01 -2.63268650e-01 4.53333259e-01 -9.38928366e-01 -1.79057091e-01 1.85153916e-01 -5.31537056e-01 -1.17879915e+00 1.25026613e-01 3.05746317e-01 -7.98765644e-02 -1.88931540e-01 6.90966964e-01 8.07528734e-01 1.97983161e-01 5.92420816e-01 -6.86462522e-01 -1.00573912e-01 7.82883227e-01 1.25196755e-01 5.45550108e-01 6.28350258e-01 -1.12834536e-01 7.65044868e-01 -1.01875675e+00 7.45367467e-01 1.48879445e+00 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ad4a5454-8064-449b-94a8-c0905e209b3f	g2pw-a-conditional-weighted-softmax-bert-for	2203.10430	null	https://arxiv.org/abs/2203.10430v5	https://arxiv.org/pdf/2203.10430v5.pdf	g2pW: A Conditional Weighted Softmax BERT for Polyphone Disambiguation in Mandarin	Polyphone disambiguation is the most crucial task in Mandarin grapheme-to-phoneme (g2p) conversion. Previous studies have approached this problem using pre-trained language models, restricted output, and extra information from Part-Of-Speech (POS) tagging. Inspired by these strategies, we propose a novel approach, called g2pW, which adapts learnable softmax-weights to condition the outputs of BERT with the polyphonic character of interest and its POS tagging. Rather than using the hard mask as in previous works, our experiments show that learning a soft-weighting function for the candidate phonemes benefits performance. In addition, our proposed g2pW does not require extra pre-trained POS tagging models while using POS tags as auxiliary features since we train the POS tagging model simultaneously with the unified encoder. Experimental results show that our g2pW outperforms existing methods on the public CPP dataset. All codes, model weights, and a user-friendly package are publicly available.	['Yi-Ren Yeh', 'Yen-Cheng Chang', 'Yu-Chuan Chang', 'Yi-Chang Chen']	2022-03-20	null	null	null	null	['polyphone-disambiguation']	['natural-language-processing']	[ 3.63380283e-01 1.81118220e-01 -3.99792343e-01 -3.93236279e-01 -1.02855098e+00 -6.33602321e-01 3.28752339e-01 -4.58825752e-03 -6.95077181e-01 6.72502637e-01 1.85470775e-01 -6.31128907e-01 5.24224877e-01 -6.20529294e-01 -7.21589267e-01 -6.25003457e-01 -4.61677648e-02 5.65731972e-02 4.18229580e-01 -6.78384230e-02 -3.19261278e-04 5.75980023e-02 -1.10459173e+00 1.64372876e-01 9.17960107e-01 8.40393007e-01 8.70885968e-01 7.32336879e-01 -2.47163326e-01 4.73423034e-01 -5.81043065e-01 -6.01755440e-01 3.13788623e-01 -4.60550249e-01 -5.79744339e-01 -3.37977737e-01 1.43555179e-01 1.88878134e-01 -2.03570262e-01 1.35355723e+00 6.88790858e-01 2.67637312e-01 3.83439094e-01 -7.03621864e-01 -7.12856352e-01 1.18720210e+00 -2.71315396e-01 2.77930290e-01 5.35890535e-02 -1.24603122e-01 1.19176686e+00 -7.73944736e-01 2.46399745e-01 1.09387386e+00 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3d8fd0ae-3542-4031-bcf3-b0776d4ddbf3	airway-measurement-by-refinement-of-synthetic	2208.14141	null	https://arxiv.org/abs/2208.14141v1	https://arxiv.org/pdf/2208.14141v1.pdf	Airway measurement by refinement of synthetic images improves mortality prediction in idiopathic pulmonary fibrosis	Several chronic lung diseases, like idiopathic pulmonary fibrosis (IPF) are characterised by abnormal dilatation of the airways. Quantification of airway features on computed tomography (CT) can help characterise disease progression. Physics based airway measurement algorithms have been developed, but have met with limited success in part due to the sheer diversity of airway morphology seen in clinical practice. Supervised learning methods are also not feasible due to the high cost of obtaining precise airway annotations. We propose synthesising airways by style transfer using perceptual losses to train our model, Airway Transfer Network (ATN). We compare our ATN model with a state-of-the-art GAN-based network (simGAN) using a) qualitative assessment; b) assessment of the ability of ATN and simGAN based CT airway metrics to predict mortality in a population of 113 patients with IPF. ATN was shown to be quicker and easier to train than simGAN. ATN-based airway measurements were also found to be consistently stronger predictors of mortality than simGAN-derived airway metrics on IPF CTs. Airway synthesis by a transformation network that refines synthetic data using perceptual losses is a realistic alternative to GAN-based methods for clinical CT analyses of idiopathic pulmonary fibrosis. Our source code can be found at https://github.com/ashkanpakzad/ATN that is compatible with the existing open-source airway analysis framework, AirQuant.	['Joseph Jacob', 'John R Hurst', 'Wim A Wuyts', 'Stijn E Verleden', 'Laurens J De Sadeleer', 'Tinne Goos', 'Marie Vermant', 'Wing Keung Cheung', 'Mou-Cheng Xu', 'Ashkan Pakzad']	2022-08-30	null	null	null	null	['mortality-prediction']	['medical']	[ 1.63301826e-01 1.32879242e-01 9.52913314e-02 -1.14724465e-01 -1.12017512e+00 -4.74301189e-01 3.60306948e-01 -9.56631899e-02 -2.59184569e-01 7.76958942e-01 5.15979230e-01 -7.03463316e-01 -2.55365729e-01 -9.38870788e-01 -3.01906824e-01 -7.35721290e-01 8.82034302e-02 1.20666730e+00 2.34559894e-01 1.05134383e-01 -4.07793313e-01 4.96453285e-01 -6.97419345e-01 4.27673876e-01 5.13991475e-01 7.18429565e-01 9.84723717e-02 1.30088949e+00 -2.19819397e-01 6.04896486e-01 -3.34701896e-01 -1.78468272e-01 2.70650774e-01 -8.43352020e-01 -9.71810520e-01 -4.80218917e-01 5.52287161e-01 -5.00958264e-01 -8.40980485e-02 1.08199276e-01 9.02583897e-01 -3.32508013e-02 7.15181708e-01 -8.46683025e-01 -2.51104057e-01 2.40033910e-01 2.79746316e-02 4.15235758e-01 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fbed58b2-3f98-45c4-8817-0777d4f21bd7	chitransformer-towards-reliable-stereo-from	2203.04554	null	https://arxiv.org/abs/2203.04554v3	https://arxiv.org/pdf/2203.04554v3.pdf	ChiTransformer:Towards Reliable Stereo from Cues	Current stereo matching techniques are challenged by restricted searching space, occluded regions and sheer size. While single image depth estimation is spared from these challenges and can achieve satisfactory results with the extracted monocular cues, the lack of stereoscopic relationship renders the monocular prediction less reliable on its own, especially in highly dynamic or cluttered environments. To address these issues in both scenarios, we present an optic-chiasm-inspired self-supervised binocular depth estimation method, wherein a vision transformer (ViT) with gated positional cross-attention (GPCA) layers is designed to enable feature-sensitive pattern retrieval between views while retaining the extensive context information aggregated through self-attentions. Monocular cues from a single view are thereafter conditionally rectified by a blending layer with the retrieved pattern pairs. This crossover design is biologically analogous to the optic-chasma structure in the human visual system and hence the name, ChiTransformer. Our experiments show that this architecture yields substantial improvements over state-of-the-art self-supervised stereo approaches by 11%, and can be used on both rectilinear and non-rectilinear (e.g., fisheye) images. Project is available at https://github.com/ISL-CV/ChiTransformer.	['Shihao Ji', 'Qing Su']	2022-03-09	null	null	null	null	['stereo-matching-1']	['computer-vision']	[ 3.15951973e-01 1.79177634e-02 -7.55952001e-02 -3.33709925e-01 -4.24788684e-01 -5.85867167e-01 6.03852570e-01 -5.37247181e-01 -2.93585628e-01 6.39248848e-01 3.00503552e-01 1.46487355e-02 5.61358780e-02 -4.85316068e-01 -6.78722143e-01 -8.66352856e-01 5.38375556e-01 3.94549966e-03 5.54836929e-01 2.35379189e-02 6.75519645e-01 3.96040052e-01 -2.22394300e+00 4.94557083e-01 7.63131440e-01 1.16223943e+00 7.61669576e-01 6.16237998e-01 2.05924645e-01 7.44351327e-01 3.89648154e-02 -2.80969501e-01 5.94239116e-01 -3.72925669e-01 -5.90453207e-01 1.27839595e-01 1.05379391e+00 -5.18055558e-01 -7.38955081e-01 8.38861704e-01 6.27352595e-01 -1.45447016e-01 4.99951184e-01 -9.94200706e-01 -4.82071966e-01 -2.66079277e-01 -6.44632280e-01 3.64338875e-01 5.78493953e-01 3.32746118e-01 8.74096990e-01 -1.19823658e+00 8.40179145e-01 9.92208719e-01 2.38954723e-01 5.59394240e-01 -1.14407492e+00 -5.28204739e-01 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899d4ad9-faf2-4ce9-b935-858f8fb209d5	deep-reinforcement-learning-for-robotic-2	2302.10717	null	https://arxiv.org/abs/2302.10717v1	https://arxiv.org/pdf/2302.10717v1.pdf	Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment	In this paper, a novel robotic grasping system is established to automatically pick up objects in cluttered scenes. A composite robotic hand composed of a suction cup and a gripper is designed for grasping the object stably. The suction cup is used for lifting the object from the clutter first and the gripper for grasping the object accordingly. We utilize the affordance map to provide pixel-wise lifting point candidates for the suction cup. To obtain a good affordance map, the active exploration mechanism is introduced to the system. An effective metric is designed to calculate the reward for the current affordance map, and a deep Q-Network (DQN) is employed to guide the robotic hand to actively explore the environment until the generated affordance map is suitable for grasping. Experimental results have demonstrated that the proposed robotic grasping system is able to greatly increase the success rate of the robotic grasping in cluttered scenes.	['Fuchun Sun', 'Huaping Liu', 'Di Guo', 'Bin Fang', 'Kai Lu', 'Yixuan Wei', 'Xiaofeng Guo', 'Yuhong Deng']	2023-02-21	null	null	null	null	['robotic-grasping']	['robots']	[-1.48080423e-01 -2.44103089e-01 -1.78149268e-01 -2.08841458e-01 -2.10684717e-01 -4.66914028e-01 -1.46025121e-01 -3.23067546e-01 -2.92118907e-01 4.33826178e-01 -2.09641010e-01 7.04731718e-02 -6.11795723e-01 -7.19005585e-01 -5.69083929e-01 -1.13161075e+00 -3.48377734e-01 1.31772175e-01 1.14235073e-01 -5.50491549e-02 5.58189154e-01 7.59606361e-01 -1.60531175e+00 -3.69492844e-02 1.00782287e+00 1.40355968e+00 1.74119937e+00 2.42368549e-01 -1.46126509e-01 2.79776275e-01 -2.34185666e-01 3.45049828e-01 6.43297851e-01 1.45536929e-01 -6.02326989e-01 1.73473924e-01 -4.14913595e-01 -1.11945307e+00 -1.48847342e-01 1.09301472e+00 4.03432727e-01 1.83211222e-01 4.28139448e-01 -1.27177906e+00 -5.75496435e-01 8.71437848e-01 -5.91761351e-01 -1.73536420e-01 -1.04161464e-01 4.62634206e-01 8.22357357e-01 -7.78568327e-01 7.68739164e-01 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b32631fd-6693-4200-a538-e16d21c05619	a-unified-object-motion-and-affinity-model	2003.11291	null	https://arxiv.org/abs/2003.11291v2	https://arxiv.org/pdf/2003.11291v2.pdf	A Unified Object Motion and Affinity Model for Online Multi-Object Tracking	Current popular online multi-object tracking (MOT) solutions apply single object trackers (SOTs) to capture object motions, while often requiring an extra affinity network to associate objects, especially for the occluded ones. This brings extra computational overhead due to repetitive feature extraction for SOT and affinity computation. Meanwhile, the model size of the sophisticated affinity network is usually non-trivial. In this paper, we propose a novel MOT framework that unifies object motion and affinity model into a single network, named UMA, in order to learn a compact feature that is discriminative for both object motion and affinity measure. In particular, UMA integrates single object tracking and metric learning into a unified triplet network by means of multi-task learning. Such design brings advantages of improved computation efficiency, low memory requirement and simplified training procedure. In addition, we equip our model with a task-specific attention module, which is used to boost task-aware feature learning. The proposed UMA can be easily trained end-to-end, and is elegant - requiring only one training stage. Experimental results show that it achieves promising performance on several MOT Challenge benchmarks.	['Ruigang Yang', 'Jianbing Shen', 'Wenguan Wang', 'Junbo Yin', 'Qinghao Meng']	2020-03-25	a-unified-object-motion-and-affinity-model-1	http://openaccess.thecvf.com/content_CVPR_2020/html/Yin_A_Unified_Object_Motion_and_Affinity_Model_for_Online_Multi-Object_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Yin_A_Unified_Object_Motion_and_Affinity_Model_for_Online_Multi-Object_CVPR_2020_paper.pdf	cvpr-2020-6	['online-multi-object-tracking']	['computer-vision']	[-1.72637522e-01 -6.22712791e-01 -2.66460210e-01 -2.80123711e-01 -7.73410976e-01 -2.56008118e-01 1.72873870e-01 -8.64442438e-02 -6.14668131e-01 3.54344279e-01 -2.81656891e-01 1.08398810e-01 -2.12543502e-01 -4.30124521e-01 -6.90219402e-01 -8.54471505e-01 3.21661890e-01 5.98900318e-01 7.54763484e-01 3.08226198e-01 -7.06806704e-02 4.20405120e-01 -1.42702091e+00 -8.05792734e-02 9.58575547e-01 1.33604658e+00 5.80686867e-01 4.07776833e-01 2.48175431e-02 7.95793295e-01 -2.64030278e-01 -3.10608864e-01 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b9c99d21-5658-4cf9-8f2e-1c23dffbcdd5	multilingual-speech-emotion-recognition-with	2211.08237	null	https://arxiv.org/abs/2211.08237v2	https://arxiv.org/pdf/2211.08237v2.pdf	Multilingual Speech Emotion Recognition With Multi-Gating Mechanism and Neural Architecture Search	Speech emotion recognition (SER) classifies audio into emotion categories such as Happy, Angry, Fear, Disgust and Neutral. While Speech Emotion Recognition (SER) is a common application for popular languages, it continues to be a problem for low-resourced languages, i.e., languages with no pretrained speech-to-text recognition models. This paper firstly proposes a language-specific model that extract emotional information from multiple pre-trained speech models, and then designs a multi-domain model that simultaneously performs SER for various languages. Our multidomain model employs a multi-gating mechanism to generate unique weighted feature combination for each language, and also searches for specific neural network structure for each language through a neural architecture search module. In addition, we introduce a contrastive auxiliary loss to build more separable representations for audio data. Our experiments show that our model raises the state-of-the-art accuracy by 3% for German and 14.3% for French.	['Akshat Gupta', 'Kehao Guo', 'Xinrui Zhang', 'HaiFeng Lan', 'Qi Meng', 'Zihan Wang']	2022-10-31	null	null	null	null	['speech-emotion-recognition']	['speech']	[-8.59607235e-02 -2.04154383e-02 -1.54687852e-01 -5.74228823e-01 -1.06338465e+00 -3.05164844e-01 2.61960030e-01 -4.66113118e-03 -5.60670197e-01 4.69678283e-01 4.26585913e-01 -8.90346523e-03 6.03504717e-01 -4.05586779e-01 -1.51967391e-01 -4.64374870e-01 3.08989454e-02 2.46034712e-01 -2.88624942e-01 -4.34330195e-01 -2.26227477e-01 4.64715391e-01 -1.23485851e+00 5.46068132e-01 5.90538919e-01 1.35593247e+00 -7.54032880e-02 7.09050059e-01 -4.62936938e-01 1.00704479e+00 -9.19960201e-01 -5.70137084e-01 -4.28200841e-01 -4.02226359e-01 -8.20391655e-01 1.66258678e-01 -2.80543566e-01 1.23085529e-01 -2.99281865e-01 1.01097703e+00 7.91086257e-01 3.12558204e-01 4.65599179e-01 -1.38700688e+00 -7.87647784e-01 5.73338628e-01 -4.48217034e-01 5.22599816e-02 4.51000988e-01 -1.17770255e-01 1.07063329e+00 -1.02254701e+00 3.61527801e-01 1.50480783e+00 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e39c9205-7aaa-4476-8a75-dd79eab20556	online-class-incremental-learning-for-real	2301.05246	null	https://arxiv.org/abs/2301.05246v1	https://arxiv.org/pdf/2301.05246v1.pdf	Online Class-Incremental Learning For Real-World Food Classification	Online Class-Incremental Learning (OCIL) aims to continuously learn new information from single-pass data streams to update the model and mitigate catastrophic forgetting. However, most existing OCIL methods make several assumptions, including non-overlapped classes across phases and an equal number of classes in each learning phase. This is a highly simplified view of typical real-world scenarios. In this paper, we extend OCIL to the real-world food image classification task by removing these assumptions and significantly improving the performance of existing OCIL methods. We first introduce a novel probabilistic framework to simulate realistic food data sequences in different scenarios, including strict, moderate, and open diets, as a new benchmark experiment protocol. Next, we propose a novel plug-and-play module to dynamically select relevant images during training for the model update to improve learning and forgetting performance. Our proposed module can be incorporated into existing Experience Replay (ER) methods, which store representative samples from each class into an episodic memory buffer for knowledge rehearsal. We evaluate our method on the challenging Food-101 dataset and show substantial improvements over the current OCIL methods, demonstrating great potential for lifelong learning of real-world food image classification.	['Fengqing Zhu', 'Jiangpeng He', 'Siddeshwar Raghavan']	2023-01-12	null	null	null	null	['class-incremental-learning']	['computer-vision']	[ 1.07762471e-01 -2.96011567e-01 -5.05826890e-01 -3.69703114e-01 -2.98062146e-01 -2.52470136e-01 2.11448982e-01 8.04564238e-01 -6.01065576e-01 7.27597415e-01 -7.49898478e-02 1.09048761e-01 -1.19006082e-01 -1.01273000e+00 -1.21321905e+00 -5.55447042e-01 -2.93184936e-01 4.91055846e-01 6.59164190e-01 -1.75617281e-02 5.80451824e-03 -5.00284657e-02 -2.26335287e+00 6.33701563e-01 1.07272780e+00 7.01614678e-01 7.76223779e-01 6.56776249e-01 -1.94797605e-01 9.53821838e-01 -5.60765803e-01 -1.79401204e-01 -9.96905267e-02 -4.32571501e-01 -5.45156896e-01 8.72581303e-02 1.16786949e-01 -5.25040925e-01 -1.87843800e-01 8.25067043e-01 6.91339016e-01 2.78734148e-01 1.64933488e-01 -1.25549376e+00 -7.48156071e-01 9.95644569e-01 -3.00521702e-01 3.08365941e-01 5.15793562e-01 -5.07051311e-03 1.48149952e-01 -7.44225502e-01 4.61807638e-01 1.00501060e+00 9.74649310e-01 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fb6d2f28-197b-4977-a407-82c50737aa3a	a-recursive-born-approach-to-nonlinear	1603.03768	null	http://arxiv.org/abs/1603.03768v1	http://arxiv.org/pdf/1603.03768v1.pdf	A Recursive Born Approach to Nonlinear Inverse Scattering	The Iterative Born Approximation (IBA) is a well-known method for describing waves scattered by semi-transparent objects. In this paper, we present a novel nonlinear inverse scattering method that combines IBA with an edge-preserving total variation (TV) regularizer. The proposed method is obtained by relating iterations of IBA to layers of a feedforward neural network and developing a corresponding error backpropagation algorithm for efficiently estimating the permittivity of the object. Simulations illustrate that, by accounting for multiple scattering, the method successfully recovers the permittivity distribution where the traditional linear inverse scattering fails.	['Ulugbek S. Kamilov', 'Dehong Liu', 'Hassan Mansour', 'Petros T. Boufounos']	2016-03-11	null	null	null	null	['transparent-objects']	['computer-vision']	[ 6.45873249e-01 -9.98663232e-02 9.52194393e-01 -5.62225342e-01 -3.07959288e-01 1.01352036e-01 1.57562479e-01 -5.08704722e-01 -4.46077257e-01 6.15068495e-01 -1.40681133e-01 -2.44475469e-01 -5.37530899e-01 -9.22380686e-01 -9.20075953e-01 -1.04784608e+00 -2.17003077e-01 3.58282447e-01 -2.11061080e-04 -1.29136816e-01 -3.59736532e-02 7.64491796e-01 -1.27817488e+00 4.78856504e-01 7.46435940e-01 1.43563843e+00 1.73161566e-01 6.22411489e-01 5.79507835e-02 9.84804213e-01 -1.74527913e-01 -2.03473151e-01 2.74485469e-01 -3.42116654e-01 -7.08734155e-01 -5.26751280e-01 4.36902255e-01 -6.64108932e-01 -4.03466851e-01 9.15216863e-01 3.38523090e-01 2.07186103e-01 9.50418532e-01 -7.28666365e-01 -1.06773734e+00 6.48195982e-01 -2.01302692e-01 1.49491087e-01 5.97246736e-02 -3.32222998e-01 5.16529322e-01 -9.60174441e-01 3.90643686e-01 8.94474924e-01 1.39073133e+00 5.64020336e-01 -1.08855522e+00 -2.85003006e-01 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-3.36735733e-02 9.89402056e-01 -2.91010022e-01 -7.97248185e-01 4.25527850e-03]	[12.50695514678955, -2.5968406200408936]
6af9c3c9-ffce-4620-8e23-5f832dea628a	microscopic-muscle-image-enhancement	1612.05719	null	http://arxiv.org/abs/1612.05719v1	http://arxiv.org/pdf/1612.05719v1.pdf	Microscopic Muscle Image Enhancement	We propose a robust image enhancement algorithm dedicated for muscle fiber specimen images captured by optical microscopes. Blur or out of focus problems are prevalent in muscle images during the image acquisition stage. Traditional image deconvolution methods do not work since they assume the blur kernels are known and also produce ring artifacts. We provide a compact framework which involves a novel spatially non-uniform blind deblurring approach specialized to muscle images which automatically detects and alleviates degraded regions. Ring artifacts problems are addressed and a kernel propagation strategy is proposed to speedup the algorithm and deals with the high non-uniformity of the blur kernels on muscle images. Experiments show that the proposed framework performs well on muscle images taken with modern advanced optical microscopes. Our framework is free of laborious parameter settings and is computationally efficient.	['Xiangfei Kong', 'Lin Yang']	2016-12-17	null	null	null	null	['image-deconvolution']	['computer-vision']	[ 3.59523952e-01 -6.25481546e-01 3.41104448e-01 -2.39553079e-02 -2.08132774e-01 -6.12321138e-01 3.18980440e-02 -5.06577492e-01 -8.31620038e-01 9.25105572e-01 -1.86126884e-02 1.56718329e-01 -2.45964736e-01 -2.23591030e-02 -4.00833189e-01 -9.80437458e-01 1.77952871e-02 3.20655406e-02 4.80582893e-01 1.78079784e-01 5.56365073e-01 3.89158934e-01 -1.11799562e+00 -2.01535493e-01 8.26918364e-01 3.28191698e-01 8.36339891e-01 1.14725220e+00 2.41744459e-01 7.63390183e-01 -5.63663960e-01 6.71122521e-02 4.07698691e-01 -5.40557981e-01 -1.01649725e+00 7.25163341e-01 4.92455691e-01 -5.85743725e-01 -4.47373956e-01 1.64812446e+00 5.51198602e-01 1.14718325e-01 5.19020498e-01 -6.08359039e-01 -7.35905111e-01 3.44808638e-01 -9.13108051e-01 8.54839981e-01 -1.43237621e-01 2.86566019e-01 1.23830967e-01 -7.81079829e-01 6.44609153e-01 6.55856669e-01 8.30341041e-01 6.72706723e-01 -1.56153631e+00 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5.02134919e-01 6.60119176e-01 -6.42057478e-01 -5.57921648e-01 8.76044452e-01]	[11.643500328063965, -2.7186403274536133]
2fb16f54-12f9-4df5-92b0-5d8a3fd24ca8	emoticon-context-aware-multimodal-emotion	2003.06692	null	https://arxiv.org/abs/2003.06692v1	https://arxiv.org/pdf/2003.06692v1.pdf	EmotiCon: Context-Aware Multimodal Emotion Recognition using Frege's Principle	We present EmotiCon, a learning-based algorithm for context-aware perceived human emotion recognition from videos and images. Motivated by Frege's Context Principle from psychology, our approach combines three interpretations of context for emotion recognition. Our first interpretation is based on using multiple modalities(e.g. faces and gaits) for emotion recognition. For the second interpretation, we gather semantic context from the input image and use a self-attention-based CNN to encode this information. Finally, we use depth maps to model the third interpretation related to socio-dynamic interactions and proximity among agents. We demonstrate the efficiency of our network through experiments on EMOTIC, a benchmark dataset. We report an Average Precision (AP) score of 35.48 across 26 classes, which is an improvement of 7-8 over prior methods. We also introduce a new dataset, GroupWalk, which is a collection of videos captured in multiple real-world settings of people walking. We report an AP of 65.83 across 4 categories on GroupWalk, which is also an improvement over prior methods.	['Dinesh Manocha', 'Rohan Chandra', 'Pooja Guhan', 'Uttaran Bhattacharya', 'Trisha Mittal', 'Aniket Bera']	2020-03-14	emoticon-context-aware-multimodal-emotion-1	http://openaccess.thecvf.com/content_CVPR_2020/html/Mittal_EmotiCon_Context-Aware_Multimodal_Emotion_Recognition_Using_Freges_Principle_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Mittal_EmotiCon_Context-Aware_Multimodal_Emotion_Recognition_Using_Freges_Principle_CVPR_2020_paper.pdf	cvpr-2020-6	['multimodal-emotion-recognition', 'emotion-recognition-in-context', 'multimodal-emotion-recognition']	['computer-vision', 'natural-language-processing', 'speech']	[ 1.17447175e-01 -2.11468428e-01 1.82470247e-01 -6.91265345e-01 -2.06005156e-01 -1.40859023e-01 5.70073843e-01 -6.14316165e-02 -5.04062593e-01 5.30412376e-01 5.63953161e-01 4.15482342e-01 1.25667781e-01 -5.51722109e-01 -4.41365302e-01 -5.13868332e-01 -4.97313976e-01 -2.33404011e-01 -4.60529625e-02 -3.65421176e-01 1.73771858e-01 4.51637432e-02 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2159a137-9c52-4106-9d3c-37bd06f44d17	predicting-gender-via-eye-movements	2206.07442	null	https://arxiv.org/abs/2206.07442v1	https://arxiv.org/pdf/2206.07442v1.pdf	Predicting Gender via Eye Movements	In this paper, we report the first stable results on gender prediction via eye movements. We use a dataset with images of faces as stimuli and with a large number of 370 participants. Stability has two meanings for us: first that we are able to estimate the standard deviation (SD) of a single prediction experiment (it is around 4.1 %); this is achieved by varying the number of participants. And second, we are able to provide a mean accuracy with a very low standard error (SEM): our accuracy is 65.2 %, and the SEM is 0.80 %; this is achieved through many runs of randomly selecting training and test sets for the prediction. Our study shows that two particular classifiers achieve the best accuracies: Random Forests and Logistic Regression. Our results reconfirm previous findings that females are more biased towards the left eyes of the stimuli.	['Sebastian Maneth', 'Sahar Mahdie Klim Al Zaidawi', 'Rishabh Vallabh Varsha Haria']	2022-06-15	null	null	null	null	['gender-prediction']	['computer-vision']	[ 1.61243588e-01 3.68711919e-01 -2.12107480e-01 -5.58506906e-01 -2.03482583e-01 -3.25326800e-01 6.34589076e-01 9.13585722e-02 -7.15165794e-01 9.54154193e-01 -1.84633866e-01 -2.07716554e-01 1.52545748e-02 -4.91727144e-01 -5.06489336e-01 -4.76228148e-01 -4.37776856e-02 3.78907949e-01 2.69822955e-01 1.78178802e-01 6.01365447e-01 4.02560711e-01 -1.93507767e+00 -6.11795150e-02 7.22735226e-01 1.09833324e+00 -1.83521971e-01 4.58328545e-01 2.93430537e-01 4.32647705e-01 -6.60928965e-01 -7.44950294e-01 1.65361166e-01 -3.59896779e-01 -5.51359534e-01 -2.46236935e-01 6.88705683e-01 -2.24455401e-01 4.10428047e-01 6.60268605e-01 4.32013690e-01 -6.82321377e-03 8.69556069e-01 -1.40827131e+00 -5.86483598e-01 3.48880351e-01 -1.00059772e+00 -3.33483964e-02 6.01199865e-01 9.26756114e-02 7.65548885e-01 -7.26376772e-01 7.98554003e-01 1.25736713e+00 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7d7d84dd-6401-4627-97ff-5e8b2b6d20a1	modeling-entities-as-semantic-points-for	2303.13095	null	https://arxiv.org/abs/2303.13095v2	https://arxiv.org/pdf/2303.13095v2.pdf	Modeling Entities as Semantic Points for Visual Information Extraction in the Wild	Recently, Visual Information Extraction (VIE) has been becoming increasingly important in both the academia and industry, due to the wide range of real-world applications. Previously, numerous works have been proposed to tackle this problem. However, the benchmarks used to assess these methods are relatively plain, i.e., scenarios with real-world complexity are not fully represented in these benchmarks. As the first contribution of this work, we curate and release a new dataset for VIE, in which the document images are much more challenging in that they are taken from real applications, and difficulties such as blur, partial occlusion, and printing shift are quite common. All these factors may lead to failures in information extraction. Therefore, as the second contribution, we explore an alternative approach to precisely and robustly extract key information from document images under such tough conditions. Specifically, in contrast to previous methods, which usually either incorporate visual information into a multi-modal architecture or train text spotting and information extraction in an end-to-end fashion, we explicitly model entities as semantic points, i.e., center points of entities are enriched with semantic information describing the attributes and relationships of different entities, which could largely benefit entity labeling and linking. Extensive experiments on standard benchmarks in this field as well as the proposed dataset demonstrate that the proposed method can achieve significantly enhanced performance on entity labeling and linking, compared with previous state-of-the-art models. Dataset is available at https://www.modelscope.cn/datasets/damo/SIBR/summary.	['Cong Yao', 'Xiang Bai', 'Wenqing Cheng', 'Humen Zhong', 'Sibo Song', 'Pengfei Wang', 'Rujiao Long', 'Zhibo Yang']	2023-03-23	null	http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Modeling_Entities_As_Semantic_Points_for_Visual_Information_Extraction_in_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Modeling_Entities_As_Semantic_Points_for_Visual_Information_Extraction_in_CVPR_2023_paper.pdf	cvpr-2023-1	['text-spotting']	['computer-vision']	[ 8.87353048e-02 -6.22928478e-02 -3.06298822e-01 -2.55250573e-01 -5.41541755e-01 -6.70448780e-01 7.32161343e-01 2.77537555e-01 -2.52025336e-01 7.34650612e-01 8.56245831e-02 -3.91162150e-02 4.57423590e-02 -7.59494305e-01 -6.94635868e-01 -4.54265207e-01 3.37180078e-01 3.23980033e-01 3.54540199e-01 -1.70773491e-01 1.94131389e-01 3.15019220e-01 -1.50791717e+00 3.05430561e-01 1.02854848e+00 9.95470524e-01 1.69115618e-01 3.97149734e-02 -5.87492764e-01 4.79238868e-01 -6.89345121e-01 -8.41956317e-01 9.43796113e-02 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71d24174-e0c9-4f81-8e48-0a8169a01c10	molecular-property-prediction-a-multilevel	1906.11081	null	https://arxiv.org/abs/1906.11081v1	https://arxiv.org/pdf/1906.11081v1.pdf	Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective	Predicting molecular properties (e.g., atomization energy) is an essential issue in quantum chemistry, which could speed up much research progress, such as drug designing and substance discovery. Traditional studies based on density functional theory (DFT) in physics are proved to be time-consuming for predicting large number of molecules. Recently, the machine learning methods, which consider much rule-based information, have also shown potentials for this issue. However, the complex inherent quantum interactions of molecules are still largely underexplored by existing solutions. In this paper, we propose a generalizable and transferable Multilevel Graph Convolutional neural Network (MGCN) for molecular property prediction. Specifically, we represent each molecule as a graph to preserve its internal structure. Moreover, the well-designed hierarchical graph neural network directly extracts features from the conformation and spatial information followed by the multilevel interactions. As a consequence, the multilevel overall representations can be utilized to make the prediction. Extensive experiments on both datasets of equilibrium and off-equilibrium molecules demonstrate the effectiveness of our model. Furthermore, the detailed results also prove that MGCN is generalizable and transferable for the prediction.	['Lixin He', 'Chengqiang Lu', 'Chao Wang', 'Peize Lin', 'Zhenya Huang', 'Qi Liu']	2019-06-25	null	null	null	null	['graph-regression']	['graphs']	[ 1.09100796e-01 -2.60722488e-01 -3.89814913e-01 -3.46591890e-01 -1.64784715e-01 -2.82947898e-01 2.64767617e-01 5.72560847e-01 1.04659773e-01 1.07172668e+00 -4.48888466e-02 -5.78819931e-01 -1.56669393e-01 -1.35123491e+00 -1.06424189e+00 -1.06938958e+00 -1.71867877e-01 2.87524629e-02 1.67093471e-01 -4.10185158e-01 3.80645424e-01 7.71008551e-01 -1.27789867e+00 2.10079312e-01 1.34174418e+00 8.49668264e-01 1.14786580e-01 1.54896647e-01 -1.37745962e-01 7.64552712e-01 3.44612310e-03 -1.53827280e-01 -2.25640997e-01 -5.27575552e-01 -5.72604179e-01 -3.23852599e-01 3.35210264e-01 -6.72299787e-02 -6.47432089e-01 1.17514133e+00 4.32752550e-01 3.21512133e-01 6.79198742e-01 -7.86895990e-01 -1.00316441e+00 3.67414027e-01 -4.71998215e-01 -4.67139632e-02 3.63857806e-01 2.54890084e-01 9.94126260e-01 -7.31266737e-01 4.06501830e-01 1.28210640e+00 4.08598661e-01 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91d45f18-ff97-42b5-ae23-aea40904f081	semantic-edge-detection-with-diverse-deep	1804.02864	null	https://arxiv.org/abs/1804.02864v5	https://arxiv.org/pdf/1804.02864v5.pdf	Semantic Edge Detection with Diverse Deep Supervision	Semantic edge detection (SED), which aims at jointly extracting edges as well as their category information, has far-reaching applications in domains such as semantic segmentation, object proposal generation, and object recognition. SED naturally requires achieving two distinct supervision targets: locating fine detailed edges and identifying high-level semantics. Our motivation comes from the hypothesis that such distinct targets prevent state-of-the-art SED methods from effectively using deep supervision to improve results. To this end, we propose a novel fully convolutional neural network using diverse deep supervision (DDS) within a multi-task framework where bottom layers aim at generating category-agnostic edges, while top layers are responsible for the detection of category-aware semantic edges. To overcome the hypothesized supervision challenge, a novel information converter unit is introduced, whose effectiveness has been extensively evaluated on SBD and Cityscapes datasets.	['DaCheng Tao', 'Jiawang Bian', 'Deng-Ping Fan', 'Ming-Ming Cheng', 'Le Zhang', 'Yun Liu']	2018-04-09	null	null	null	null	['object-proposal-generation']	['computer-vision']	[ 3.61101061e-01 3.91553730e-01 -1.54212400e-01 -6.15011692e-01 -6.98057950e-01 -2.77971894e-01 8.12469840e-01 1.88315004e-01 -5.04851222e-01 3.96630555e-01 1.20390095e-01 -5.14452048e-02 6.95138425e-02 -8.72443974e-01 -8.78074825e-01 -3.07450086e-01 -4.26205210e-02 4.53417808e-01 5.72367549e-01 -1.67810068e-01 6.89169541e-02 1.39710084e-01 -1.66661680e+00 3.74631196e-01 1.06338537e+00 1.12847674e+00 2.27655470e-01 3.39082599e-01 -2.34610021e-01 6.43688858e-01 -3.88364822e-01 -5.07176757e-01 1.04745105e-01 -3.58360678e-01 -9.73105431e-01 3.46941143e-01 4.31798816e-01 -1.08204097e-01 -1.08940843e-02 1.29107022e+00 2.48005837e-01 -7.88269043e-02 6.22402370e-01 -1.45868635e+00 -4.59785640e-01 5.24354994e-01 -4.94795471e-01 4.77337353e-02 -1.27369061e-01 4.19775099e-02 1.27391934e+00 -8.21176827e-01 6.00924015e-01 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66bb5056-5e0a-4a13-8358-0d5e885d5691	slack-stable-learning-of-augmentations-with-1	2306.09998	null	https://arxiv.org/abs/2306.09998v1	https://arxiv.org/pdf/2306.09998v1.pdf	SLACK: Stable Learning of Augmentations with Cold-start and KL regularization	Data augmentation is known to improve the generalization capabilities of neural networks, provided that the set of transformations is chosen with care, a selection often performed manually. Automatic data augmentation aims at automating this process. However, most recent approaches still rely on some prior information; they start from a small pool of manually-selected default transformations that are either used to pretrain the network or forced to be part of the policy learned by the automatic data augmentation algorithm. In this paper, we propose to directly learn the augmentation policy without leveraging such prior knowledge. The resulting bilevel optimization problem becomes more challenging due to the larger search space and the inherent instability of bilevel optimization algorithms. To mitigate these issues (i) we follow a successive cold-start strategy with a Kullback-Leibler regularization, and (ii) we parameterize magnitudes as continuous distributions. Our approach leads to competitive results on standard benchmarks despite a more challenging setting, and generalizes beyond natural images.	['Julien Mairal', 'Diane Larlus', 'Michael Arbel', 'Juliette Marrie']	2023-06-16	slack-stable-learning-of-augmentations-with	http://openaccess.thecvf.com//content/CVPR2023/html/Marrie_SLACK_Stable_Learning_of_Augmentations_With_Cold-Start_and_KL_Regularization_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Marrie_SLACK_Stable_Learning_of_Augmentations_With_Cold-Start_and_KL_Regularization_CVPR_2023_paper.pdf	cvpr-2023-1	['bilevel-optimization']	['methodology']	[ 5.94667792e-01 9.48188826e-02 -3.61257315e-01 -4.41366881e-01 -5.30109644e-01 -7.51512766e-01 9.43318903e-01 1.37613997e-01 -9.71253872e-01 7.09941030e-01 1.19630292e-01 -4.52097207e-01 -9.40559655e-02 -5.59710026e-01 -8.28840733e-01 -7.96161771e-01 1.56343967e-01 7.16965139e-01 9.32462811e-02 -2.33642220e-01 1.41176268e-01 5.49680829e-01 -1.32198596e+00 -2.32541606e-01 9.87236142e-01 1.01285648e+00 -8.74620974e-02 4.60900664e-01 -7.73170143e-02 5.39991796e-01 -3.61932904e-01 -3.36385339e-01 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3a2b4469-ff5c-44a4-9c35-db15987f15bf	inflected-forms-are-redundant-in-question	2301.00397	null	https://arxiv.org/abs/2301.00397v1	https://arxiv.org/pdf/2301.00397v1.pdf	Inflected Forms Are Redundant in Question Generation Models	Neural models with an encoder-decoder framework provide a feasible solution to Question Generation (QG). However, after analyzing the model vocabulary we find that current models (both RNN-based and pre-training based) have more than 23\% inflected forms. As a result, the encoder will generate separate embeddings for the inflected forms, leading to a waste of training data and parameters. Even worse, in decoding these models are vulnerable to irrelevant noise and they suffer from high computational costs. In this paper, we propose an approach to enhance the performance of QG by fusing word transformation. Firstly, we identify the inflected forms of words from the input of encoder, and replace them with the root words, letting the encoder pay more attention to the repetitive root words. Secondly, we propose to adapt QG as a combination of the following actions in the encode-decoder framework: generating a question word, copying a word from the source sequence or generating a word transformation type. Such extension can greatly decrease the size of predicted words in the decoder as well as noise. We apply our approach to a typical RNN-based model and \textsc{UniLM} to get the improved versions. We conduct extensive experiments on SQuAD and MS MARCO datasets. The experimental results show that the improved versions can significantly outperform the corresponding baselines in terms of BLEU, ROUGE-L and METEOR as well as time cost.	['Chengzhong Xu', 'Hongyin Tang', 'Xingwu Sun']	2023-01-01	null	null	null	null	['question-generation']	['natural-language-processing']	[ 3.41569841e-01 1.87751547e-01 2.72984564e-01 -1.95987180e-01 -9.57796097e-01 -6.41342163e-01 4.41269964e-01 -4.83826594e-03 -6.99664712e-01 6.59249604e-01 4.42671776e-01 -6.01835907e-01 3.51914436e-01 -8.81573200e-01 -7.43525028e-01 -4.46501225e-01 5.42593181e-01 2.54034936e-01 3.05989921e-01 -4.65233535e-01 2.36370668e-01 -2.05460623e-01 -1.31104779e+00 3.01022619e-01 1.28361106e+00 7.14741826e-01 5.54786086e-01 6.82674408e-01 -6.08929455e-01 6.11710906e-01 -8.85542035e-01 -8.44124496e-01 1.45726413e-01 -7.83776581e-01 -8.63038599e-01 -3.75558101e-02 1.74635246e-01 -4.09765810e-01 -3.82011026e-01 1.08001792e+00 7.56034911e-01 1.32285625e-01 5.77658176e-01 -8.17801237e-01 -1.01560962e+00 8.52790356e-01 -2.10750297e-01 1.36976227e-01 2.76508003e-01 1.33518204e-01 1.03784049e+00 -1.09911335e+00 3.91772538e-01 1.32986593e+00 3.59847009e-01 7.67630398e-01 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-2.36673400e-01 3.05942208e-01 9.49038565e-02 -8.87743160e-02 -4.92090374e-01 8.69987160e-02]	[11.56331729888916, 9.69242000579834]
2ea314b5-3143-4913-bc1f-ce38b0de40b9	lingjing-at-semeval-2022-task-1-multi-task	null	null	https://aclanthology.org/2022.semeval-1.4	https://aclanthology.org/2022.semeval-1.4.pdf	LingJing at SemEval-2022 Task 1: Multi-task Self-supervised Pre-training for Multilingual Reverse Dictionary	This paper introduces the approach of Team LingJing’s experiments on SemEval-2022 Task 1 Comparing Dictionaries and Word Embeddings (CODWOE). This task aims at comparing two types of semantic descriptions and including two sub-tasks: the definition modeling and reverse dictionary track. Our team focuses on the reverse dictionary track and adopts the multi-task self-supervised pre-training for multilingual reverse dictionaries. Specifically, the randomly initialized mDeBERTa-base model is used to perform multi-task pre-training on the multilingual training datasets. The pre-training step is divided into two stages, namely the MLM pre-training stage and the contrastive pre-training stage. The experimental results show that the proposed method has achieved good performance in the reverse dictionary track, where we rank the 1-st in the Sgns targets of the EN and RU languages. All the experimental codes are open-sourced at https://github.com/WENGSYX/Semeval.	['Shutao Li', 'Bin Sun', 'Shizhu He', 'Fei Xia', 'Yixuan Weng', 'Bin Li']	null	null	null	null	semeval-naacl-2022-7	['reverse-dictionary']	['natural-language-processing']	[-2.8532711e-01 -1.5299523e-01 -6.1565828e-01 -4.5196080e-01 -8.7744665e-01 -5.7253665e-01 8.8349277e-01 7.2638340e-02 -9.7589022e-01 6.2508279e-01 4.7472197e-01 -4.2125723e-01 2.2515757e-01 -4.3141818e-01 -3.3422711e-01 -5.0390840e-01 2.6225334e-01 1.0548007e+00 -1.7269872e-01 -4.7303236e-01 8.3561435e-02 -1.8947853e-01 -1.1347241e+00 3.1738928e-01 8.5557044e-01 7.1375155e-01 6.9017673e-01 2.5941989e-01 -3.2072514e-01 6.5806407e-01 -3.0886048e-01 -7.6969713e-01 1.4617212e-01 -4.1715807e-01 -6.6353661e-01 -4.0887138e-01 3.8504663e-01 2.3585472e-02 -2.8343716e-01 1.3711466e+00 9.2860574e-01 1.4147849e-01 7.0127296e-01 -9.4359869e-01 -1.0382317e+00 1.2621679e+00 -2.4628465e-01 3.9603716e-01 4.1130286e-02 -7.1041323e-02 1.4459008e+00 -1.4757630e+00 6.0542631e-01 1.3662457e+00 7.5279361e-01 7.7912033e-01 -8.2115149e-01 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ca67e65c-daa9-44fe-a6d5-0f8a4afcd4f5	vision-transformer-for-fast-and-efficient	2105.08582	null	https://arxiv.org/abs/2105.08582v1	https://arxiv.org/pdf/2105.08582v1.pdf	Vision Transformer for Fast and Efficient Scene Text Recognition	Scene text recognition (STR) enables computers to read text in natural scenes such as object labels, road signs and instructions. STR helps machines perform informed decisions such as what object to pick, which direction to go, and what is the next step of action. In the body of work on STR, the focus has always been on recognition accuracy. There is little emphasis placed on speed and computational efficiency which are equally important especially for energy-constrained mobile machines. In this paper we propose ViTSTR, an STR with a simple single stage model architecture built on a compute and parameter efficient vision transformer (ViT). On a comparable strong baseline method such as TRBA with accuracy of 84.3%, our small ViTSTR achieves a competitive accuracy of 82.6% (84.2% with data augmentation) at 2.4x speed up, using only 43.4% of the number of parameters and 42.2% FLOPS. The tiny version of ViTSTR achieves 80.3% accuracy (82.1% with data augmentation), at 2.5x the speed, requiring only 10.9% of the number of parameters and 11.9% FLOPS. With data augmentation, our base ViTSTR outperforms TRBA at 85.2% accuracy (83.7% without augmentation) at 2.3x the speed but requires 73.2% more parameters and 61.5% more FLOPS. In terms of trade-offs, nearly all ViTSTR configurations are at or near the frontiers to maximize accuracy, speed and computational efficiency all at the same time.	['Rowel Atienza']	2021-05-18	null	null	null	null	['scene-text-recognition']	['computer-vision']	[ 3.45539480e-01 -2.51998514e-01 -3.35736305e-01 -1.98436499e-01 -6.26451850e-01 -5.32280624e-01 6.78207636e-01 -1.31027862e-01 -7.67271042e-01 1.86025411e-01 -1.74759701e-01 -7.65944302e-01 4.96805578e-01 -7.09238231e-01 -6.84811413e-01 -3.61485541e-01 5.26181936e-01 5.49860597e-01 3.74669731e-01 -6.43839985e-02 4.94208008e-01 4.58490551e-01 -1.41868293e+00 2.61457115e-01 4.82604444e-01 1.07887268e+00 2.51434743e-01 1.21846652e+00 -2.00631812e-01 1.08311021e+00 -4.06947047e-01 -4.58685979e-02 2.33384699e-01 -3.21081840e-02 -9.28280473e-01 9.60920826e-02 8.32583070e-01 -5.70114017e-01 -5.89473426e-01 4.47622359e-01 3.18825424e-01 1.51943564e-01 4.39581692e-01 -1.27378869e+00 -2.63426453e-01 2.84837753e-01 -9.70167935e-01 4.11161333e-01 9.56146866e-02 3.73971701e-01 8.26873779e-01 -9.22276318e-01 1.55088753e-01 9.35784459e-01 4.98937696e-01 4.30126756e-01 -1.07436156e+00 -4.31763351e-01 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68fd0dd5-b737-4bd4-aea9-c11eaf735704	adversarial-alignment-breaking-the-trade-off	2306.03229	null	https://arxiv.org/abs/2306.03229v1	https://arxiv.org/pdf/2306.03229v1.pdf	Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception	Deep neural networks (DNNs) are known to have a fundamental sensitivity to adversarial attacks, perturbations of the input that are imperceptible to humans yet powerful enough to change the visual decision of a model. Adversarial attacks have long been considered the "Achilles' heel" of deep learning, which may eventually force a shift in modeling paradigms. Nevertheless, the formidable capabilities of modern large-scale DNNs have somewhat eclipsed these early concerns. Do adversarial attacks continue to pose a threat to DNNs? Here, we investigate how the robustness of DNNs to adversarial attacks has evolved as their accuracy on ImageNet has continued to improve. We measure adversarial robustness in two different ways: First, we measure the smallest adversarial attack needed to cause a model to change its object categorization decision. Second, we measure how aligned successful attacks are with the features that humans find diagnostic for object recognition. We find that adversarial attacks are inducing bigger and more easily detectable changes to image pixels as DNNs grow better on ImageNet, but these attacks are also becoming less aligned with features that humans find diagnostic for recognition. To better understand the source of this trade-off, we turn to the neural harmonizer, a DNN training routine that encourages models to leverage the same features as humans to solve tasks. Harmonized DNNs achieve the best of both worlds and experience attacks that are detectable and affect features that humans find diagnostic for recognition, meaning that attacks on these models are more likely to be rendered ineffective by inducing similar effects on human perception. Our findings suggest that the sensitivity of DNNs to adversarial attacks can be mitigated by DNN scale, data scale, and training routines that align models with biological intelligence.	['Thomas Serre', 'Stephanie Olaiya', 'Thomas Fel', 'Alekh Karkada Ashok', 'Thibaut Boissin', 'Pinyuan Feng', 'Drew Linsley']	2023-06-05	null	null	null	null	['adversarial-attack', 'adversarial-robustness', 'object-recognition', 'object-categorization']	['adversarial', 'adversarial', 'computer-vision', 'computer-vision']	[ 3.16644877e-01 1.02955960e-01 4.69437867e-01 -2.93944567e-01 -1.59385756e-01 -1.14131832e+00 7.98632026e-01 -2.09616512e-01 -7.34887898e-01 4.80470777e-01 3.98659743e-02 -3.12226027e-01 5.63896447e-02 -9.03448880e-01 -1.06903160e+00 -5.87683678e-01 -5.19596897e-02 7.89563283e-02 1.77909851e-01 -5.14429271e-01 1.13132171e-01 9.33215499e-01 -1.30496919e+00 2.03930035e-01 5.26463330e-01 5.78225970e-01 -2.32655123e-01 7.46720135e-01 4.11771446e-01 9.11138415e-01 -1.22253537e+00 -5.42034805e-01 6.60528958e-01 -1.63869724e-01 -6.07680559e-01 -2.43272081e-01 9.10161495e-01 -3.94135296e-01 -6.49209142e-01 1.45571470e+00 4.86543506e-01 -1.09824397e-01 5.91451228e-01 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2.90122986e-01 -4.30929869e-01 -1.05519295e+00 -8.14722240e-01 1.34632736e-01 -3.31652611e-01 5.31112254e-01 8.06693673e-01 2.80759186e-01 5.59304535e-01 5.60257912e-01 -7.69122601e-01 -7.72251368e-01 -7.04927981e-01 -3.21474135e-01 6.74271703e-01 4.15193766e-01 -3.67832959e-01 -6.54541731e-01 3.04438118e-02]	[5.6315436363220215, 7.876433849334717]
ea0962f9-7351-4eff-ac77-05e31bf67853	3d-scene-reconstruction-from-a-single	null	null	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3925_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123670052.pdf	3D Scene Reconstruction from a Single Viewport	We present a novel approach to infer volumetric reconstructions from a single viewport, based only on an RGB image and a reconstructed normal image. To overcome the problem of reconstructing regions in 3D that are occluded in the 2D image, we propose to learn this information from synthetically generated high-resolution data. To do this, we introduce a deep network architecture that is specifically designed for volumetric TSDF data by featuring a specific tree net architecture. Our framework can handle a 3D resolution of $512^3$ by introducing a dedicated compression technique based on a modified autoencoder. Furthermore, we introduce a novel loss shaping technique for 3D data that guides the learning process towards regions where free and occupied space are close to each other. As we show in experiments on synthetic and realistic benchmark data, this leads to very good reconstruction results, both visually and in terms of quantitative measures.	['Rudolph Triebel', 'Maximilian Denninger']	null	null	null	null	eccv-2020-8	['3d-scene-reconstruction']	['computer-vision']	[ 1.93210199e-01 2.53642797e-01 3.13037306e-01 -2.07227707e-01 -4.57614094e-01 -5.69970347e-02 4.57987040e-01 -1.29488651e-02 -3.43526900e-01 8.07062209e-01 5.80362305e-02 -1.46663585e-03 2.25256234e-02 -1.39354646e+00 -1.05043471e+00 -4.76381421e-01 -1.19299538e-01 5.91814756e-01 3.31322134e-01 -1.73022524e-01 3.03611550e-02 9.63205814e-01 -1.81796420e+00 4.03990775e-01 4.09715950e-01 1.34623957e+00 3.58512044e-01 4.85268056e-01 -1.82606965e-01 8.99770260e-01 -3.34462732e-01 -1.75649554e-01 5.45852184e-01 -1.53036386e-01 -7.77928472e-01 2.60199308e-01 2.78211206e-01 -7.74775803e-01 -4.30793166e-01 5.13352394e-01 3.80929410e-01 1.76380388e-02 6.68720722e-01 -4.37832713e-01 1.98282208e-03 1.25438124e-01 -4.31755692e-01 -2.70436015e-02 5.19090176e-01 9.62691847e-03 6.47110879e-01 -8.71072054e-01 9.88930702e-01 1.14403927e+00 6.86430156e-01 3.77125710e-01 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-8.15768421e-01 3.70344877e-01 5.49641609e-01 -7.65705705e-01 -3.91500771e-01 -8.72540101e-02]	[8.836307525634766, -3.014913320541382]
a21c29c1-c83d-422f-bb39-63b15a674474	an-extension-of-fano-s-inequality-for	2009.08097	null	https://arxiv.org/abs/2009.08097v1	https://arxiv.org/pdf/2009.08097v1.pdf	An Extension of Fano's Inequality for Characterizing Model Susceptibility to Membership Inference Attacks	Deep neural networks have been shown to be vulnerable to membership inference attacks wherein the attacker aims to detect whether specific input data were used to train the model. These attacks can potentially leak private or proprietary data. We present a new extension of Fano's inequality and employ it to theoretically establish that the probability of success for a membership inference attack on a deep neural network can be bounded using the mutual information between its inputs and its activations. This enables the use of mutual information to measure the susceptibility of a DNN model to membership inference attacks. In our empirical evaluation, we show that the correlation between the mutual information and the susceptibility of the DNN model to membership inference attacks is 0.966, 0.996, and 0.955 for CIFAR-10, SVHN and GTSRB models, respectively.	['Ananthram Swami', 'Susmit Jha', 'Sumit Kumar Jha', 'Sunny Raj', 'Laura L. Pullum', 'Rickard Ewetz', 'Alvaro Velasquez']	2020-09-17	null	null	null	null	['membership-inference-attack']	['computer-vision']	[ 3.25378962e-02 2.19279900e-01 5.85931204e-02 -4.93270367e-01 -2.87408352e-01 -1.07627416e+00 6.03305578e-01 -2.42531970e-02 -7.21412301e-01 8.66937160e-01 -5.26597977e-01 -1.04136288e+00 -2.05183059e-01 -1.02454281e+00 -1.12821472e+00 -8.01752448e-01 -3.89649481e-01 9.24577191e-02 2.31098101e-01 3.08020532e-01 6.63799122e-02 8.78848255e-01 -1.01727891e+00 1.83518931e-01 2.14094386e-01 1.26657259e+00 -6.22208357e-01 5.32140315e-01 5.23167789e-01 7.16380894e-01 -1.15572357e+00 -6.23243749e-01 3.48738104e-01 5.78411184e-02 -8.98963988e-01 -8.93841684e-01 3.91698003e-01 -6.52382433e-01 -8.47799182e-01 1.65295994e+00 1.11719199e-01 -1.08745731e-01 6.67894363e-01 -1.62828422e+00 -1.39707923e-01 1.24354255e+00 -1.28682524e-01 4.22024220e-01 -2.31472999e-01 5.88626862e-02 8.43930244e-01 7.03501925e-02 8.86427090e-02 1.05730546e+00 4.95507151e-01 7.90211201e-01 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-9.05582428e-01 1.49540454e-01 4.05168265e-01 -3.65640044e-01 -9.94520545e-01 -3.60397875e-01]	[5.846221923828125, 7.4417266845703125]
48fed4f4-13f9-4dc4-8bf6-166cfba57ece	heterogeneous-face-recognition-via-face	2206.04854	null	https://arxiv.org/abs/2206.04854v1	https://arxiv.org/pdf/2206.04854v1.pdf	Heterogeneous Face Recognition via Face Synthesis with Identity-Attribute Disentanglement	Heterogeneous Face Recognition (HFR) aims to match faces across different domains (e.g., visible to near-infrared images), which has been widely applied in authentication and forensics scenarios. However, HFR is a challenging problem because of the large cross-domain discrepancy, limited heterogeneous data pairs, and large variation of facial attributes. To address these challenges, we propose a new HFR method from the perspective of heterogeneous data augmentation, named Face Synthesis with Identity-Attribute Disentanglement (FSIAD). Firstly, the identity-attribute disentanglement (IAD) decouples face images into identity-related representations and identity-unrelated representations (called attributes), and then decreases the correlation between identities and attributes. Secondly, we devise a face synthesis module (FSM) to generate a large number of images with stochastic combinations of disentangled identities and attributes for enriching the attribute diversity of synthetic images. Both the original images and the synthetic ones are utilized to train the HFR network for tackling the challenges and improving the performance of HFR. Extensive experiments on five HFR databases validate that FSIAD obtains superior performance than previous HFR approaches. Particularly, FSIAD obtains 4.8% improvement over state of the art in terms of VR@FAR=0.01% on LAMP-HQ, the largest HFR database so far.	['Xiao-Yu Zhang', 'Mandi Luo', 'Chaoyou Fu', 'Jian Liang', 'Ziming Yang']	2022-06-10	null	null	null	null	['heterogeneous-face-recognition']	['computer-vision']	[ 3.07541937e-01 -3.50829422e-01 3.36581953e-02 -4.15713817e-01 -6.76304042e-01 -5.89716554e-01 6.15210295e-01 -6.47199988e-01 6.56555519e-02 6.49790525e-01 1.44154131e-01 7.42990077e-02 -9.49392766e-02 -7.16955900e-01 -3.23996514e-01 -8.94855082e-01 3.10517967e-01 2.48686448e-01 -4.65084970e-01 -3.41954499e-01 -1.32758260e-01 7.69242167e-01 -1.87716603e+00 2.12023810e-01 9.53782499e-01 1.02300739e+00 -2.85555422e-01 4.26536128e-02 1.21651374e-01 4.01572436e-01 -6.31259799e-01 -7.62800634e-01 5.30163884e-01 -3.98500174e-01 -2.26534501e-01 4.84109782e-02 6.86685026e-01 -3.21227729e-01 -6.40038013e-01 1.03577197e+00 8.64238918e-01 1.15667567e-01 4.51402187e-01 -1.61127746e+00 -1.12409925e+00 1.75310597e-01 -8.87757957e-01 -1.33588448e-01 5.41195393e-01 1.39821112e-01 6.18800938e-01 -1.36129260e+00 5.51859260e-01 1.60463333e+00 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1.29625309e+00 -5.29134929e-01 -5.73294401e-01 -5.78825295e-01 -6.75494075e-01 8.07917774e-01 4.07170206e-01 -9.33149993e-01 -3.56132329e-01 -6.79375455e-02]	[13.12350845336914, 0.4519170820713043]
6c495e34-7bd2-4a44-914a-348236d25ea9	multi-view-low-rank-sparse-subspace	1708.08732	null	http://arxiv.org/abs/1708.08732v1	http://arxiv.org/pdf/1708.08732v1.pdf	Multi-view Low-rank Sparse Subspace Clustering	Most existing approaches address multi-view subspace clustering problem by constructing the affinity matrix on each view separately and afterwards propose how to extend spectral clustering algorithm to handle multi-view data. This paper presents an approach to multi-view subspace clustering that learns a joint subspace representation by constructing affinity matrix shared among all views. Relying on the importance of both low-rank and sparsity constraints in the construction of the affinity matrix, we introduce the objective that balances between the agreement across different views, while at the same time encourages sparsity and low-rankness of the solution. Related low-rank and sparsity constrained optimization problem is for each view solved using the alternating direction method of multipliers. Furthermore, we extend our approach to cluster data drawn from nonlinear subspaces by solving the corresponding problem in a reproducing kernel Hilbert space. The proposed algorithm outperforms state-of-the-art multi-view subspace clustering algorithms on one synthetic and four real-world datasets.	['Ivica Kopriva', 'Maria Brbic']	2017-08-29	null	null	null	null	['multi-view-subspace-clustering']	['computer-vision']	[-3.11433524e-01 -3.77360761e-01 -8.69271457e-02 -2.85548091e-01 -7.37079501e-01 -9.23381031e-01 3.57518226e-01 -4.67121363e-01 -1.63409710e-01 2.06268966e-01 5.95563114e-01 3.12962800e-01 -5.40422678e-01 -1.98731676e-01 -3.47119987e-01 -1.01976001e+00 1.36292443e-01 5.96023142e-01 -1.17504328e-01 2.80393124e-01 2.13993236e-01 3.58204514e-01 -1.25560641e+00 4.71079439e-01 6.92039073e-01 3.18118155e-01 1.49753923e-02 4.76199538e-01 2.86926031e-01 2.73890108e-01 1.49222955e-01 1.22258157e-01 5.67598104e-01 -4.30462867e-01 -6.73966289e-01 7.95097709e-01 4.63188410e-01 6.92070574e-02 -1.72647491e-01 1.05589914e+00 4.65236157e-01 3.12662095e-01 7.78106272e-01 -1.38273573e+00 -3.39546531e-01 2.18915775e-01 -9.28557515e-01 -1.06163643e-01 3.28740954e-01 -4.27536547e-01 9.87099171e-01 -1.27848208e+00 8.16412508e-01 1.10073543e+00 3.18359882e-01 2.30044663e-01 -1.67345357e+00 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2d2c7abd-e5ee-4f49-8e8f-e9e39413b550	improving-hyperspectral-adversarial	2210.16346	null	https://arxiv.org/abs/2210.16346v4	https://arxiv.org/pdf/2210.16346v4.pdf	Improving Hyperspectral Adversarial Robustness Under Multiple Attacks	Semantic segmentation models classifying hyperspectral images (HSI) are vulnerable to adversarial examples. Traditional approaches to adversarial robustness focus on training or retraining a single network on attacked data, however, in the presence of multiple attacks these approaches decrease in performance compared to networks trained individually on each attack. To combat this issue we propose an Adversarial Discriminator Ensemble Network (ADE-Net) which focuses on attack type detection and adversarial robustness under a unified model to preserve per data-type weight optimally while robustifiying the overall network. In the proposed method, a discriminator network is used to separate data by attack type into their specific attack-expert ensemble network.	['Salimeh Yasaei Sekeh', 'Nicholas Soucy']	2022-10-28	null	null	null	null	['type']	['speech']	[ 8.40007663e-01 1.37028903e-01 2.03362346e-01 -2.12881982e-01 -5.66694498e-01 -1.27269804e+00 4.22577173e-01 -8.66044536e-02 -1.46791562e-01 6.06693387e-01 -3.63582015e-01 -3.82646590e-01 -1.35474965e-01 -1.07182586e+00 -6.12854302e-01 -9.50303912e-01 7.68305585e-02 1.41145706e-01 2.67242789e-01 -2.22526550e-01 1.08935267e-01 9.99104202e-01 -9.65839863e-01 2.63563633e-01 1.26035285e+00 1.03654540e+00 -5.81683993e-01 9.29351330e-01 2.39643931e-01 7.50005960e-01 -1.05222189e+00 -4.00706291e-01 8.13699961e-01 -3.96700054e-01 -7.36492217e-01 -1.95281934e-02 8.08584630e-01 -1.94180742e-01 -5.75355768e-01 1.71308863e+00 4.82644588e-01 2.78983951e-01 8.26513767e-01 -1.63706279e+00 -7.71831632e-01 6.62398100e-01 -2.51754463e-01 1.10929310e-01 -1.63334534e-02 4.33824837e-01 5.32212734e-01 -1.73000544e-01 1.92139015e-01 1.02563810e+00 7.80028403e-01 7.59266913e-01 -1.22513139e+00 -9.44904089e-01 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ed405372-d502-45a3-8dae-04d0d9745dad	towards-accurate-deceptive-opinion-spam	1711.09181	null	http://arxiv.org/abs/1711.09181v2	http://arxiv.org/pdf/1711.09181v2.pdf	Towards Accurate Deceptive Opinion Spam Detection based on Word Order-preserving CNN	Nowadays, deep learning has been widely used. In natural language learning, the analysis of complex semantics has been achieved because of its high degree of flexibility. The deceptive opinions detection is an important application area in deep learning model, and related mechanisms have been given attention and researched. On-line opinions are quite short, varied types and content. In order to effectively identify deceptive opinions, we need to comprehensively study the characteristics of deceptive opinions, and explore novel characteristics besides the textual semantics and emotional polarity that have been widely used in text analysis. The detection mechanism based on deep learning has better self-adaptability and can effectively identify all kinds of deceptive opinions. In this paper, we optimize the convolution neural network model by embedding the word order characteristics in its convolution layer and pooling layer, which makes convolution neural network more suitable for various text classification and deceptive opinions detection. The TensorFlow-based experiments demonstrate that the detection mechanism proposed in this paper achieve more accurate deceptive opinion detection results.	['Limin Liu', 'Mengjie Guo', 'Siyuan Zhao', 'Zhiwei Xu']	2017-11-25	null	null	null	null	['spam-detection']	['natural-language-processing']	[-4.65165347e-01 -7.20261157e-01 1.10090628e-01 -5.71906030e-01 3.13549787e-01 -4.48324412e-01 4.10657823e-01 2.33633891e-01 -5.01822412e-01 4.19640511e-01 2.05639973e-01 -2.56859481e-01 2.08652362e-01 -8.03110778e-01 -1.92745719e-02 -7.85069346e-01 3.48567218e-01 -7.57877529e-03 3.63398381e-02 -6.47779167e-01 8.56455028e-01 5.09783268e-01 -1.22150898e+00 6.58332229e-01 1.27105510e+00 1.40572453e+00 -4.79990870e-01 4.02515501e-01 -3.77987087e-01 1.31566596e+00 -1.20877874e+00 -8.34384441e-01 -3.68196249e-01 -3.18956226e-01 -5.91611922e-01 -1.16353653e-01 1.11550637e-01 -6.12973809e-01 -4.75473285e-01 1.46715319e+00 7.79283345e-01 -9.84152406e-02 8.01618636e-01 -1.21805620e+00 -1.25637662e+00 4.46960986e-01 -5.89747608e-01 5.99272013e-01 1.34093031e-01 5.84627502e-02 5.94378531e-01 -9.46514904e-01 -1.93381086e-01 1.58559191e+00 3.75007719e-01 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104c634e-38fd-4259-82b4-0ae87e9dc174	tracking-small-and-fast-moving-objects-a	2209.04284	null	https://arxiv.org/abs/2209.04284v1	https://arxiv.org/pdf/2209.04284v1.pdf	Tracking Small and Fast Moving Objects: A Benchmark	With more and more large-scale datasets available for training, visual tracking has made great progress in recent years. However, current research in the field mainly focuses on tracking generic objects. In this paper, we present TSFMO, a benchmark for \textbf{T}racking \textbf{S}mall and \textbf{F}ast \textbf{M}oving \textbf{O}bjects. This benchmark aims to encourage research in developing novel and accurate methods for this challenging task particularly. TSFMO consists of 250 sequences with about 50k frames in total. Each frame in these sequences is carefully and manually annotated with a bounding box. To the best of our knowledge, TSFMO is the first benchmark dedicated to tracking small and fast moving objects, especially connected to sports. To understand how existing methods perform and to provide comparison for future research on TSFMO, we extensively evaluate 20 state-of-the-art trackers on the benchmark. The evaluation results exhibit that more effort are required to improve tracking small and fast moving objects. Moreover, to encourage future research, we proposed a novel tracker S-KeepTrack which surpasses all 20 evaluated approaches. By releasing TSFMO, we expect to facilitate future researches and applications of tracking small and fast moving objects. The TSFMO and evaluation results as well as S-KeepTrack are available at \url{https://github.com/CodeOfGithub/S-KeepTrack}.	['Shuiwang Li', 'Jingdong Liang', 'Yuming Qiu', 'Fuliang Wu', 'Zhewen Zhang']	2022-09-09	null	null	null	null	['visual-tracking']	['computer-vision']	[-2.49096468e-01 -5.53408325e-01 -4.33737487e-01 -6.28050044e-02 -2.80235261e-01 -6.03680849e-01 2.65515447e-01 -1.41280249e-01 -4.03844297e-01 6.45046413e-01 -1.77343607e-01 -2.03927413e-01 1.51655912e-01 -3.70018423e-01 -8.02808642e-01 -5.70108533e-01 -3.71862650e-01 3.35105538e-01 1.01894081e+00 -2.35127673e-01 -1.63115337e-02 4.94796306e-01 -1.69059682e+00 1.25462919e-01 5.58355212e-01 9.40355301e-01 4.62176770e-01 8.18951130e-01 1.55314490e-01 6.48579240e-01 -7.81846285e-01 -6.81304216e-01 4.11909074e-01 -1.86587065e-01 -6.22105062e-01 -2.24996254e-01 1.04377270e+00 -2.58102000e-01 -5.63160598e-01 1.10751724e+00 6.29402339e-01 3.45135599e-01 1.26562029e-01 -1.58046067e+00 -4.70599979e-01 5.55816054e-01 -5.58023512e-01 9.09237564e-01 3.48854691e-01 4.76625025e-01 4.99873936e-01 -7.20413625e-01 7.47930825e-01 1.02690649e+00 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d69a00e8-85e4-4086-80e5-54a836260360	crovia-seeing-drone-scenes-from-car	2304.07199	null	https://arxiv.org/abs/2304.07199v1	https://arxiv.org/pdf/2304.07199v1.pdf	CROVIA: Seeing Drone Scenes from Car Perspective via Cross-View Adaptation	Understanding semantic scene segmentation of urban scenes captured from the Unmanned Aerial Vehicles (UAV) perspective plays a vital role in building a perception model for UAV. With the limitations of large-scale densely labeled data, semantic scene segmentation for UAV views requires a broad understanding of an object from both its top and side views. Adapting from well-annotated autonomous driving data to unlabeled UAV data is challenging due to the cross-view differences between the two data types. Our work proposes a novel Cross-View Adaptation (CROVIA) approach to effectively adapt the knowledge learned from on-road vehicle views to UAV views. First, a novel geometry-based constraint to cross-view adaptation is introduced based on the geometry correlation between views. Second, cross-view correlations from image space are effectively transferred to segmentation space without any requirement of paired on-road and UAV view data via a new Geometry-Constraint Cross-View (GeiCo) loss. Third, the multi-modal bijective networks are introduced to enforce the global structural modeling across views. Experimental results on new cross-view adaptation benchmarks introduced in this work, i.e., SYNTHIA to UAVID and GTA5 to UAVID, show the State-of-the-Art (SOTA) performance of our approach over prior adaptation methods	['Khoa Luu', 'Jackson Cothren', 'Son Lam Phung', 'Ashley Dowling', 'Chi Nhan Duong', 'Thanh-Dat Truong']	2023-04-14	null	null	null	null	['scene-segmentation']	['computer-vision']	[ 1.71943545e-01 5.77748232e-02 4.22926992e-02 -6.88601077e-01 -4.15390998e-01 -9.80182528e-01 4.21086311e-01 -5.12083173e-01 -1.99081600e-01 4.32604879e-01 -2.97143698e-01 -3.76221240e-02 -8.26410353e-02 -9.12219584e-01 -1.08356130e+00 -4.16977346e-01 4.49480683e-01 4.04485852e-01 6.66661918e-01 -4.07685846e-01 -2.93589294e-01 4.68354434e-01 -1.53797650e+00 7.05729425e-02 1.01870978e+00 9.63450134e-01 5.47388375e-01 4.58952218e-01 9.36214346e-03 2.62728810e-01 6.07708134e-02 -3.66454184e-01 9.55576956e-01 -9.01808143e-02 -9.29458737e-01 7.58885920e-01 9.15383637e-01 -3.47797960e-01 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4d621c7c-409c-46e0-9ce6-7b91c5c0ead2	robust-data-driven-approach-for-predicting	1908.03665	null	https://arxiv.org/abs/1908.03665v1	https://arxiv.org/pdf/1908.03665v1.pdf	Robust data-driven approach for predicting the configurational energy of high entropy alloys	High entropy alloys (HEAs) have been increasingly attractive as promising next-generation materials due to their various excellent properties. It's necessary to essentially characterize the degree of chemical ordering and identify order-disorder transitions through efficient simulation and modeling of thermodynamics. In this study, a robust data-driven framework based on Bayesian approaches is proposed and demonstrated on the accurate and efficient prediction of configurational energy of high entropy alloys. The proposed effective pair interaction (EPI) model with ensemble sampling is used to map the configuration and its corresponding energy. Given limited data calculated by first-principles calculations, Bayesian regularized regression not only offers an accurate and stable prediction but also effectively quantifies the uncertainties associated with EPI parameters. Compared with the arbitrary determination of model complexity, we further conduct a physical feature selection to identify the truncation of coordination shells in EPI model using Bayesian information criterion. The results achieve efficient and robust performance in predicting the configurational energy, particularly given small data. The developed methodology is applied to study a series of refractory HEAs, i.e. NbMoTaW, NbMoTaWV and NbMoTaWTi where it is demonstrated how dataset size affects the confidence we can place in statistical estimates of configurational energy when data are sparse.	['Junqi Yin', 'Markus Eisenbach', 'Sirui Bi', 'Jiaxin Zhang', 'Guannan Zhang', 'Xianglin Liu']	2019-08-10	null	null	null	null	['small-data']	['computer-vision']	[-1.30645391e-02 -1.40503347e-01 1.00490250e-01 -3.12400281e-01 -7.96481192e-01 1.34553686e-01 5.70851803e-01 1.66014254e-01 -3.02800059e-01 1.22809184e+00 -7.88813904e-02 1.60679054e-02 -7.55016387e-01 -5.55410743e-01 -3.58550668e-01 -1.27757251e+00 9.13471952e-02 1.07219732e+00 4.51960951e-01 -3.58793825e-01 6.88270032e-01 4.19120908e-01 -1.91478169e+00 2.12277193e-02 1.10193574e+00 1.04944658e+00 5.69206297e-01 2.38021761e-01 2.85579681e-01 2.78315306e-01 -1.70978099e-01 1.30049005e-01 -7.76095092e-02 -4.01271321e-02 -6.71195626e-01 -4.05307829e-01 -3.54262412e-01 -1.41002983e-02 3.38823460e-02 7.33259678e-01 5.22716284e-01 2.55177528e-01 1.38351357e+00 -6.85521960e-01 -1.37669355e-01 6.88273907e-01 -2.91629940e-01 -9.17191133e-02 2.45076865e-01 2.04136580e-01 9.82810676e-01 -8.57388735e-01 6.44143939e-01 7.13591814e-01 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