Split (2)

train · 44.6k rows

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068d65db-2012-4a5d-ac52-53f70b90f58b	a-cap-anticipation-captioning-with	2304.06602	null	https://arxiv.org/abs/2304.06602v1	https://arxiv.org/pdf/2304.06602v1.pdf	A-CAP: Anticipation Captioning with Commonsense Knowledge	Humans possess the capacity to reason about the future based on a sparse collection of visual cues acquired over time. In order to emulate this ability, we introduce a novel task called Anticipation Captioning, which generates a caption for an unseen oracle image using a sparsely temporally-ordered set of images. To tackle this new task, we propose a model called A-CAP, which incorporates commonsense knowledge into a pre-trained vision-language model, allowing it to anticipate the caption. Through both qualitative and quantitative evaluations on a customized visual storytelling dataset, A-CAP outperforms other image captioning methods and establishes a strong baseline for anticipation captioning. We also address the challenges inherent in this task.	['Hideki Nakayama', 'Akihiro Sugimoto', 'Quoc-An Luong', 'Duc Minh Vo']	2023-04-13	null	http://openaccess.thecvf.com//content/CVPR2023/html/Vo_A-Cap_Anticipation_Captioning_With_Commonsense_Knowledge_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Vo_A-Cap_Anticipation_Captioning_With_Commonsense_Knowledge_CVPR_2023_paper.pdf	cvpr-2023-1	['visual-storytelling']	['natural-language-processing']	[ 6.85383856e-01 5.69933593e-01 -1.66354305e-03 -5.22268891e-01 -7.17937350e-01 -6.20409667e-01 1.06493175e+00 -1.26115292e-01 -1.71660319e-01 6.67702079e-01 7.55928278e-01 -1.45177305e-01 5.16869545e-01 -3.09318602e-01 -1.27388728e+00 -5.20875938e-02 2.24916533e-01 4.35167789e-01 -1.22177057e-01 -6.16451465e-02 3.96576226e-02 -8.31132010e-02 -1.38640738e+00 7.63407111e-01 6.18519366e-01 9.17955339e-01 7.22129405e-01 6.62625968e-01 2.83892244e-01 1.32833612e+00 -1.20504871e-01 -4.64714974e-01 1.07746050e-01 -4.92875844e-01 -9.09873784e-01 4.09471035e-01 4.73834008e-01 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bb3af8c9-e7e2-43ef-9655-92eb46bcad40	learning-to-relate-to-previous-turns-in	2306.02553	null	https://arxiv.org/abs/2306.02553v1	https://arxiv.org/pdf/2306.02553v1.pdf	Learning to Relate to Previous Turns in Conversational Search	Conversational search allows a user to interact with a search system in multiple turns. A query is strongly dependent on the conversation context. An effective way to improve retrieval effectiveness is to expand the current query with historical queries. However, not all the previous queries are related to, and useful for expanding the current query. In this paper, we propose a new method to select relevant historical queries that are useful for the current query. To cope with the lack of labeled training data, we use a pseudo-labeling approach to annotate useful historical queries based on their impact on the retrieval results. The pseudo-labeled data are used to train a selection model. We further propose a multi-task learning framework to jointly train the selector and the retriever during fine-tuning, allowing us to mitigate the possible inconsistency between the pseudo labels and the changed retriever. Extensive experiments on four conversational search datasets demonstrate the effectiveness and broad applicability of our method compared with several strong baselines.	['Yang Liu', 'Peng Li', 'Yutao Zhu', 'Kelong Mao', 'Kaiyu Huang', 'Jian-Yun Nie', 'Fengran Mo']	2023-06-05	null	null	null	null	['conversational-search']	['natural-language-processing']	[ 1.05490178e-01 -2.27439731e-01 -4.81814444e-01 -5.12611628e-01 -1.31036055e+00 -8.36434841e-01 7.59127855e-01 5.46071976e-02 -5.08565426e-01 7.14932561e-01 6.15089893e-01 -1.40014244e-02 -1.91300213e-01 -5.80161393e-01 -4.46274608e-01 -4.89588916e-01 3.33006948e-01 7.93205857e-01 4.57808942e-01 -5.24311662e-01 3.48998010e-01 2.21621692e-01 -1.51101649e+00 5.95746696e-01 9.78706062e-01 6.84282601e-01 5.98520398e-01 4.24446255e-01 -2.15853482e-01 6.37420952e-01 -8.79343092e-01 -1.36000887e-01 -4.49998342e-02 -3.92999053e-01 -1.24986947e+00 -1.12942718e-01 1.10390283e-01 -3.89739275e-01 -3.73862565e-01 4.79571044e-01 5.78147829e-01 5.86460710e-01 3.02711040e-01 -1.02044463e+00 -1.70448303e-01 7.33560383e-01 -7.57861510e-02 3.68426174e-01 7.12517321e-01 -2.19046772e-01 1.31523180e+00 -8.49887550e-01 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22e5f242-7aa2-414b-8235-ef6b761c5011	self-prompting-large-language-models-for-open	2212.08635	null	https://arxiv.org/abs/2212.08635v2	https://arxiv.org/pdf/2212.08635v2.pdf	Self-Prompting Large Language Models for Zero-Shot Open-Domain QA	Open-Domain Question Answering (ODQA) aims at answering factoid questions without explicitly providing specific background documents. In a zero-shot setting, this task is more challenging since no data is available to train customized models like Retriever-Readers. Recently, Large Language Models (LLMs) like GPT-3 have shown their power in zero-shot ODQA with direct prompting methods, but these methods are still far from releasing the full powerfulness of LLMs only in an implicitly invoking way. In this paper, we propose a Self-Prompting framework to explicitly utilize the massive knowledge stored in the parameters of LLMs and their strong instruction understanding abilities. Concretely, we prompt LLMs step by step to generate multiple pseudo QA pairs with background passages and explanations from scratch and then use those generated elements for in-context learning. Experimental results show our method surpasses previous SOTA methods significantly on three widely-used ODQA datasets, and even achieves comparable performance with some Retriever-Reader models fine-tuned on full training data.	['Hai Zhao', 'Zhuosheng Zhang', 'Junlong Li']	2022-12-16	null	null	null	null	['open-domain-question-answering']	['natural-language-processing']	[ 1.37728602e-01 4.91989285e-01 3.34416665e-02 -2.54535377e-01 -1.41536844e+00 -8.16747963e-01 6.26356602e-01 2.32272387e-01 -4.40992147e-01 7.38756895e-01 4.04712588e-01 -7.16234326e-01 -1.33817524e-01 -9.65137482e-01 -8.28790665e-01 -2.61790633e-01 4.13457960e-01 1.00860786e+00 8.84256124e-01 -1.15440559e+00 1.80102929e-01 -1.02893412e-01 -1.61745310e+00 5.67209601e-01 1.62791467e+00 5.20762742e-01 6.83925271e-01 9.01031494e-01 -8.06440055e-01 1.48171616e+00 -6.05657041e-01 -6.58393085e-01 -3.08491230e-01 -5.15646875e-01 -1.24571002e+00 -4.40650761e-01 6.39372766e-01 -6.91728115e-01 -3.47856551e-01 5.86633503e-01 6.43772423e-01 5.47963023e-01 3.85060698e-01 -7.15707362e-01 -1.38161898e+00 8.98361266e-01 -2.85567008e-02 3.00723046e-01 6.56350315e-01 3.09875488e-01 1.07986891e+00 -8.17068756e-01 4.35925215e-01 1.23501611e+00 1.60779715e-01 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9b7c5b98-2340-45b8-95cc-6da2a079fd99	cholectriplet2022-show-me-a-tool-and-tell-me	2302.06294	null	https://arxiv.org/abs/2302.06294v1	https://arxiv.org/pdf/2302.06294v1.pdf	CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection	Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. The benefit is that this formalization helps to obtain a more detailed understanding of tool-tissue interaction which can be used to develop better Artificial Intelligence assistance for image-guided surgery. Earlier efforts and the CholecTriplet challenge introduced in 2021 have put together techniques aimed at recognizing these triplets from surgical footage. Estimating also the spatial locations of the triplets would offer a more precise intraoperative context-aware decision support for computer-assisted intervention. This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection. It includes weakly-supervised bounding box localization of every visible surgical instrument (or tool), as the key actors, and the modeling of each tool-activity in the form of <instrument, verb, target> triplet. The paper describes a baseline method and 10 new deep learning algorithms presented at the challenge to solve the task. It also provides thorough methodological comparisons of the methods, an in-depth analysis of the obtained results, their significance, and useful insights for future research directions and applications in surgery.	['Nicolas Padoy', 'Didier Mutter', 'Cristians Gonzalez', 'Barbara Seeliger', 'Pietro Mascagni', 'Nassir Navab', 'Lena Maier-Hein', 'Eduard Vazquez', 'Estevão Lima', 'Jan-Hinrich Nölke', 'Jaime Fonseca', 'Thuy Nuong Tran', 'Sudarshan Regmi', 'Pedro Morais', 'Patrick Godau', 'Max Berniker', 'Shrawan Kumar Thapa', 'Debdoot Sheet', 'Zhenkun Wang', 'Tobias Czempiel', 'João L. Vilaça', 'Melanie Schellenberg', 'Guo Rui', 'Ziheng Wang', 'Binod Bhattarai', 'Pranav Poudel', 'Rachana Sathish', 'Sista Raviteja', 'Han Li', 'Shuangchun Gui', 'Ege Özsoy', 'Felix Holm', 'Satoshi Kasai', 'Satoshi Kondo', 'Helena R. Torres', 'Bruno Oliveira', 'Guoyan Zheng', 'Xiaoyang Zou', 'Finn-Henri Smidt', 'Amine Yamlahi', 'Wolfgang Reiter', 'Jonas Hajek', 'Kun Yuan', 'Armine Vardazaryan', 'Deepak Alapatt', 'Aditya Murali', 'Saurav Sharma', 'Tong Yu', 'Chinedu Innocent Nwoye']	2023-02-13	null	null	null	null	['action-triplet-detection', 'action-triplet-recognition', 'surgical-tool-detection']	['computer-vision', 'computer-vision', 'computer-vision']	[ 1.59230277e-01 3.07061493e-01 -6.61856294e-01 4.45197560e-02 -9.78279650e-01 -7.16131032e-01 6.03557646e-01 2.85788298e-01 -4.42774594e-01 2.84368128e-01 5.63995123e-01 -5.27685583e-01 -5.00107408e-01 -6.10701479e-02 -5.02689481e-01 -8.21472943e-01 -3.09075862e-01 5.58423638e-01 -1.41138390e-01 5.28763458e-02 5.10140300e-01 8.95692348e-01 -1.16030705e+00 5.63125491e-01 2.15150774e-01 1.14922941e+00 3.91477853e-01 5.21906734e-01 -4.43154387e-02 9.12874460e-01 -6.03408456e-01 3.41389291e-02 3.41932148e-01 -3.24442774e-01 -8.50378931e-01 -2.04275414e-01 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a3eb2492-71f3-4aaf-a9dc-e45732f9c195	textless-direct-speech-to-speech-translation	2211.00115	null	https://arxiv.org/abs/2211.00115v1	https://arxiv.org/pdf/2211.00115v1.pdf	Textless Direct Speech-to-Speech Translation with Discrete Speech Representation	Research on speech-to-speech translation (S2ST) has progressed rapidly in recent years. Many end-to-end systems have been proposed and show advantages over conventional cascade systems, which are often composed of recognition, translation and synthesis sub-systems. However, most of the end-to-end systems still rely on intermediate textual supervision during training, which makes it infeasible to work for languages without written forms. In this work, we propose a novel model, Textless Translatotron, which is based on Translatotron 2, for training an end-to-end direct S2ST model without any textual supervision. Instead of jointly training with an auxiliary task predicting target phonemes as in Translatotron 2, the proposed model uses an auxiliary task predicting discrete speech representations which are obtained from learned or random speech quantizers. When a speech encoder pre-trained with unsupervised speech data is used for both models, the proposed model obtains translation quality nearly on-par with Translatotron 2 on the multilingual CVSS-C corpus as well as the bilingual Fisher Spanish-English corpus. On the latter, it outperforms the prior state-of-the-art textless model by +18.5 BLEU.	['Chung-Cheng Chiu', 'Ye Jia', 'Xinjian Li']	2022-10-31	null	null	null	null	['speech-to-speech-translation']	['speech']	[ 3.28194618e-01 1.25412136e-01 -3.39099139e-01 -4.58457500e-01 -1.37961483e+00 -3.84572804e-01 7.30401993e-01 -3.81954938e-01 -5.03903925e-01 6.61691606e-01 1.94968686e-01 -7.99966872e-01 8.34040225e-01 -2.24187687e-01 -7.46610701e-01 -4.91589874e-01 7.48076022e-01 7.45906591e-01 4.10417002e-03 -4.73144084e-01 -3.22640121e-01 -1.52468801e-01 -8.34735155e-01 5.10405242e-01 1.14175618e+00 8.50254655e-01 5.84210992e-01 6.45555913e-01 -4.66409177e-02 6.76892400e-01 -4.77474093e-01 -7.71446407e-01 1.17660932e-01 -1.01428998e+00 -4.76278692e-01 -2.22657043e-02 3.41766804e-01 -2.77366102e-01 -5.47188580e-01 1.13003373e+00 7.72595704e-01 -3.50232087e-02 6.61296725e-01 -8.36483538e-01 -9.05243933e-01 9.54454958e-01 -1.67224973e-01 8.63534659e-02 5.40012754e-02 3.01478487e-02 1.05531633e+00 -1.52148557e+00 4.24924672e-01 1.53101671e+00 3.14576805e-01 7.40235627e-01 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-1.44392872e+00 -1.67542458e-01 -1.32272080e-01 6.25611961e-01 -3.11391115e-01 -2.55726904e-01 2.60806888e-01]	[14.517621994018555, 7.15815544128418]
f1a48bd4-66f5-4647-b33c-1f823783192b	measuring-and-modeling-the-motor-system-with	2103.11775	null	https://arxiv.org/abs/2103.11775v1	https://arxiv.org/pdf/2103.11775v1.pdf	Measuring and modeling the motor system with machine learning	The utility of machine learning in understanding the motor system is promising a revolution in how to collect, measure, and analyze data. The field of movement science already elegantly incorporates theory and engineering principles to guide experimental work, and in this review we discuss the growing use of machine learning: from pose estimation, kinematic analyses, dimensionality reduction, and closed-loop feedback, to its use in understanding neural correlates and untangling sensorimotor systems. We also give our perspective on new avenues where markerless motion capture combined with biomechanical modeling and neural networks could be a new platform for hypothesis-driven research.	['Mackenzie W. Mathis', 'Alexander Mathis', 'Alessandro Marin Vargas', 'Sébastien B. Hausmann']	2021-03-22	null	null	null	null	['markerless-motion-capture']	['computer-vision']	[ 9.01549682e-02 -1.62857711e-01 -9.23147440e-01 1.67324111e-01 -3.47126514e-01 -4.38678116e-01 3.07677805e-01 -2.44555265e-01 -9.94638145e-01 7.89014399e-01 4.40386027e-01 -3.11647832e-01 -3.50977719e-01 -2.34890372e-01 -6.35638177e-01 -3.37239057e-01 -4.35844541e-01 9.62949917e-02 1.61320284e-01 -5.13195336e-01 4.36919093e-01 6.42727554e-01 -1.26911128e+00 -1.06359124e-01 4.30054396e-01 2.95150667e-01 4.95415390e-01 6.45535588e-01 6.17297351e-01 4.47490007e-01 -9.85142887e-02 1.54377997e-01 1.05382174e-01 -4.24775869e-01 -5.28375626e-01 -2.63521135e-01 3.28186303e-01 -3.89280498e-01 -5.36326110e-01 5.05362511e-01 8.23955297e-01 4.72203463e-01 5.32909453e-01 -9.81977344e-01 -4.06737775e-01 2.80155480e-01 -2.65028208e-01 5.55828989e-01 4.18060839e-01 5.51943898e-01 5.23103178e-01 -4.27181840e-01 7.97542334e-01 1.02054667e+00 6.68066561e-01 7.39482880e-01 -1.17448080e+00 -3.20813388e-01 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-1.32692206e+00 -3.66557128e-04 -1.37195745e-02]	[6.949415683746338, 0.06248697638511658]
c2735268-09ac-4a32-aee2-db767bbf9d42	controllable-continuous-gaze-redirection	2010.04513	null	https://arxiv.org/abs/2010.04513v1	https://arxiv.org/pdf/2010.04513v1.pdf	Controllable Continuous Gaze Redirection	In this work, we present interpGaze, a novel framework for controllable gaze redirection that achieves both precise redirection and continuous interpolation. Given two gaze images with different attributes, our goal is to redirect the eye gaze of one person into any gaze direction depicted in the reference image or to generate continuous intermediate results. To accomplish this, we design a model including three cooperative components: an encoder, a controller and a decoder. The encoder maps images into a well-disentangled and hierarchically-organized latent space. The controller adjusts the magnitudes of latent vectors to the desired strength of corresponding attributes by altering a control vector. The decoder converts the desired representations from the attribute space to the image space. To facilitate covering the full space of gaze directions, we introduce a high-quality gaze image dataset with a large range of directions, which also benefits researchers in related areas. Extensive experimental validation and comparisons to several baseline methods show that the proposed interpGaze outperforms state-of-the-art methods in terms of image quality and redirection precision.	['Wensen Feng', 'Jing-Hao Xue', 'Yujiu Yang', 'Weihao Xia']	2020-10-09	null	null	null	null	['gaze-redirection']	['computer-vision']	[ 3.85604143e-01 8.96709040e-02 -3.01927447e-01 -6.72976017e-01 -2.76846915e-01 -3.23305458e-01 4.08053517e-01 -7.73581505e-01 2.38583405e-02 5.72218299e-01 3.41267318e-01 -1.18908323e-02 -4.19294052e-02 -3.81823689e-01 -5.80972254e-01 -8.62650990e-01 2.72877663e-01 -6.97880751e-03 -6.40305653e-02 -4.99324948e-02 5.66263378e-01 4.24102359e-02 -2.07858253e+00 3.54242362e-02 1.00485647e+00 9.89076436e-01 2.08245233e-01 5.37393928e-01 3.07793409e-01 6.77906752e-01 -2.98008174e-01 -2.62723923e-01 1.50139838e-01 -5.39511621e-01 -5.86338580e-01 1.87166601e-01 7.28825092e-01 -3.27126086e-01 -3.67773287e-02 1.11920905e+00 4.83251184e-01 -1.45830020e-01 6.98779166e-01 -1.63071239e+00 -1.40159941e+00 1.22183859e-01 -9.06463504e-01 -3.69643569e-02 6.92187786e-01 4.30371106e-01 8.77590358e-01 -7.56114662e-01 4.95505691e-01 1.35784709e+00 5.55375069e-02 8.45451415e-01 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5a7c10f9-776b-4c59-a509-50915047b04e	design-process-is-a-reinforcement-learning	2211.03136	null	https://arxiv.org/abs/2211.03136v1	https://arxiv.org/pdf/2211.03136v1.pdf	Design Process is a Reinforcement Learning Problem	While reinforcement learning has been used widely in research during the past few years, it found fewer real-world applications than supervised learning due to some weaknesses that the RL algorithms suffer from, such as performance degradation in transitioning from the simulator to the real world. Here, we argue the design process is a reinforcement learning problem and can potentially be a proper application for RL algorithms as it is an offline process and conventionally is done in CAD software - a sort of simulator. This creates opportunities for using RL methods and, at the same time, raises challenges. While the design processes are so diverse, here we focus on the space layout planning (SLP), frame it as an RL problem under the Markov Decision Process, and use PPO to address the layout design problem. To do so, we developed an environment named RLDesigner, to simulate the SLP. The RLDesigner is an OpenAI Gym compatible environment that can be easily customized to define a diverse range of design scenarios. We publicly share the environment to encourage both RL and architecture communities to use it for testing different RL algorithms or in their design practice. The codes are available in the following GitHub repository https://github.com/ RezaKakooee/rldesigner/tree/Second_Paper	['Benjamin Dillunberger', 'Reza kakooee']	2022-11-06	null	null	null	null	['layout-design']	['computer-vision']	[-3.10724258e-01 7.80696794e-02 -2.08664641e-01 -9.79301855e-02 -4.97361869e-01 -7.26216674e-01 2.03240305e-01 -2.29431540e-01 9.39765275e-02 7.94226170e-01 -3.42640765e-02 -8.19938004e-01 -2.20732823e-01 -8.44728351e-01 -4.61345464e-01 -5.07572532e-01 -2.16856048e-01 5.52312613e-01 3.00157100e-01 -2.84327090e-01 2.98295408e-01 6.50214791e-01 -1.61139035e+00 4.78206836e-02 4.65064883e-01 4.30345774e-01 3.47768009e-01 6.16228461e-01 1.78824719e-02 7.52775908e-01 -6.67253196e-01 3.32233042e-01 3.07898134e-01 -5.48950791e-01 -8.38021040e-01 -9.00553390e-02 -3.03310841e-01 -2.88614213e-01 -2.30047330e-01 4.02220279e-01 9.32418108e-01 -3.31828184e-02 2.56439745e-01 -1.54591060e+00 1.74668953e-01 6.21480703e-01 -3.57482970e-01 -1.71913698e-01 7.18298376e-01 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ded8b1b5-9ad3-4f43-9c08-3bb653c069f5	mixnerf-modeling-a-ray-with-mixture-density	2302.08788	null	https://arxiv.org/abs/2302.08788v2	https://arxiv.org/pdf/2302.08788v2.pdf	MixNeRF: Modeling a Ray with Mixture Density for Novel View Synthesis from Sparse Inputs	Neural Radiance Field (NeRF) has broken new ground in the novel view synthesis due to its simple concept and state-of-the-art quality. However, it suffers from severe performance degradation unless trained with a dense set of images with different camera poses, which hinders its practical applications. Although previous methods addressing this problem achieved promising results, they relied heavily on the additional training resources, which goes against the philosophy of sparse-input novel-view synthesis pursuing the training efficiency. In this work, we propose MixNeRF, an effective training strategy for novel view synthesis from sparse inputs by modeling a ray with a mixture density model. Our MixNeRF estimates the joint distribution of RGB colors along the ray samples by modeling it with mixture of distributions. We also propose a new task of ray depth estimation as a useful training objective, which is highly correlated with 3D scene geometry. Moreover, we remodel the colors with regenerated blending weights based on the estimated ray depth and further improves the robustness for colors and viewpoints. Our MixNeRF outperforms other state-of-the-art methods in various standard benchmarks with superior efficiency of training and inference.	['Nojun Kwak', 'Yeonjin Chang', 'Donghoon Han', 'Seunghyeon Seo']	2023-02-17	null	http://openaccess.thecvf.com//content/CVPR2023/html/Seo_MixNeRF_Modeling_a_Ray_With_Mixture_Density_for_Novel_View_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Seo_MixNeRF_Modeling_a_Ray_With_Mixture_Density_for_Novel_View_CVPR_2023_paper.pdf	cvpr-2023-1	['philosophy']	['miscellaneous']	[ 5.79523556e-02 -5.59687257e-01 3.28507572e-02 -4.12956834e-01 -6.55558527e-01 -4.23862934e-01 5.85223556e-01 -6.70359612e-01 -1.27485484e-01 5.54493964e-01 3.04274797e-01 2.04897132e-02 5.25281839e-02 -1.03687763e+00 -8.93012464e-01 -1.04512787e+00 5.46211421e-01 2.03319103e-01 1.14681326e-01 -2.93339342e-01 1.94134802e-01 5.35958886e-01 -1.56743014e+00 2.07160234e-01 8.95385325e-01 8.98579895e-01 3.35010886e-01 6.57528043e-01 -9.87315103e-02 8.64653766e-01 -4.78432178e-01 -5.33531725e-01 5.76891303e-01 -4.88481462e-01 -7.43866116e-02 1.05059175e-02 6.21671081e-01 -6.94923222e-01 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545706e8-5885-4f1f-8561-68b229f45e54	grammatical-error-detection-based-on-machine	null	null	https://aclanthology.org/W16-4918	https://aclanthology.org/W16-4918.pdf	Grammatical Error Detection Based on Machine Learning for Mandarin as Second Language Learning	Mandarin is not simple language for foreigner. Even using Mandarin as the mother tongue, they have to spend more time to learn when they were child. The following issues are the reason why causes learning problem. First, the word is envolved by Hieroglyphic. So a character can express meanings independently, but become a word has another semantic. Second, the Mandarin{'}s grammars have flexible rule and special usage. Therefore, the common grammatical errors can classify to missing, redundant, selection and disorder. In this paper, we proposed the structure of the Recurrent Neural Networks using Long Short-term memory (RNN-LSTM). It can detect the error type from the foreign learner writing. The features based on the word vector and part-of-speech vector. In the test data found that our method in the detection level of recall better than the others, even as high as 0.9755. That is because we give the possibility of greater choice in detecting errors.	['Chan-Kun Yeh', 'Tsung-Wei Hsu', 'Jui-Feng Yeh']	2016-12-01	null	null	null	ws-2016-12	['grammatical-error-detection']	['natural-language-processing']	[-1.87073052e-01 -1.95665926e-01 -5.72683811e-02 -2.42084846e-01 -2.33300939e-01 -3.56409192e-01 6.06281720e-02 -1.81891650e-01 -5.43754041e-01 9.04037833e-01 2.36812234e-01 -3.73930991e-01 3.34254727e-02 -8.42881620e-01 -7.20631003e-01 -5.85840046e-01 2.87291497e-01 1.53978437e-01 2.81334370e-01 -5.53595722e-01 5.57496011e-01 1.84633449e-01 -1.68146861e+00 3.75440925e-01 1.17108810e+00 6.80745959e-01 8.92569721e-01 1.89488187e-01 -7.70303786e-01 1.16489339e+00 -1.04712832e+00 -1.40931875e-01 -1.76447064e-01 -7.47244298e-01 -7.05968380e-01 -3.31773043e-01 7.64412852e-03 -2.90236861e-01 -1.55958831e-01 1.43308151e+00 5.14563680e-01 1.21200964e-01 5.67171216e-01 -5.80911517e-01 -1.17511559e+00 1.04553461e+00 -9.68035161e-02 4.05095339e-01 4.82641637e-01 -2.56863415e-01 3.88988644e-01 -9.92676318e-01 4.57089067e-01 1.41188085e+00 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83f92a54-a87c-4e77-bd31-cba082a00bed	hierarchically-fusing-long-and-short-term	2304.02089	null	https://arxiv.org/abs/2304.02089v1	https://arxiv.org/pdf/2304.02089v1.pdf	Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search	Estimating Click-Through Rate (CTR) is a vital yet challenging task in personalized product search. However, existing CTR methods still struggle in the product search settings due to the following three challenges including how to more effectively extract users' short-term interests with respect to multiple aspects, how to extract and fuse users' long-term interest with short-term interests, how to address the entangling characteristic of long and short-term interests. To resolve these challenges, in this paper, we propose a new approach named Hierarchical Interests Fusing Network (HIFN), which consists of four basic modules namely Short-term Interests Extractor (SIE), Long-term Interests Extractor (LIE), Interests Fusion Module (IFM) and Interests Disentanglement Module (IDM). Specifically, SIE is proposed to extract user's short-term interests by integrating three fundamental interests encoders within it namely query-dependent, target-dependent and causal-dependent interest encoder, respectively, followed by delivering the resultant representation to the module LIE, where it can effectively capture user long-term interests by devising an attention mechanism with respect to the short-term interests from SIE module. In IFM, the achieved long and short-term interests are further fused in an adaptive manner, followed by concatenating it with original raw context features for the final prediction result. Last but not least, considering the entangling characteristic of long and short-term interests, IDM further devises a self-supervised framework to disentangle long and short-term interests. Extensive offline and online evaluations on a real-world e-commerce platform demonstrate the superiority of HIFN over state-of-the-art methods.	['Qi Rao', 'Jing Zhang', 'Hong Wen', 'Qijie Shen']	2023-04-04	null	null	null	null	['click-through-rate-prediction']	['miscellaneous']	[ 8.36042091e-02 -1.23042285e-01 -6.92407966e-01 -5.77847958e-01 -7.37164319e-01 -3.82963777e-01 8.49878132e-01 -1.53292179e-01 -2.91387647e-01 4.89332616e-01 7.07734764e-01 -8.29697326e-02 -5.03521681e-01 -7.70983040e-01 -3.93570930e-01 -3.47072810e-01 -4.72811945e-02 2.66997397e-01 2.22633481e-02 -4.04479802e-01 3.37133318e-01 8.55827332e-02 -1.85304952e+00 2.31478870e-01 1.24050438e+00 1.36623216e+00 3.22753668e-01 3.57348144e-01 -2.36645833e-01 7.41024315e-01 -2.61157751e-01 -3.58793795e-01 7.84739181e-02 -2.40779236e-01 -7.81300187e-01 -2.32102677e-01 3.36554162e-02 -4.52431560e-01 -4.17050302e-01 6.08255088e-01 6.20534897e-01 3.51253331e-01 3.94233912e-01 -1.13408947e+00 -8.52855980e-01 6.28541350e-01 -4.93011177e-01 1.05281256e-01 4.41454113e-01 3.48027088e-02 1.53047085e+00 -8.65093946e-01 5.08964598e-01 1.13540030e+00 8.57714266e-02 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-7.15562642e-01 5.42229533e-01 2.72691518e-01 -6.00173891e-01 -6.32346690e-01 -2.98215449e-01]	[10.16010856628418, 5.576891899108887]
9f612edc-6e97-4345-9ae2-10cb6923ea54	indoor-sound-source-localization-with	1712.07814	null	http://arxiv.org/abs/1712.07814v1	http://arxiv.org/pdf/1712.07814v1.pdf	Indoor Sound Source Localization with Probabilistic Neural Network	It is known that adverse environments such as high reverberation and low signal-to-noise ratio (SNR) pose a great challenge to indoor sound source localization. To address this challenge, in this paper, we propose a sound source localization algorithm based on probabilistic neural network, namely Generalized cross correlation Classification Algorithm (GCA). Experimental results for adverse environments with high reverberation time T60 up to 600ms and low SNR such as -10dB show that, the average azimuth angle error and elevation angle error by GCA are only 4.6 degrees and 3.1 degrees respectively. Compared with three recently published algorithms, GCA has increased the success rate on direction of arrival estimation significantly with good robustness to environmental changes. These results show that the proposed GCA can localize accurately and robustly for diverse indoor applications where the site acoustic features can be studied prior to the localization stage.	['Susanto Rahardja', 'Jiajia Chen', 'Yingxiang Sun', 'Chau Yuen']	2017-12-21	null	null	null	null	['direction-of-arrival-estimation']	['audio']	[ 3.22545110e-03 -7.98017740e-01 7.48681664e-01 -1.81829497e-01 -1.14052987e+00 -5.97017407e-01 1.31393358e-01 1.01740912e-01 -3.99524361e-01 8.99951756e-01 4.44221273e-02 -5.62126756e-01 -5.23566723e-01 -6.96632087e-01 -3.44340265e-01 -1.05071878e+00 -5.16116023e-01 -2.41474643e-01 1.86778829e-01 1.59058809e-01 1.13776475e-01 4.05356824e-01 -1.58524776e+00 -3.44224006e-01 8.30288708e-01 1.16226959e+00 4.45951998e-01 9.29781616e-01 2.68074065e-01 2.36120477e-01 -1.24552059e+00 4.16050673e-01 1.95079148e-02 -1.33690983e-01 1.39362095e-02 -9.36500192e-01 1.13786690e-01 1.21704876e-01 5.70733547e-02 9.77163553e-01 1.31947398e+00 5.41002512e-01 4.07154590e-01 -1.04372394e+00 -6.12732321e-02 5.36621034e-01 -3.11792344e-01 5.69142222e-01 4.12026823e-01 -4.87129986e-01 3.81784976e-01 -7.89166927e-01 -2.61688650e-01 7.43670583e-01 1.17227507e+00 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64d07c76-3263-4a20-b651-912cbdc62908	ominacs-online-ml-based-iot-network-attack	2302.09225	null	https://arxiv.org/abs/2302.09225v2	https://arxiv.org/pdf/2302.09225v2.pdf	OMINACS: Online ML-Based IoT Network Attack Detection and Classification System	Several Machine Learning (ML) methodologies have been proposed to improve security in Internet Of Things (IoT) networks and reduce the damage caused by the action of malicious agents. However, detecting and classifying attacks with high accuracy and precision is still a major challenge. This paper proposes an online attack detection and network traffic classification system, which combines stream Machine Learning, Deep Learning, and Ensemble Learning technique. Using multiple stages of data analysis, the system can detect the presence of malicious traffic flows and classify them according to the type of attack they represent. Furthermore, we show how to implement this system both in an IoT network and from an ML point of view. The system was evaluated in three IoT network security datasets, in which it obtained accuracy and precision above 90% with a reduced false alarm rate.	['Antônio Abelém', 'Diego Abreu']	2023-02-18	null	null	null	null	['traffic-classification']	['miscellaneous']	[ 1.05189867e-02 -5.78456283e-01 -2.21464187e-01 -2.33771130e-02 -4.54245508e-03 -5.00699282e-01 5.59587479e-01 4.68825281e-01 -2.43896008e-01 4.15830195e-01 -3.06613266e-01 -7.50315964e-01 -1.67227224e-01 -1.32986116e+00 -1.35365343e-02 -5.95262349e-01 -1.20820897e-02 3.36895794e-01 4.22827423e-01 5.45520103e-03 3.34900588e-01 7.64418960e-01 -1.39019775e+00 5.79067349e-01 3.54436219e-01 1.62045634e+00 -6.81809425e-01 7.56442606e-01 -2.85778522e-01 1.06536090e+00 -9.27713335e-01 -4.97613549e-01 4.37569231e-01 4.28039469e-02 -5.32494843e-01 -3.91620904e-01 -5.97093068e-02 -2.32073531e-01 -5.06968081e-01 8.88515055e-01 5.59116125e-01 -4.57470477e-01 4.22252119e-01 -1.65199995e+00 5.92694879e-02 8.16947460e-01 -3.34180623e-01 6.28040195e-01 9.91986021e-02 2.12992683e-01 5.64121664e-01 -2.36275777e-01 -1.31406009e-01 1.12830448e+00 4.94231343e-01 4.25037146e-01 -8.21536303e-01 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4.70433533e-01 2.90616482e-01 -4.01716858e-01 -6.68079257e-01 -3.00934285e-01]	[5.207535743713379, 7.193223476409912]
efd7dd0f-4529-48bf-9ccc-cd9ad547b6b9	instancemotseg-real-time-instance-motion	2008.07008	null	https://arxiv.org/abs/2008.07008v4	https://arxiv.org/pdf/2008.07008v4.pdf	Monocular Instance Motion Segmentation for Autonomous Driving: KITTI InstanceMotSeg Dataset and Multi-task Baseline	Moving object segmentation is a crucial task for autonomous vehicles as it can be used to segment objects in a class agnostic manner based on their motion cues. It enables the detection of unseen objects during training (e.g., moose or a construction truck) based on their motion and independent of their appearance. Although pixel-wise motion segmentation has been studied in autonomous driving literature, it has been rarely addressed at the instance level, which would help separate connected segments of moving objects leading to better trajectory planning. As the main issue is the lack of large public datasets, we create a new InstanceMotSeg dataset comprising of 12.9K samples improving upon our KITTIMoSeg dataset. In addition to providing instance level annotations, we have added 4 additional classes which is crucial for studying class agnostic motion segmentation. We adapt YOLACT and implement a motion-based class agnostic instance segmentation model which would act as a baseline for the dataset. We also extend it to an efficient multi-task model which additionally provides semantic instance segmentation sharing the encoder. The model then learns separate prototype coefficients within the class agnostic and semantic heads providing two independent paths of object detection for redundant safety. To obtain real-time performance, we study different efficient encoders and obtain 39 fps on a Titan Xp GPU using MobileNetV2 with an improvement of 10% mAP relative to the baseline. Our model improves the previous state of the art motion segmentation method by 3.3%. The dataset and qualitative results video are shared in our website at https://sites.google.com/view/instancemotseg/.	['Ahmad El-Sallab', 'Muhammad Helmi', 'Waleed Hamdy', 'Senthil Yogamani', 'Eslam Mohamed', 'Hazem Rashed', 'Mennatullah Siam', 'Mahmoud Ewaisha']	2020-08-16	null	null	null	null	['motion-segmentation']	['computer-vision']	[ 2.21973322e-02 1.89049855e-01 -5.39215624e-01 -3.17537725e-01 -8.12077582e-01 -7.97450662e-01 4.54724967e-01 -1.10144891e-01 -6.80544913e-01 4.27161872e-01 -3.46444309e-01 -5.03114760e-01 2.60829151e-01 -7.95317471e-01 -1.16998947e+00 -6.25910342e-01 4.81059328e-02 6.64210021e-01 1.09922194e+00 -2.47493714e-01 2.13460937e-01 4.15371865e-01 -1.71491182e+00 3.59601229e-01 8.37670207e-01 9.71850216e-01 4.58016664e-01 8.58307421e-01 -2.06404878e-03 4.72142220e-01 -3.27493936e-01 -1.20660134e-01 6.79738522e-01 6.61954880e-02 -1.01416576e+00 1.06296875e-01 6.70921326e-01 -4.57520366e-01 -2.39694253e-01 8.75182271e-01 2.08470285e-01 2.07695499e-01 4.48767006e-01 -1.57899737e+00 1.58126764e-02 3.37991983e-01 -6.69027328e-01 3.61827105e-01 -2.60474294e-01 5.01112521e-01 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148f2f14-0a5d-44ff-b232-24ceb248e84e	rrf102-meeting-the-trec-covid-challenge-with	2010.002	null	https://arxiv.org/abs/2010.00200v1	https://arxiv.org/pdf/2010.00200v1.pdf	RRF102: Meeting the TREC-COVID Challenge with a 100+ Runs Ensemble	In this paper, we report the results of our participation in the TREC-COVID challenge. To meet the challenge of building a search engine for rapidly evolving biomedical collection, we propose a simple yet effective weighted hierarchical rank fusion approach, that ensembles together 102 runs from (a) lexical and semantic retrieval systems, (b) pre-trained and fine-tuned BERT rankers, and (c) relevance feedback runs. Our ablation studies demonstrate the contributions of each of these systems to the overall ensemble. The submitted ensemble runs achieved state-of-the-art performance in rounds 4 and 5 of the TREC-COVID challenge.	['Ryan Mcdonald', 'Michael Bendersky', 'Honglei Zhuang', 'Keith Hall', 'Shuguang Han', 'Ji Ma']	2020-10-01	null	null	null	null	['semantic-retrieval']	['natural-language-processing']	[ 1.92012697e-01 -2.53833890e-01 6.80494383e-02 -4.09446090e-01 -1.70321655e+00 -6.93091631e-01 8.66233051e-01 4.17283416e-01 -1.02228773e+00 8.78559530e-01 6.73445225e-01 -2.29436353e-01 -6.02209508e-01 3.78457978e-02 -1.49296923e-02 -4.55363899e-01 -3.37874830e-01 9.81153548e-01 5.98228633e-01 -5.66456616e-01 2.25834742e-01 1.97549835e-01 -1.33440518e+00 8.12789917e-01 6.86900795e-01 7.39067376e-01 -6.39329627e-02 8.98688853e-01 3.77995342e-01 4.02505636e-01 -4.74568963e-01 -4.31080967e-01 -1.42945454e-01 3.53046991e-02 -8.95888925e-01 -8.54221225e-01 6.10561185e-02 -2.87198890e-02 -2.99993992e-01 7.11120784e-01 8.92385066e-01 2.28731856e-01 7.66863942e-01 -5.58230877e-01 -1.25625253e-01 7.12567687e-01 -2.64686048e-01 6.40169621e-01 5.76392770e-01 -9.14410278e-02 1.06441414e+00 -1.06746829e+00 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7b798cbd-c90f-4869-9770-261c1c4ba3e0	classification-of-us-supreme-court-cases	2304.08649	null	https://arxiv.org/abs/2304.08649v2	https://arxiv.org/pdf/2304.08649v2.pdf	Classification of US Supreme Court Cases using BERT-Based Techniques	Models based on bidirectional encoder representations from transformers (BERT) produce state of the art (SOTA) results on many natural language processing (NLP) tasks such as named entity recognition (NER), part-of-speech (POS) tagging etc. An interesting phenomenon occurs when classifying long documents such as those from the US supreme court where BERT-based models can be considered difficult to use on a first-pass or out-of-the-box basis. In this paper, we experiment with several BERT-based classification techniques for US supreme court decisions or supreme court database (SCDB) and compare them with the previous SOTA results. We then compare our results specifically with SOTA models for long documents. We compare our results for two classification tasks: (1) a broad classification task with 15 categories and (2) a fine-grained classification task with 279 categories. Our best result produces an accuracy of 80\% on the 15 broad categories and 60\% on the fine-grained 279 categories which marks an improvement of 8\% and 28\% respectively from previously reported SOTA results.	['John E. Ortega', 'Adam Meyers', 'Shubham Vatsal']	2023-04-17	null	null	null	null	['part-of-speech-tagging']	['natural-language-processing']	[-9.04061273e-02 9.76898894e-02 -8.73509496e-02 -5.76258957e-01 -1.09000039e+00 -7.62591660e-01 1.03782785e+00 5.67624092e-01 -7.52967775e-01 1.08733356e+00 5.89931011e-01 -9.58909929e-01 -2.58008510e-01 -9.88148212e-01 -4.38217342e-01 -3.00086021e-01 1.24769087e-03 7.10081518e-01 2.13143393e-01 -3.10299337e-01 3.71522605e-01 8.10204923e-01 -1.14341962e+00 7.16368556e-01 5.50004959e-01 8.35600317e-01 -1.40842125e-01 8.53258908e-01 -6.61350191e-01 1.03895569e+00 -5.90902150e-01 -8.68607759e-01 2.92377651e-01 -1.39911994e-01 -1.26752305e+00 -6.47338390e-01 4.85046804e-01 2.11177737e-01 -7.81181306e-02 9.18088377e-01 5.24689257e-01 2.08254829e-01 1.00236726e+00 -8.99965525e-01 -7.64098942e-01 8.86368454e-01 -3.32195014e-01 5.84555686e-01 3.33068043e-01 -4.38911766e-01 1.25637376e+00 -9.08470511e-01 8.14750016e-01 1.17429399e+00 8.25345993e-01 4.42703307e-01 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d5018a6f-e3fc-4294-bd70-bc285e1984b2	bitiimt-a-bilingual-text-infilling-method-for	null	null	https://aclanthology.org/2022.acl-long.138	https://aclanthology.org/2022.acl-long.138.pdf	BiTIIMT: A Bilingual Text-infilling Method for Interactive Machine Translation	Interactive neural machine translation (INMT) is able to guarantee high-quality translations by taking human interactions into account. Existing IMT systems relying on lexical constrained decoding (LCD) enable humans to translate in a flexible translation order beyond the left-to-right. However, they typically suffer from two significant limitations in translation efficiency and quality due to the reliance on LCD. In this work, we propose a novel BiTIIMT system, Bilingual Text-Infilling for Interactive Neural Machine Translation. The key idea to BiTIIMT is Bilingual Text-infilling (BiTI) which aims to fill missing segments in a manually revised translation for a given source sentence. We propose a simple yet effective solution by casting this task as a sequence-to-sequence task. In this way, our system performs decoding without explicit constraints and makes full use of revised words for better translation prediction. Experiment results show that BiTiIMT performs significantly better and faster than state-of-the-art LCD-based IMT on three translation tasks.	['Jiajun Chen', 'Shuming Shi', 'ShuJian Huang', 'Qu Cui', 'Guoping Huang', 'Lemao Liu', 'Yanling Xiao']	null	null	null	null	acl-2022-5	['text-infilling']	['natural-language-processing']	[ 5.89174688e-01 5.22451755e-03 -3.18952292e-01 -5.06045341e-01 -1.03612578e+00 -6.33046269e-01 5.89464247e-01 -3.88965160e-01 -4.06420648e-01 8.96454751e-01 2.98449337e-01 -1.02845597e+00 4.77632016e-01 -4.74479795e-01 -1.00724828e+00 -2.21277550e-01 6.13351583e-01 7.70603061e-01 -1.43228635e-01 -4.97719198e-01 4.10950221e-02 -2.86400970e-02 -7.99335122e-01 6.63265526e-01 1.38970602e+00 3.56155753e-01 5.78500092e-01 3.09047580e-01 -3.72573882e-01 9.15955901e-02 -4.36404586e-01 -7.81563878e-01 4.35446918e-01 -1.01623678e+00 -7.37840354e-01 -9.95201617e-02 1.68027863e-01 -3.90375286e-01 1.01663157e-01 8.38312984e-01 6.89280510e-01 -3.82245511e-01 5.20317316e-01 -7.69394875e-01 -9.50746775e-01 1.11324441e+00 -3.96821171e-01 -1.11546606e-01 3.70618016e-01 4.51802239e-02 9.63090897e-01 -1.41697609e+00 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1b37ad41-57b9-43f5-9b50-df59154ccb9e	distribution-aligned-feature-clustering-for	2301.06685	null	https://arxiv.org/abs/2301.06685v1	https://arxiv.org/pdf/2301.06685v1.pdf	Distribution Aligned Feature Clustering for Zero-Shot Sketch-Based Image Retrieval	Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a challenging cross-modal retrieval task. In prior arts, the retrieval is conducted by sorting the distance between the query sketch and each image in the gallery. However, the domain gap and the zero-shot setting make neural networks hard to generalize. This paper tackles the challenges from a new perspective: utilizing gallery image features. We propose a Cluster-then-Retrieve (ClusterRetri) method that performs clustering on the gallery images and uses the cluster centroids as proxies for retrieval. Furthermore, a distribution alignment loss is proposed to align the image and sketch features with a common Gaussian distribution, reducing the domain gap. Despite its simplicity, our proposed method outperforms the state-of-the-art methods by a large margin on popular datasets, e.g., up to 31% and 39% relative improvement of mAP@all on the Sketchy and TU-Berlin datasets.	['Huimin Ma', 'Jiansheng Chen', 'Fangzheng Zhao', 'Kun Song', 'Yuchen Wu']	2023-01-17	null	null	null	null	['sketch-based-image-retrieval']	['computer-vision']	[-4.01680730e-02 -6.47819221e-01 -5.33115804e-01 -2.12660730e-01 -1.50192046e+00 -8.10405016e-01 9.40916896e-01 -8.59447867e-02 -2.41093189e-01 3.13531369e-01 1.06946230e-02 3.63816172e-01 -4.48100269e-01 -4.60702151e-01 -6.60749257e-01 -7.02487111e-01 3.68513912e-01 6.41534805e-01 3.60998847e-02 -3.71798314e-02 5.94530582e-01 4.33466375e-01 -1.67051029e+00 2.13286921e-01 6.17358565e-01 1.19632125e+00 2.84728765e-01 3.57237667e-01 -1.77817732e-01 3.44457656e-01 -5.17949820e-01 -5.70471346e-01 4.75255698e-01 -9.47650671e-02 -4.41561371e-01 2.62704911e-03 1.03764439e+00 -5.84782362e-01 -8.68037760e-01 9.85304236e-01 6.76358283e-01 4.35281247e-01 9.89082813e-01 -1.52208495e+00 -1.13911450e+00 3.32502633e-01 -7.62305140e-01 -3.45494092e-01 2.08169192e-01 -2.64810562e-01 1.28461647e+00 -1.27267849e+00 1.02779090e+00 1.27658999e+00 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0c4a531a-e181-410d-aae5-679e036127dc	f-siamese-tracker-a-frustum-based-double	2010.1151	null	https://arxiv.org/abs/2010.11510v1	https://arxiv.org/pdf/2010.11510v1.pdf	F-Siamese Tracker: A Frustum-based Double Siamese Network for 3D Single Object Tracking	This paper presents F-Siamese Tracker, a novel approach for single object tracking prominently characterized by more robustly integrating 2D and 3D information to reduce redundant search space. A main challenge in 3D single object tracking is how to reduce search space for generating appropriate 3D candidates. Instead of solely relying on 3D proposals, firstly, our method leverages the Siamese network applied on RGB images to produce 2D region proposals which are then extruded into 3D viewing frustums. Besides, we perform an online accuracy validation on the 3D frustum to generate refined point cloud searching space, which can be embedded directly into the existing 3D tracking backbone. For efficiency, our approach gains better performance with fewer candidates by reducing search space. In addition, benefited from introducing the online accuracy validation, for occasional cases with strong occlusions or very sparse points, our approach can still achieve high precision, even when the 2D Siamese tracker loses the target. This approach allows us to set a new state-of-the-art in 3D single object tracking by a significant margin on a sparse outdoor dataset (KITTI tracking). Moreover, experiments on 2D single object tracking show that our framework boosts 2D tracking performance as well.	['Wanlong Li', 'Feng Wen', 'Yong liu', 'Chujuan Zhang', 'Xin Kong', 'Jinhao Cui', 'Hao Zou']	2020-10-22	null	null	null	null	['3d-single-object-tracking']	['computer-vision']	[-2.95043707e-01 -1.96450204e-01 -1.69890940e-01 2.97966599e-01 -8.83296132e-01 -9.17015612e-01 4.89214540e-01 -4.11575511e-02 -4.55763578e-01 2.67855018e-01 -4.45621550e-01 -6.15528338e-02 3.61221880e-02 -3.73098820e-01 -8.12862635e-01 -5.66721559e-01 -1.02306642e-01 7.72167087e-01 9.59814787e-01 -1.07718602e-01 -4.45086509e-04 1.12605929e+00 -1.49759531e+00 -5.90845108e-01 6.33012176e-01 1.19199800e+00 5.96729666e-02 4.74582851e-01 -5.70468828e-02 8.23495612e-02 -6.33369029e-01 -2.62656927e-01 9.21043515e-01 7.85212517e-02 5.33419028e-02 2.27427319e-01 9.56226826e-01 -4.59123552e-01 -2.02157170e-01 1.09809351e+00 4.37882721e-01 -4.69795465e-02 3.04924309e-01 -1.32326305e+00 -9.61417556e-02 7.55437389e-02 -8.24088097e-01 9.18674469e-03 4.20150757e-01 5.98722517e-01 6.17327869e-01 -1.11335337e+00 7.46946156e-01 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59f77d95-c897-404f-8086-39458f28ce64	breast-cancer-detection-using-artificial	2203.04308	null	https://arxiv.org/abs/2203.04308v1	https://arxiv.org/pdf/2203.04308v1.pdf	Breast cancer detection using artificial intelligence techniques: A systematic literature review	Cancer is one of the most dangerous diseases to humans, and yet no permanent cure has been developed for it. Breast cancer is one of the most common cancer types. According to the National Breast Cancer foundation, in 2020 alone, more than 276,000 new cases of invasive breast cancer and more than 48,000 non-invasive cases were diagnosed in the US. To put these figures in perspective, 64% of these cases are diagnosed early in the disease's cycle, giving patients a 99% chance of survival. Artificial intelligence and machine learning have been used effectively in detection and treatment of several dangerous diseases, helping in early diagnosis and treatment, and thus increasing the patient's chance of survival. Deep learning has been designed to analyze the most important features affecting detection and treatment of serious diseases. For example, breast cancer can be detected using genes or histopathological imaging. Analysis at the genetic level is very expensive, so histopathological imaging is the most common approach used to detect breast cancer. In this research work, we systematically reviewed previous work done on detection and treatment of breast cancer using genetic sequencing or histopathological imaging with the help of deep learning and machine learning. We also provide recommendations to researchers who will work in this field	['Omar Elgendy', 'Yaman Afadar', 'Qassim Nasir', 'Manar Abu Talib', 'Ali Bou Nassif']	2022-03-08	null	null	null	null	['breast-cancer-detection', 'breast-cancer-detection']	['knowledge-base', 'medical']	[ 2.64752775e-01 3.33093047e-01 -8.01281691e-01 -2.41021007e-01 -5.77205420e-01 -1.04454540e-01 2.71611009e-02 7.12180436e-01 -5.10034204e-01 6.14708662e-01 1.44855872e-01 -6.39885783e-01 -2.83406992e-02 -9.40032601e-01 -1.83990479e-01 -9.66165721e-01 4.51500863e-02 6.77341163e-01 -8.85239616e-02 -1.52012020e-01 -1.07625894e-01 7.04227567e-01 -1.11631298e+00 3.78505021e-01 7.80311227e-01 9.14309382e-01 1.94007382e-01 7.23439336e-01 -2.52641797e-01 4.72157657e-01 -2.57444113e-01 -4.77669477e-01 -1.84781998e-01 -5.47131896e-01 -6.50101721e-01 -3.80436093e-01 -2.54880100e-01 -2.96098858e-01 -1.26109853e-01 1.00361311e+00 5.82108080e-01 -7.44042516e-01 5.91320574e-01 -6.85455203e-01 -8.13524425e-02 4.36462998e-01 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d2f3a3ec-5e46-4423-ab0c-c266534c52d4	improving-rouge-for-timeline-summarization	null	null	https://aclanthology.org/E17-2046	https://aclanthology.org/E17-2046.pdf	Improving ROUGE for Timeline Summarization	Current evaluation metrics for timeline summarization either ignore the temporal aspect of the task or require strict date matching. We introduce variants of ROUGE that allow alignment of daily summaries via temporal distance or semantic similarity. We argue for the suitability of these variants in a theoretical analysis and demonstrate it in a battery of task-specific tests.	['Katja Markert', 'Sebastian Martschat']	2017-04-01	null	null	null	eacl-2017-4	['timeline-summarization']	['natural-language-processing']	[-8.20564292e-03 -1.28527030e-01 -4.70364481e-01 -5.88821769e-01 -7.74666727e-01 -8.73648584e-01 1.21887589e+00 6.86571836e-01 -4.45464909e-01 8.06446552e-01 1.00173652e+00 -3.04072589e-01 -6.45519614e-01 -4.80386764e-01 -1.26621962e-01 -8.04757923e-02 -5.66392004e-01 2.98755467e-01 3.68840575e-01 -3.19040388e-01 7.94549644e-01 2.28604108e-01 -1.41412544e+00 2.06363618e-01 1.06297827e+00 8.01144600e-01 -1.06242165e-01 8.77192020e-01 -2.14009643e-01 6.12942159e-01 -1.17383611e+00 -2.44273946e-01 -7.32046515e-02 -4.90987867e-01 -9.43735003e-01 -1.44386053e-01 4.88637596e-01 -2.41420344e-01 -4.53275770e-01 6.08595788e-01 5.33889771e-01 6.22574508e-01 7.31707215e-01 -1.34009171e+00 -4.29506868e-01 5.04349411e-01 -2.86912590e-01 9.97963071e-01 9.72498298e-01 -3.13260019e-01 1.38045108e+00 -3.76505047e-01 8.69140267e-01 9.84412253e-01 9.38929439e-01 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73cef685-5956-4837-85e2-cd7dc285e699	a-high-fidelity-synthetic-face-framework-for	2007.08364	null	https://arxiv.org/abs/2007.08364v1	https://arxiv.org/pdf/2007.08364v1.pdf	A high fidelity synthetic face framework for computer vision	Analysis of faces is one of the core applications of computer vision, with tasks ranging from landmark alignment, head pose estimation, expression recognition, and face recognition among others. However, building reliable methods requires time-consuming data collection and often even more time-consuming manual annotation, which can be unreliable. In our work we propose synthesizing such facial data, including ground truth annotations that would be almost impossible to acquire through manual annotation at the consistency and scale possible through use of synthetic data. We use a parametric face model together with hand crafted assets which enable us to generate training data with unprecedented quality and diversity (varying shape, texture, expression, pose, lighting, and hair).	['Tadas Baltrusaitis', 'Thomas J. Cashman', 'Marek Kowalski', 'Virginia Estellers', 'Erroll Wood', 'Sebastian Dziadzio', 'Jamie Shotton', 'Charlie Hewitt', 'Matthew Johnson']	2020-07-16	null	null	null	null	['head-pose-estimation']	['computer-vision']	[ 2.61713862e-01 2.99441218e-01 3.57700497e-01 -6.25774801e-01 -6.14966631e-01 -6.64921820e-01 7.36772835e-01 -9.89426151e-02 -1.67594358e-01 7.28079736e-01 -1.17204100e-01 1.49544939e-01 3.65203954e-02 -2.89160192e-01 -4.13001478e-01 -6.30718768e-01 6.78723454e-02 6.97613716e-01 -1.54303685e-01 -1.76050097e-01 -7.46880546e-02 8.84037912e-01 -1.85369802e+00 -2.91159689e-01 4.30294096e-01 1.07084334e+00 -3.27116013e-01 3.72170240e-01 9.18094143e-02 1.62299752e-01 -3.75702381e-01 -6.52671635e-01 2.98974872e-01 -2.43249580e-01 -4.40155149e-01 6.03740692e-01 6.87037528e-01 -1.30279079e-01 1.68752924e-01 8.58951271e-01 4.31937993e-01 -1.07954005e-02 5.22194266e-01 -1.26230490e+00 -1.99808076e-01 -8.65762979e-02 -7.03528404e-01 -4.80930477e-01 5.27550459e-01 -5.56005910e-02 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39ac33f1-3201-4a36-abbe-7d0ba46ddfbd	a-deep-neural-architecture-for-harmonizing-3	2303.00175	null	https://arxiv.org/abs/2303.00175v2	https://arxiv.org/pdf/2303.00175v2.pdf	A Deep Neural Architecture for Harmonizing 3-D Input Data Analysis and Decision Making in Medical Imaging	Harmonizing the analysis of data, especially of 3-D image volumes, consisting of different number of slices and annotated per volume, is a significant problem in training and using deep neural networks in various applications, including medical imaging. Moreover, unifying the decision making of the networks over different input datasets is crucial for the generation of rich data-driven knowledge and for trusted usage in the applications. This paper presents a new deep neural architecture, named RACNet, which includes routing and feature alignment steps and effectively handles different input lengths and single annotations of the 3-D image inputs, whilst providing highly accurate decisions. In addition, through latent variable extraction from the trained RACNet, a set of anchors are generated providing further insight on the network's decision making. These can be used to enrich and unify data-driven knowledge extracted from different datasets. An extensive experimental study illustrates the above developments, focusing on COVID-19 diagnosis through analysis of 3-D chest CT scans from databases generated in different countries and medical centers.	['Stefanos Kollias', 'Anastasios Arsenos', 'Dimitrios Kollias']	2023-03-01	null	null	null	null	['covid-19-detection']	['medical']	[ 2.53532261e-01 1.40328750e-01 -2.66295373e-01 -9.23908472e-01 -6.50594592e-01 -4.29345667e-01 6.99918196e-02 5.68858683e-01 -4.67824996e-01 6.57628298e-01 8.72228146e-02 -5.00913918e-01 -5.13494492e-01 -6.72042131e-01 -2.81244636e-01 -8.10983658e-01 -2.98414677e-01 9.04896617e-01 1.00029435e-03 1.17803842e-01 -1.61582217e-01 9.36515152e-01 -1.02975035e+00 4.85873520e-01 4.27276909e-01 1.20789146e+00 3.22669953e-01 5.91258645e-01 -1.97124302e-01 7.23873258e-01 -6.32605195e-01 -3.42970043e-01 1.63221940e-01 4.64391476e-03 -9.02735949e-01 5.92087135e-02 2.01023728e-01 -3.56512874e-01 -3.20011601e-02 7.37914383e-01 7.46394455e-01 -1.59947947e-01 7.70429015e-01 -8.76863778e-01 -2.93290555e-01 7.43199468e-01 -1.67557806e-01 5.33324957e-01 -3.73451978e-01 3.30081396e-02 7.19801009e-01 -5.03644228e-01 7.11487532e-01 9.20167387e-01 6.22840464e-01 3.11552942e-01 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-3.17440778e-01 4.65993673e-01 5.69956541e-01 -7.96550155e-01 9.25550833e-02 2.24248230e-01]	[14.798783302307129, -2.271860122680664]
faec938a-e1d1-489f-a76d-3fb768375665	move2hear-active-audio-visual-source	2105.07142	null	https://arxiv.org/abs/2105.07142v2	https://arxiv.org/pdf/2105.07142v2.pdf	Move2Hear: Active Audio-Visual Source Separation	We introduce the active audio-visual source separation problem, where an agent must move intelligently in order to better isolate the sounds coming from an object of interest in its environment. The agent hears multiple audio sources simultaneously (e.g., a person speaking down the hall in a noisy household) and it must use its eyes and ears to automatically separate out the sounds originating from a target object within a limited time budget. Towards this goal, we introduce a reinforcement learning approach that trains movement policies controlling the agent's camera and microphone placement over time, guided by the improvement in predicted audio separation quality. We demonstrate our approach in scenarios motivated by both augmented reality (system is already co-located with the target object) and mobile robotics (agent begins arbitrarily far from the target object). Using state-of-the-art realistic audio-visual simulations in 3D environments, we demonstrate our model's ability to find minimal movement sequences with maximal payoff for audio source separation. Project: http://vision.cs.utexas.edu/projects/move2hear.	['Kristen Grauman', 'Ziad Al-Halah', 'Sagnik Majumder']	2021-05-15	null	http://openaccess.thecvf.com//content/ICCV2021/html/Majumder_Move2Hear_Active_Audio-Visual_Source_Separation_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Majumder_Move2Hear_Active_Audio-Visual_Source_Separation_ICCV_2021_paper.pdf	iccv-2021-1	['audio-source-separation']	['audio']	[ 3.22025210e-01 2.26225868e-01 4.45919275e-01 2.69104421e-01 -1.30785906e+00 -8.73955786e-01 2.67230690e-01 1.79896399e-01 -4.88320917e-01 4.23477739e-01 1.57173917e-01 1.00455374e-01 -2.22901657e-01 -2.54784137e-01 -6.86171293e-01 -9.48613524e-01 -4.50750560e-01 4.54639018e-01 2.30525017e-01 -7.54513545e-03 2.39845380e-01 5.05985677e-01 -1.79514229e+00 -2.98779439e-02 3.65699053e-01 5.80164015e-01 8.82424355e-01 1.66199064e+00 4.54994678e-01 1.09482253e+00 -7.78126240e-01 4.99703377e-01 4.07821357e-01 -5.72710931e-01 -5.81279933e-01 3.38313043e-01 -1.40796136e-02 -2.46388391e-01 -5.98482080e-02 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171e4250-f51f-468c-b44b-1311d5fce6fa	a-few-shot-sequential-approach-for-object	2007.01899	null	https://arxiv.org/abs/2007.01899v2	https://arxiv.org/pdf/2007.01899v2.pdf	A Few-Shot Sequential Approach for Object Counting	In this work, we address the problem of few-shot multi-class object counting with point-level annotations. The proposed technique leverages a class agnostic attention mechanism that sequentially attends to objects in the image and extracts their relevant features. This process is employed on an adapted prototypical-based few-shot approach that uses the extracted features to classify each one either as one of the classes present in the support set images or as background. The proposed technique is trained on point-level annotations and uses a novel loss function that disentangles class-dependent and class-agnostic aspects of the model to help with the task of few-shot object counting. We present our results on a variety of object-counting/detection datasets, including FSOD and MS COCO. In addition, we introduce a new dataset that is specifically designed for weakly supervised multi-class object counting/detection and contains considerably different classes and distribution of number of classes/instances per image compared to the existing datasets. We demonstrate the robustness of our approach by testing our system on a totally different distribution of classes from what it has been trained on.	['Negar Rostamzadeh', 'Pegah Kamousi', 'Negin Sokhandan', 'Eniola Alese', 'Alejandro Posada']	2020-07-03	null	null	null	null	['object-counting']	['computer-vision']	[ 5.81526041e-01 -2.63874114e-01 -1.60030320e-01 -2.90187776e-01 -8.47871423e-01 -3.66330147e-01 8.42098773e-01 6.61763430e-01 -9.30634856e-01 5.13113976e-01 -3.04982901e-01 1.43551022e-01 4.75616455e-02 -7.97805071e-01 -6.10122502e-01 -5.84929764e-01 2.15420216e-01 7.30711818e-01 8.71824503e-01 1.88830987e-01 6.01532757e-01 4.76761431e-01 -2.05219650e+00 4.84245926e-01 2.12760210e-01 9.12338853e-01 2.53642172e-01 1.09709430e+00 -1.39720514e-01 1.31408834e+00 -7.19562650e-01 -4.53329742e-01 3.01118284e-01 -1.49356902e-01 -7.40866959e-01 1.56563193e-01 1.05038655e+00 -2.34397978e-01 -5.30364364e-02 1.09780896e+00 4.03082639e-01 1.68947518e-01 1.12092328e+00 -1.12287498e+00 -1.41838104e-01 -6.89429492e-02 -9.58871305e-01 1.03263497e+00 -2.56979540e-02 2.14744985e-01 8.92212391e-01 -9.73056734e-01 4.02107477e-01 1.14589775e+00 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2933ca2b-5a4e-4ac5-8d62-d12baae78deb	the-missing-data-encoder-cross-channel-image	1905.01861	null	https://arxiv.org/abs/1905.01861v1	https://arxiv.org/pdf/1905.01861v1.pdf	The Missing Data Encoder: Cross-Channel Image Completion\\with Hide-And-Seek Adversarial Network	Image completion is the problem of generating whole images from fragments only. It encompasses inpainting (generating a patch given its surrounding), reverse inpainting/extrapolation (generating the periphery given the central patch) as well as colorization (generating one or several channels given other ones). In this paper, we employ a deep network to perform image completion, with adversarial training as well as perceptual and completion losses, and call it the ``missing data encoder'' (MDE). We consider several configurations based on how the seed fragments are chosen. We show that training MDE for ``random extrapolation and colorization'' (MDE-REC), i.e. using random channel-independent fragments, allows a better capture of the image semantics and geometry. MDE training makes use of a novel ``hide-and-seek'' adversarial loss, where the discriminator seeks the original non-masked regions, while the generator tries to hide them. We validate our models both qualitatively and quantitatively on several datasets, showing their interest for image completion, unsupervised representation learning as well as face occlusion handling.	['Matthieu Cord', 'Patrick Perez', 'Arnaud Dapogny']	2019-05-06	null	null	null	null	['occlusion-handling']	['computer-vision']	[ 5.80285251e-01 4.54580963e-01 3.58134329e-01 -2.41190985e-01 -9.01108623e-01 -7.17281163e-01 7.17590570e-01 -1.65072992e-01 -2.36492306e-01 7.30461836e-01 2.22424716e-02 2.91411448e-02 3.93951893e-01 -8.72266710e-01 -1.30986607e+00 -9.68155444e-01 -8.83224159e-02 4.23861951e-01 -1.57048792e-01 -1.44415066e-01 -7.86972195e-02 7.51363695e-01 -1.47991514e+00 5.11177778e-01 7.06988454e-01 9.93505299e-01 7.82131031e-02 7.51217902e-01 1.71710044e-01 8.61007810e-01 -7.40228534e-01 -6.22814119e-01 5.39846778e-01 -5.97425282e-01 -5.93918025e-01 4.56509471e-01 5.33168614e-01 -5.61907887e-01 -2.16267645e-01 7.38101244e-01 3.86066437e-01 -7.08940104e-02 7.64384389e-01 -1.23920166e+00 -9.35845971e-01 2.76043057e-01 -7.44500577e-01 -4.65785205e-01 5.14113545e-01 3.45215052e-01 6.62909746e-01 -9.82140422e-01 9.43102896e-01 1.21155369e+00 6.21238649e-01 8.15818310e-01 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7ea7b4d1-90f2-409a-a70b-8f8beda2377b	risk-perspective-exploration-in	2206.1417	null	https://arxiv.org/abs/2206.14170v2	https://arxiv.org/pdf/2206.14170v2.pdf	Risk Perspective Exploration in Distributional Reinforcement Learning	Distributional reinforcement learning demonstrates state-of-the-art performance in continuous and discrete control settings with the features of variance and risk, which can be used to explore. However, the exploration method employing the risk property is hard to find, although numerous exploration methods in Distributional RL employ the variance of return distribution per action. In this paper, we present risk scheduling approaches that explore risk levels and optimistic behaviors from a risk perspective. We demonstrate the performance enhancement of the DMIX algorithm using risk scheduling in a multi-agent setting with comprehensive experiments.	['Se-Young Yun', 'Joonkee Kim', 'Jihwan Oh']	2022-06-28	null	null	null	null	['distributional-reinforcement-learning']	['methodology']	[-3.88774693e-01 1.88002810e-01 -8.23761225e-01 -1.51762828e-01 -1.12571704e+00 -3.12562197e-01 5.64837396e-01 1.76383302e-01 -7.25428700e-01 1.30289114e+00 5.22231795e-02 -3.77450228e-01 -7.72643149e-01 -9.20522690e-01 -3.74669045e-01 -9.82570410e-01 -8.78509641e-01 5.91407835e-01 -3.85807902e-01 -1.58330202e-01 3.04288924e-01 7.39004761e-02 -1.27992845e+00 -4.73712057e-01 1.03590178e+00 1.16373241e+00 2.43294537e-01 2.60089427e-01 1.40220433e-01 8.35567653e-01 -7.97470033e-01 3.52450386e-02 4.04729664e-01 -2.56002903e-01 -3.77754211e-01 -3.91716272e-01 -4.51436937e-01 -6.00002170e-01 3.33401933e-02 1.12617636e+00 7.26946533e-01 6.11512005e-01 6.05401814e-01 -1.48979330e+00 -4.56274301e-01 1.07229388e+00 -1.02877462e+00 1.63150057e-01 2.95676500e-01 3.88799906e-01 1.07286060e+00 -2.07855493e-01 2.91622162e-01 1.61377084e+00 1.06919244e-01 4.93802994e-01 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-7.68792331e-01 -1.13084459e+00 -1.77699134e-01 3.44420165e-01 -1.24374723e+00 -3.68261307e-01 -9.75114584e-01]	[4.150386810302734, 2.5610995292663574]
5f3da63f-86a0-4f45-9505-40e5d45de9cc	marginalized-average-attentional-network-for-1	1905.08586	null	https://arxiv.org/abs/1905.08586v1	https://arxiv.org/pdf/1905.08586v1.pdf	Marginalized Average Attentional Network for Weakly-Supervised Learning	In weakly-supervised temporal action localization, previous works have failed to locate dense and integral regions for each entire action due to the overestimation of the most salient regions. To alleviate this issue, we propose a marginalized average attentional network (MAAN) to suppress the dominant response of the most salient regions in a principled manner. The MAAN employs a novel marginalized average aggregation (MAA) module and learns a set of latent discriminative probabilities in an end-to-end fashion. MAA samples multiple subsets from the video snippet features according to a set of latent discriminative probabilities and takes the expectation over all the averaged subset features. Theoretically, we prove that the MAA module with learned latent discriminative probabilities successfully reduces the difference in responses between the most salient regions and the others. Therefore, MAAN is able to generate better class activation sequences and identify dense and integral action regions in the videos. Moreover, we propose a fast algorithm to reduce the complexity of constructing MAA from O($2^T$) to O($T^2$). Extensive experiments on two large-scale video datasets show that our MAAN achieves superior performance on weakly-supervised temporal action localization	['Dit-yan Yeung', 'Ivor W. Tsang', 'Yueming Lyu', 'Yuan Yuan', 'Xi Shen']	2019-05-21	marginalized-average-attentional-network-for	https://openreview.net/forum?id=HkljioCcFQ	https://openreview.net/pdf?id=HkljioCcFQ	iclr-2019-5	['weakly-supervised-action-localization', 'weakly-supervised-temporal-action']	['computer-vision', 'computer-vision']	[ 3.67815465e-01 -8.21223706e-02 -4.42130208e-01 -1.74618959e-01 -9.47002709e-01 -1.57385454e-01 2.45395049e-01 -3.40165824e-01 -4.28019434e-01 5.14313161e-01 4.44041640e-01 3.16743582e-01 -2.88343191e-01 -4.48302329e-01 -9.54738736e-01 -1.06614614e+00 -2.27722958e-01 1.68254420e-01 6.84395015e-01 1.59637466e-01 1.82032228e-01 1.18712105e-01 -1.58044696e+00 6.16324902e-01 8.99243772e-01 1.13036454e+00 3.94488811e-01 2.41348326e-01 1.95668980e-01 1.08826387e+00 -3.80766690e-01 1.75834328e-01 3.63074899e-01 -8.90152991e-01 -5.77471137e-01 2.52331227e-01 5.88374376e-01 -3.09407532e-01 -2.86330283e-01 1.09855974e+00 4.20377672e-01 4.77785051e-01 5.38521588e-01 -1.10139823e+00 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9a594cd8-98da-4887-a97f-8e6b33c60d8f	visualization-and-interpretation-of-latent	1903.1157	null	http://arxiv.org/abs/1903.11570v1	http://arxiv.org/pdf/1903.11570v1.pdf	Visualization and Interpretation of Latent Spaces for Controlling Expressive Speech Synthesis through Audio Analysis	The field of Text-to-Speech has experienced huge improvements last years benefiting from deep learning techniques. Producing realistic speech becomes possible now. As a consequence, the research on the control of the expressiveness, allowing to generate speech in different styles or manners, has attracted increasing attention lately. Systems able to control style have been developed and show impressive results. However the control parameters often consist of latent variables and remain complex to interpret. In this paper, we analyze and compare different latent spaces and obtain an interpretation of their influence on expressive speech. This will enable the possibility to build controllable speech synthesis systems with an understandable behaviour.	['Thierry Dutoit', 'Noé Tits', 'Kevin El Haddad', 'Fengna Wang', 'Vincent Pagel']	2019-03-27	null	null	null	null	['learning-network-representations', 'emotional-speech-synthesis', 'expressive-speech-synthesis']	['methodology', 'speech', 'speech']	[ 3.16085108e-02 3.54011178e-01 -1.28510550e-01 -4.61064786e-01 -2.48085290e-01 -4.34004664e-01 9.98260617e-01 4.81952392e-02 -8.58586356e-02 6.91586852e-01 1.79621905e-01 -8.20767358e-02 -2.11140290e-01 -8.33287418e-01 -4.20972884e-01 -9.44271684e-01 1.55016541e-01 5.61930358e-01 3.89055698e-04 -4.12282467e-01 -4.56153452e-02 7.30241060e-01 -1.95849562e+00 4.96318012e-01 7.16509938e-01 5.42468548e-01 4.47445035e-01 8.27089846e-01 -3.14636797e-01 5.99137545e-01 -9.09926772e-01 -7.87330270e-02 -7.11376742e-02 -5.26347816e-01 -4.42004353e-01 5.61843067e-02 2.08406858e-02 1.20488681e-01 -5.84605597e-02 9.76143181e-01 4.77498978e-01 -8.76866430e-02 6.75029933e-01 -1.14377749e+00 -5.51783621e-01 1.07580662e+00 3.02016824e-01 -1.67235836e-01 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305d9cbd-bd7f-4441-8827-1a8afad13105	private-meeting-summarization-without	2305.15894	null	https://arxiv.org/abs/2305.15894v1	https://arxiv.org/pdf/2305.15894v1.pdf	Private Meeting Summarization Without Performance Loss	Meeting summarization has an enormous business potential, but in addition to being a hard problem, roll-out is challenged by privacy concerns. We explore the problem of meeting summarization under differential privacy constraints and find, to our surprise, that while differential privacy leads to slightly lower performance on in-sample data, differential privacy improves performance when evaluated on unseen meeting types. Since meeting summarization systems will encounter a great variety of meeting types in practical employment scenarios, this observation makes safe meeting summarization seem much more feasible. We perform extensive error analysis and identify potential risks in meeting summarization under differential privacy, including a faithfulness analysis.	['Anders Søgaard', 'Seolhwa Lee']	2023-05-25	null	null	null	null	['meeting-summarization']	['natural-language-processing']	[ 3.27989399e-01 6.26736820e-01 -8.30591172e-02 -4.83486116e-01 -1.44405150e+00 -7.21864045e-01 3.45708609e-01 6.92419827e-01 -3.70229840e-01 1.02726960e+00 7.92152226e-01 -3.30552459e-01 -8.70121047e-02 -4.88042057e-01 -4.72423136e-01 -4.48593736e-01 -3.38983864e-01 5.88835180e-01 -2.37287283e-01 -3.84306125e-02 2.66795844e-01 9.57060903e-02 -1.02109301e+00 3.27710450e-01 9.44799423e-01 5.32969356e-01 -7.07928181e-01 7.55615056e-01 2.99960852e-01 7.66831338e-02 -1.06092477e+00 -7.91672647e-01 7.40799963e-01 -2.15680182e-01 -8.90152514e-01 2.38236010e-01 6.07943714e-01 -4.03832138e-01 -4.31537390e-01 8.22605669e-01 7.46518433e-01 1.57018602e-01 7.14952350e-01 -1.26835227e+00 -3.64956856e-01 7.73048520e-01 -8.82237077e-01 1.42511576e-01 9.24487233e-01 2.37891078e-02 1.40638113e+00 -4.67245013e-01 4.15005326e-01 8.88641357e-01 7.68399477e-01 4.69386637e-01 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1df71a30-5933-4456-bc00-de6641f9c689	semantic-preserving-adversarial-text-attacks	2108.10015	null	https://arxiv.org/abs/2108.10015v2	https://arxiv.org/pdf/2108.10015v2.pdf	Semantic-Preserving Adversarial Text Attacks	Deep neural networks (DNNs) are known to be vulnerable to adversarial images, while their robustness in text classification is rarely studied. Several lines of text attack methods have been proposed in the literature, including character-level, word-level, and sentence-level attacks. However, it is still a challenge to minimize the number of word changes necessary to induce misclassification, while simultaneously ensuring lexical correctness, syntactic soundness, and semantic similarity. In this paper, we propose a Bigram and Unigram based adaptive Semantic Preservation Optimization (BU-SPO) method to examine the vulnerability of deep models. Our method has four major merits. Firstly, we propose to attack text documents not only at the unigram word level but also at the bigram level which better keeps semantics and avoids producing meaningless outputs. Secondly, we propose a hybrid method to replace the input words with options among both their synonyms candidates and sememe candidates, which greatly enriches the potential substitutions compared to only using synonyms. Thirdly, we design an optimization algorithm, i.e., Semantic Preservation Optimization (SPO), to determine the priority of word replacements, aiming to reduce the modification cost. Finally, we further improve the SPO with a semantic Filter (named SPOF) to find the adversarial example with the highest semantic similarity. We evaluate the effectiveness of our BU-SPO and BU-SPOF on IMDB, AG's News, and Yahoo! Answers text datasets by attacking four popular DNNs models. Results show that our methods achieve the highest attack success rates and semantics rates by changing the smallest number of words compared with existing methods.	['DaCheng Tao', 'Wei Liu', 'James Bailey', 'Weifeng Liu', 'Xinghao Yang']	2021-08-23	null	null	null	null	['adversarial-text']	['adversarial']	[ 2.11934656e-01 -4.15369689e-01 5.24252243e-02 -2.90848762e-01 -1.97005346e-01 -6.63318694e-01 5.42816043e-01 2.55806118e-01 -7.90838778e-01 5.76713622e-01 2.36629829e-01 -4.50669348e-01 4.31915186e-02 -1.06906319e+00 -5.08876860e-01 -6.06914341e-01 5.94727457e-01 1.42628968e-01 5.01432478e-01 -3.97963554e-01 3.01277757e-01 4.90148336e-01 -1.20784903e+00 2.84722894e-01 1.14663625e+00 8.01363587e-01 9.16841626e-02 2.69352376e-01 -3.62176180e-01 5.06722331e-01 -9.70077336e-01 -1.08262634e+00 2.56061405e-01 -3.98968428e-01 -6.32713616e-01 -3.97326559e-01 2.25947902e-01 -3.68496299e-01 -4.51398194e-01 1.62534380e+00 8.09927404e-01 3.24016698e-02 4.09495026e-01 -1.08295500e+00 -7.13869035e-01 1.03009653e+00 -2.79302031e-01 2.76848406e-01 -2.11920999e-02 3.61743048e-02 9.59616601e-01 -6.57856643e-01 3.08972865e-01 1.63233232e+00 5.36875725e-01 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9016f2cf-ed36-4d29-84b9-111a0f147419	detecting-incongruity-between-news-headline	1811.07066	null	http://arxiv.org/abs/1811.07066v2	http://arxiv.org/pdf/1811.07066v2.pdf	Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder	Some news headlines mislead readers with overrated or false information, and identifying them in advance will better assist readers in choosing proper news stories to consume. This research introduces million-scale pairs of news headline and body text dataset with incongruity label, which can uniquely be utilized for detecting news stories with misleading headlines. On this dataset, we develop two neural networks with hierarchical architectures that model a complex textual representation of news articles and measure the incongruity between the headline and the body text. We also present a data augmentation method that dramatically reduces the text input size a model handles by independently investigating each paragraph of news stories, which further boosts the performance. Our experiments and qualitative evaluations demonstrate that the proposed methods outperform existing approaches and efficiently detect news stories with misleading headlines in the real world.	['Kunwoo Park', 'Hongjun Lim', 'Seunghyun Yoon', 'Seungpil Won', 'Kyomin Jung', 'Meeyoung Cha', 'Joongbo Shin']	2018-11-17	null	null	null	null	['incongruity-detection']	['natural-language-processing']	[-3.03375963e-02 1.78201169e-01 -5.33236623e-01 -4.33787256e-01 -8.55749905e-01 -5.90880215e-01 6.55079365e-01 5.56968153e-01 -3.10053855e-01 7.46254921e-01 1.20246816e+00 -2.05181316e-01 1.62569463e-01 -8.76699746e-01 -9.46996033e-01 -1.79233432e-01 2.89509326e-01 5.29420376e-01 1.25943378e-01 -4.82155174e-01 5.72324276e-01 -3.88220608e-01 -1.29603934e+00 1.11105680e+00 8.06338668e-01 8.69922578e-01 -1.73177347e-01 5.85441053e-01 -3.21369827e-01 1.47336650e+00 -1.10993266e+00 -8.06905389e-01 -2.33733535e-01 -5.04528284e-01 -7.57937968e-01 2.21589267e-01 5.80797255e-01 -6.09052718e-01 -3.68887961e-01 1.15316904e+00 4.82393473e-01 -2.55693287e-01 5.35204291e-01 -8.12079370e-01 -1.00531268e+00 1.66544068e+00 -8.17087829e-01 7.62580514e-01 5.27112961e-01 -4.45688546e-01 1.23350430e+00 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382f27c4-ee33-4617-93b4-964cdca0634d	a-scale-independent-multi-objective	2302.04179	null	https://arxiv.org/abs/2302.04179v4	https://arxiv.org/pdf/2302.04179v4.pdf	A Scale-Independent Multi-Objective Reinforcement Learning with Convergence Analysis	Many sequential decision-making problems need optimization of different objectives which possibly conflict with each other. The conventional way to deal with a multi-task problem is to establish a scalar objective function based on a linear combination of different objectives. However, for the case of having conflicting objectives with different scales, this method needs a trial-and-error approach to properly find proper weights for the combination. As such, in most cases, this approach cannot guarantee an optimal Pareto solution. In this paper, we develop a single-agent scale-independent multi-objective reinforcement learning on the basis of the Advantage Actor-Critic (A2C) algorithm. A convergence analysis is then done for the devised multi-objective algorithm providing a convergence-in-mean guarantee. We then perform some experiments over a multi-task problem to evaluate the performance of the proposed algorithm. Simulation results show the superiority of developed multi-objective A2C approach against the single-objective algorithm.	['Mohsen Amidzadeh']	2023-02-08	null	null	null	null	['multi-objective-reinforcement-learning']	['methodology']	[ 2.45959327e-01 -2.37263158e-01 1.18724637e-01 -1.52935073e-01 -7.71338761e-01 -1.89520463e-01 2.52787143e-01 5.12497962e-01 -8.15728009e-01 1.22312200e+00 -2.16088787e-01 -6.43812940e-02 -8.89654815e-01 -4.18310523e-01 -3.04638714e-01 -1.09637725e+00 8.62368196e-03 7.14531124e-01 2.05238964e-02 -2.82560199e-01 5.77083588e-01 1.20905548e-01 -1.30798054e+00 -3.08017343e-01 1.20700467e+00 8.91922951e-01 5.62776566e-01 7.27420628e-01 7.39992037e-02 2.32870385e-01 -7.83301592e-01 -3.31909768e-02 3.44367117e-01 -4.18893963e-01 -4.00587559e-01 3.08906585e-01 -3.35225254e-01 3.76349390e-02 8.40236485e-01 1.08836830e+00 7.76590049e-01 4.73087341e-01 7.18002617e-01 -1.35610008e+00 -9.94960293e-02 3.03482085e-01 -9.18789566e-01 2.46998683e-01 1.05634533e-01 2.18965217e-01 8.24994266e-01 -3.69042724e-01 1.68865025e-01 1.22825241e+00 2.15233281e-01 1.72662333e-01 -1.18149567e+00 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9323b692-9302-483d-92f7-d08d3a9c5dc4	are-transformers-effective-for-time-series	2205.13504	null	https://arxiv.org/abs/2205.13504v3	https://arxiv.org/pdf/2205.13504v3.pdf	Are Transformers Effective for Time Series Forecasting?	Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task. Despite the growing performance over the past few years, we question the validity of this line of research in this work. Specifically, Transformers is arguably the most successful solution to extract the semantic correlations among the elements in a long sequence. However, in time series modeling, we are to extract the temporal relations in an ordered set of continuous points. While employing positional encoding and using tokens to embed sub-series in Transformers facilitate preserving some ordering information, the nature of the \emph{permutation-invariant} self-attention mechanism inevitably results in temporal information loss. To validate our claim, we introduce a set of embarrassingly simple one-layer linear models named LTSF-Linear for comparison. Experimental results on nine real-life datasets show that LTSF-Linear surprisingly outperforms existing sophisticated Transformer-based LTSF models in all cases, and often by a large margin. Moreover, we conduct comprehensive empirical studies to explore the impacts of various design elements of LTSF models on their temporal relation extraction capability. We hope this surprising finding opens up new research directions for the LTSF task. We also advocate revisiting the validity of Transformer-based solutions for other time series analysis tasks (e.g., anomaly detection) in the future. Code is available at: \url{https://github.com/cure-lab/LTSF-Linear}.	['Qiang Xu', 'Lei Zhang', 'Muxi Chen', 'Ailing Zeng']	2022-05-26	null	null	null	null	['temporal-relation-extraction']	['natural-language-processing']	[ 5.84353507e-02 -1.73343346e-01 -2.97916979e-01 -3.82349342e-01 -6.82562947e-01 -7.73157895e-01 6.70193911e-01 1.96018681e-01 -2.72812671e-03 3.16040188e-01 4.17713881e-01 -7.71002948e-01 -4.06911075e-01 -5.79186618e-01 -6.51604474e-01 -6.05311155e-01 -6.31723106e-01 -1.23939281e-02 2.29219105e-02 -2.69637644e-01 1.45484775e-01 3.51953566e-01 -1.55895030e+00 3.18738282e-01 7.92581737e-01 1.38169324e+00 -1.47406489e-01 1.98985025e-01 -8.29764009e-02 1.06335449e+00 -3.36852878e-01 -4.56129909e-01 1.85428068e-01 -2.25749686e-01 -8.35374296e-01 -2.27400362e-01 -4.06814516e-02 -1.87482372e-01 -3.94730866e-01 8.29066277e-01 2.27522284e-01 6.53969124e-02 2.66560793e-01 -1.54606700e+00 -6.56117141e-01 8.57423186e-01 -2.65778810e-01 6.32320225e-01 3.56296986e-01 1.21937886e-01 1.43511522e+00 -7.96787977e-01 3.40738505e-01 8.73350441e-01 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-4.60434616e-01 -7.92526305e-01 -3.93186599e-01 5.25854290e-01 4.22049552e-01 -5.64611375e-01 -3.81986171e-01 1.51515808e-02]	[7.003968715667725, 2.935858726501465]
7bccb47f-5bfa-444d-9361-14440b35c04a	consistency-training-with-virtual-adversarial	2104.07284	null	https://arxiv.org/abs/2104.07284v2	https://arxiv.org/pdf/2104.07284v2.pdf	Consistency Training with Virtual Adversarial Discrete Perturbation	Consistency training regularizes a model by enforcing predictions of original and perturbed inputs to be similar. Previous studies have proposed various augmentation methods for the perturbation but are limited in that they are agnostic to the training model. Thus, the perturbed samples may not aid in regularization due to their ease of classification from the model. In this context, we propose an augmentation method of adding a discrete noise that would incur the highest divergence between predictions. This virtual adversarial discrete noise obtained by replacing a small portion of tokens while keeping original semantics as much as possible efficiently pushes a training model's decision boundary. Experimental results show that our proposed method outperforms other consistency training baselines with text editing, paraphrasing, or a continuous noise on semi-supervised text classification tasks and a robustness benchmark	['Jaewoo Kang', 'Gyuwan Kim', 'Jungsoo Park']	2021-04-15	null	https://aclanthology.org/2022.naacl-main.414	https://aclanthology.org/2022.naacl-main.414.pdf	naacl-2022-7	['semi-supervised-text-classification-1']	['natural-language-processing']	[ 4.69299912e-01 5.05182743e-01 -2.99900711e-01 -5.52331626e-01 -7.28474915e-01 -6.10547006e-01 7.01453745e-01 2.35626027e-01 -3.21834236e-01 8.80737901e-01 1.92305237e-01 -2.86549747e-01 4.23670590e-01 -6.85241699e-01 -1.09071064e+00 -4.89602655e-01 4.78239208e-01 3.69890094e-01 9.32239369e-02 -2.51441479e-01 1.72829166e-01 1.39907226e-01 -1.05422068e+00 5.08037865e-01 1.17961824e+00 7.75758982e-01 -4.22173999e-02 2.61529654e-01 -9.49068516e-02 8.37765276e-01 -8.26269090e-01 -7.91244686e-01 5.00625491e-01 -5.17833948e-01 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43e5a6be-a1db-4471-97bd-6fb0d8f8cc8c	privacy-against-real-time-speech-emotion	2211.09273	null	https://arxiv.org/abs/2211.09273v1	https://arxiv.org/pdf/2211.09273v1.pdf	Privacy against Real-Time Speech Emotion Detection via Acoustic Adversarial Evasion of Machine Learning	Emotional Surveillance is an emerging area with wide-reaching privacy concerns. These concerns are exacerbated by ubiquitous IoT devices with multiple sensors that can support these surveillance use cases. The work presented here considers one such use case: the use of a speech emotion recognition (SER) classifier tied to a smart speaker. This work demonstrates the ability to evade black-box SER classifiers tied to a smart speaker without compromising the utility of the smart speaker. This privacy concern is considered through the lens of adversarial evasion of machine learning. Our solution, Defeating Acoustic Recognition of Emotion via Genetic Programming (DARE-GP), uses genetic programming to generate non-invasive additive audio perturbations (AAPs). By constraining the evolution of these AAPs, transcription accuracy can be protected while simultaneously degrading SER classifier performance. The additive nature of these AAPs, along with an approach that generates these AAPs for a fixed set of users in an utterance and user location-independent manner, supports real-time, real-world evasion of SER classifiers. DARE-GP's use of spectral features, which underlay the emotional content of speech, allows the transferability of AAPs to previously unseen black-box SER classifiers. Further, DARE-GP outperforms state-of-the-art SER evasion techniques and is robust against defenses employed by a knowledgeable adversary. The evaluations in this work culminate with acoustic evaluations against two off-the-shelf commercial smart speakers, where a single AAP could evade a black box classifier over 70% of the time. The final evaluation deployed AAP playback on a small-form-factor system (raspberry pi) integrated with a wake-word system to evaluate the efficacy of a real-world, real-time deployment where DARE-GP is automatically invoked with the smart speaker's wake word.	['Asif Salekin', 'Avery Gump', 'Yi Xiao', 'Brian Testa']	2022-11-17	null	null	null	null	['speech-emotion-recognition']	['speech']	[ 4.13521320e-01 5.16781092e-01 4.85885710e-01 -1.84508011e-01 -1.05092752e+00 -1.00647783e+00 3.34552974e-01 -2.69469440e-01 -3.21328372e-01 5.20298600e-01 -7.35315681e-02 -4.07425761e-01 2.62196213e-02 -4.55969006e-01 -7.44777203e-01 -9.22602117e-01 -3.52330118e-01 -1.06803171e-01 -2.99862474e-02 -3.02094162e-01 -4.83182997e-01 5.27387977e-01 -1.75639260e+00 3.62190217e-01 2.43201911e-01 1.47881615e+00 -6.20007575e-01 1.06959224e+00 7.32832551e-01 3.39102536e-01 -1.30481315e+00 -3.77672136e-01 5.19397259e-01 -2.89115272e-02 3.64963734e-03 -5.99441171e-01 7.40257725e-02 -2.79276311e-01 2.23146543e-01 7.86978960e-01 9.47970986e-01 -7.98955038e-02 7.12918118e-02 -1.86378109e+00 -1.07385859e-01 2.85771102e-01 5.50392736e-03 -1.55049533e-01 8.27052832e-01 2.92771310e-01 5.99351883e-01 2.99166124e-02 2.85758264e-02 1.10494506e+00 8.94186199e-01 7.73917437e-01 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af1e211e-1fe3-4eb4-9dc1-8a6fccb27228	a-pilot-study-of-text-to-sql-semantic-parsing	2010.01891	null	https://arxiv.org/abs/2010.01891v1	https://arxiv.org/pdf/2010.01891v1.pdf	A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese	Semantic parsing is an important NLP task. However, Vietnamese is a low-resource language in this research area. In this paper, we present the first public large-scale Text-to-SQL semantic parsing dataset for Vietnamese. We extend and evaluate two strong semantic parsing baselines EditSQL (Zhang et al., 2019) and IRNet (Guo et al., 2019) on our dataset. We compare the two baselines with key configurations and find that: automatic Vietnamese word segmentation improves the parsing results of both baselines; the normalized pointwise mutual information (NPMI) score (Bouma, 2009) is useful for schema linking; latent syntactic features extracted from a neural dependency parser for Vietnamese also improve the results; and the monolingual language model PhoBERT for Vietnamese (Nguyen and Nguyen, 2020) helps produce higher performances than the recent best multilingual language model XLM-R (Conneau et al., 2020).	['Dat Quoc Nguyen', 'Mai Hoang Dao', 'Anh Tuan Nguyen']	2020-10-05	null	https://aclanthology.org/2020.findings-emnlp.364	https://aclanthology.org/2020.findings-emnlp.364.pdf	findings-of-the-association-for-computational	['vietnamese-word-segmentation']	['natural-language-processing']	[-1.24744773e-01 4.70047832e-01 -3.29959184e-01 -6.61415577e-01 -1.36487865e+00 -8.90892446e-01 2.81412333e-01 3.08472365e-01 -7.42257655e-01 9.72353637e-01 6.18272662e-01 -3.54482144e-01 2.82112032e-01 -9.25682604e-01 -9.09118593e-01 -8.20696354e-02 1.60143390e-01 7.62267113e-01 3.65478605e-01 -3.76958698e-01 2.77590245e-01 -5.24052139e-03 -9.48721290e-01 5.11109531e-01 1.29081655e+00 3.53079259e-01 6.13794506e-01 4.27268505e-01 -5.01957119e-01 5.49245954e-01 -5.53407431e-01 -9.34085667e-01 1.24457203e-01 -3.72946382e-01 -1.17845511e+00 -6.77776158e-01 3.76149744e-01 1.11743286e-02 1.82293296e-01 1.16509914e+00 5.05972743e-01 -1.51428178e-01 2.90916473e-01 -9.23750103e-01 -7.95524538e-01 1.30612624e+00 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11e3d928-2538-4dd9-b6d4-aa73935cc982	identification-of-novel-classes-for-improving	2303.10422	null	https://arxiv.org/abs/2303.10422v1	https://arxiv.org/pdf/2303.10422v1.pdf	Identification of Novel Classes for Improving Few-Shot Object Detection	Conventional training of deep neural networks requires a large number of the annotated image which is a laborious and time-consuming task, particularly for rare objects. Few-shot object detection (FSOD) methods offer a remedy by realizing robust object detection using only a few training samples per class. An unexplored challenge for FSOD is that instances from unlabeled novel classes that do not belong to the fixed set of training classes appear in the background. These objects behave similarly to label noise, leading to FSOD performance degradation. We develop a semi-supervised algorithm to detect and then utilize these unlabeled novel objects as positive samples during training to improve FSOD performance. Specifically, we propose a hierarchical ternary classification region proposal network (HTRPN) to localize the potential unlabeled novel objects and assign them new objectness labels. Our improved hierarchical sampling strategy for the region proposal network (RPN) also boosts the perception ability of the object detection model for large objects. Our experimental results indicate that our method is effective and outperforms the existing state-of-the-art (SOTA) FSOD methods.	['Mohammad Rostami', 'Zeyu Shangguan']	2023-03-18	null	null	null	null	['robust-object-detection', 'few-shot-object-detection']	['computer-vision', 'computer-vision']	[ 2.82433540e-01 4.03347723e-02 -2.87899584e-01 -3.46814483e-01 -5.34167767e-01 -2.85474002e-01 3.92268121e-01 5.79461306e-02 -5.02600491e-01 6.39986634e-01 -4.42393899e-01 2.12233812e-01 2.68354356e-01 -8.26558590e-01 -6.56395137e-01 -8.42325747e-01 1.97905943e-01 4.12533671e-01 1.18741167e+00 2.66552478e-01 5.03819287e-02 7.64042735e-01 -1.88242316e+00 3.01760823e-01 7.75603175e-01 1.28019536e+00 7.11415708e-01 1.87039763e-01 -2.58053988e-01 8.25346351e-01 -8.30039382e-01 8.00021216e-02 3.34441096e-01 -2.91804492e-01 -4.60901856e-01 3.58240634e-01 6.27610743e-01 -6.90193892e-01 -2.95467526e-01 1.32348990e+00 5.56762576e-01 5.04407287e-01 7.26313114e-01 -1.31286061e+00 -6.01747990e-01 5.28685689e-01 -7.70239592e-01 6.27840698e-01 -4.13624465e-01 4.95115705e-02 6.77322626e-01 -1.48180711e+00 5.38923800e-01 1.30174601e+00 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88108cc7-ced0-4b35-a472-86a008f2ef8d	extreme-q-learning-maxent-rl-without-entropy	2301.02328	null	https://arxiv.org/abs/2301.02328v2	https://arxiv.org/pdf/2301.02328v2.pdf	Extreme Q-Learning: MaxEnt RL without Entropy	Modern Deep Reinforcement Learning (RL) algorithms require estimates of the maximal Q-value, which are difficult to compute in continuous domains with an infinite number of possible actions. In this work, we introduce a new update rule for online and offline RL which directly models the maximal value using Extreme Value Theory (EVT), drawing inspiration from economics. By doing so, we avoid computing Q-values using out-of-distribution actions which is often a substantial source of error. Our key insight is to introduce an objective that directly estimates the optimal soft-value functions (LogSumExp) in the maximum entropy RL setting without needing to sample from a policy. Using EVT, we derive our \emph{Extreme Q-Learning} framework and consequently online and, for the first time, offline MaxEnt Q-learning algorithms, that do not explicitly require access to a policy or its entropy. Our method obtains consistently strong performance in the D4RL benchmark, outperforming prior works by \emph{10+ points} on the challenging Franka Kitchen tasks while offering moderate improvements over SAC and TD3 on online DM Control tasks. Visualizations and code can be found on our website at https://div99.github.io/XQL/.	['Stefano Ermon', 'Matthieu Geist', 'Joey Hejna', 'Divyansh Garg']	2023-01-05	null	null	null	null	['d4rl']	['robots']	[-3.89231175e-01 2.21108675e-01 -4.59626198e-01 -1.42908603e-01 -1.01981139e+00 -7.01338112e-01 3.96289438e-01 1.46317989e-01 -7.57082939e-01 1.20305383e+00 -1.18925437e-01 -5.81681013e-01 -3.96003902e-01 -6.22175932e-01 -8.90578985e-01 -6.93177938e-01 -2.22076356e-01 5.42004108e-01 -3.53059977e-01 -1.83752209e-01 2.62526989e-01 3.46719846e-02 -1.06589782e+00 -3.16024244e-01 1.06774414e+00 1.32623672e+00 1.91861629e-01 6.33695662e-01 1.84721068e-01 9.03434396e-01 -4.23443913e-01 -3.70343387e-01 6.51855052e-01 -4.50429112e-01 -5.77366710e-01 -1.59888014e-01 -6.00811932e-03 -9.06289220e-01 -1.26640961e-01 1.09774446e+00 6.65297389e-01 4.79077816e-01 5.54737628e-01 -1.22160876e+00 -5.68786740e-01 7.35779166e-01 -6.04093075e-01 2.26381458e-02 9.78460386e-02 6.79298878e-01 1.32530916e+00 -3.62976074e-01 3.66129637e-01 1.14713621e+00 3.47855181e-01 4.91013050e-01 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a4f561cc-a4de-4ba1-b952-a0186f2dc6dc	dynamic-adaptive-threshold-based-learning-for	2208.10221	null	https://arxiv.org/abs/2208.10221v1	https://arxiv.org/pdf/2208.10221v1.pdf	Dynamic Adaptive Threshold based Learning for Noisy Annotations Robust Facial Expression Recognition	The real-world facial expression recognition (FER) datasets suffer from noisy annotations due to crowd-sourcing, ambiguity in expressions, the subjectivity of annotators and inter-class similarity. However, the recent deep networks have strong capacity to memorize the noisy annotations leading to corrupted feature embedding and poor generalization. To handle noisy annotations, we propose a dynamic FER learning framework (DNFER) in which clean samples are selected based on dynamic class specific threshold during training. Specifically, DNFER is based on supervised training using selected clean samples and unsupervised consistent training using all the samples. During training, the mean posterior class probabilities of each mini-batch is used as dynamic class-specific threshold to select the clean samples for supervised training. This threshold is independent of noise rate and does not need any clean data unlike other methods. In addition, to learn from all samples, the posterior distributions between weakly-augmented image and strongly-augmented image are aligned using an unsupervised consistency loss. We demonstrate the robustness of DNFER on both synthetic as well as on real noisy annotated FER datasets like RAFDB, FERPlus, SFEW and AffectNet.	['S Balasubramanian', 'Bobbili Veerendra Raj Kumar', 'Naveen Siva Kumar Badveeti', 'Darshan Gera']	2022-08-22	null	null	null	null	['facial-expression-recognition']	['computer-vision']	[ 1.55655205e-01 1.39173329e-01 1.47848189e-01 -9.94394064e-01 -8.15145195e-01 -2.83421069e-01 2.91070461e-01 -2.77824461e-01 -6.73908830e-01 9.86151040e-01 3.22269127e-02 7.76236773e-01 1.27721995e-01 -3.95272672e-01 -7.17368126e-01 -9.75332797e-01 -3.99203151e-02 2.44882286e-01 2.93957070e-02 -2.70459503e-01 -4.51817155e-01 2.88695782e-01 -1.62700987e+00 5.27230561e-01 6.64918244e-01 1.57227612e+00 -8.06867629e-02 2.26646900e-01 -2.17825577e-01 9.60214376e-01 -8.13892007e-01 -7.88277090e-01 1.94671482e-01 -4.02854711e-01 -6.42387092e-01 3.48198771e-01 3.52470845e-01 -1.89227268e-01 -1.00450538e-01 1.44603848e+00 6.75481260e-01 2.45296583e-01 3.09458792e-01 -1.41439211e+00 -5.66415370e-01 3.95721167e-01 -6.01722360e-01 -6.23681955e-02 2.64325887e-01 1.24017656e-01 6.66068435e-01 -1.22999060e+00 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7.47053087e-01 -7.27358103e-01 -4.58847046e-01 -1.13808775e+00 -7.07663536e-01 8.23421419e-01 3.52733254e-01 -8.38132203e-01 -5.50431967e-01 3.16268295e-01]	[13.640731811523438, 1.7090647220611572]
211055d0-6197-418c-a8f6-7873338672f0	spatially-selective-deep-non-linear-filters	2211.0242	null	https://arxiv.org/abs/2211.02420v2	https://arxiv.org/pdf/2211.02420v2.pdf	Spatially Selective Deep Non-linear Filters for Speaker Extraction	In a scenario with multiple persons talking simultaneously, the spatial characteristics of the signals are the most distinct feature for extracting the target signal. In this work, we develop a deep joint spatial-spectral non-linear filter that can be steered in an arbitrary target direction. For this we propose a simple and effective conditioning mechanism, which sets the initial state of the filter's recurrent layers based on the target direction. We show that this scheme is more effective than the baseline approach and increases the flexibility of the filter at no performance cost. The resulting spatially selective non-linear filters can also be used for speech separation of an arbitrary number of speakers and enable very accurate multi-speaker localization as we demonstrate in this paper.	['Timo Gerkmann', 'Kristina Tesch']	2022-11-04	null	null	null	null	['speech-separation']	['speech']	[ 6.37548491e-02 -2.10246876e-01 1.48974478e-01 -3.94619018e-01 -1.05142975e+00 -6.91594779e-01 5.38943946e-01 -5.00480771e-01 -4.35946822e-01 6.65062368e-01 4.52651143e-01 -8.08292255e-03 -2.40378588e-01 -2.75853485e-01 -4.16447014e-01 -9.45217550e-01 2.34659016e-02 2.36802369e-01 3.71850818e-01 -1.47879705e-01 -3.09747159e-02 7.21297085e-01 -1.42184687e+00 4.68946666e-01 4.53231603e-01 7.70882368e-01 3.38468283e-01 9.35061574e-01 3.49147469e-01 3.36881548e-01 -7.91833162e-01 2.30724841e-01 1.79255217e-01 -5.85931540e-01 -5.43289721e-01 1.13044620e-01 4.87612933e-01 6.69470578e-02 -4.56322998e-01 9.29933012e-01 1.00271857e+00 4.32737917e-01 5.17989516e-01 -5.74613273e-01 -1.22420549e-01 8.39097321e-01 -3.40648234e-01 5.23717642e-01 3.09246987e-01 -1.38887674e-01 8.58833969e-01 -9.19937372e-01 1.18195757e-01 1.47055387e+00 7.01501608e-01 6.94914103e-01 -1.26490128e+00 -6.61354363e-01 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e52ee6f7-6417-44bf-9730-29b630e5776c	modernizing-old-photos-using-multiple	2304.04461	null	https://arxiv.org/abs/2304.04461v1	https://arxiv.org/pdf/2304.04461v1.pdf	Modernizing Old Photos Using Multiple References via Photorealistic Style Transfer	This paper firstly presents old photo modernization using multiple references by performing stylization and enhancement in a unified manner. In order to modernize old photos, we propose a novel multi-reference-based old photo modernization (MROPM) framework consisting of a network MROPM-Net and a novel synthetic data generation scheme. MROPM-Net stylizes old photos using multiple references via photorealistic style transfer (PST) and further enhances the results to produce modern-looking images. Meanwhile, the synthetic data generation scheme trains the network to effectively utilize multiple references to perform modernization. To evaluate the performance, we propose a new old photos benchmark dataset (CHD) consisting of diverse natural indoor and outdoor scenes. Extensive experiments show that the proposed method outperforms other baselines in performing modernization on real old photos, even though no old photos were used during training. Moreover, our method can appropriately select styles from multiple references for each semantic region in the old photo to further improve the modernization performance.	['Munchurl Kim', 'Jae-Ho Lee', 'Hyeonjun Sim', 'Soo Ye Kim', 'Agus Gunawan']	2023-04-10	null	http://openaccess.thecvf.com//content/CVPR2023/html/Gunawan_Modernizing_Old_Photos_Using_Multiple_References_via_Photorealistic_Style_Transfer_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Gunawan_Modernizing_Old_Photos_Using_Multiple_References_via_Photorealistic_Style_Transfer_CVPR_2023_paper.pdf	cvpr-2023-1	['synthetic-data-generation', 'synthetic-data-generation']	['medical', 'miscellaneous']	[ 3.79850715e-01 -1.09216154e-01 -4.59487103e-02 -2.90013969e-01 -4.67816800e-01 -4.62524652e-01 8.79570663e-01 -6.36724412e-01 -5.91429949e-01 8.20423722e-01 3.22494119e-01 6.61710799e-02 3.67526948e-01 -8.44841897e-01 -9.43606198e-01 -5.45435965e-01 6.74013257e-01 1.62186682e-01 1.37545839e-01 -3.59265476e-01 4.46516484e-01 5.84886968e-01 -1.42421007e+00 -6.00050576e-02 1.29961169e+00 6.24213934e-01 5.51895618e-01 6.12436891e-01 -7.39980862e-02 1.96664706e-01 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cda56a79-63f1-45a1-951e-f4fd3b270142	group-activity-recognition-using-self	2303.12149	null	https://arxiv.org/abs/2303.12149v3	https://arxiv.org/pdf/2303.12149v3.pdf	SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition	In this paper, we propose a new, simple, and effective Self-supervised Spatio-temporal Transformers (SPARTAN) approach to Group Activity Recognition (GAR) using unlabeled video data. Given a video, we create local and global Spatio-temporal views with varying spatial patch sizes and frame rates. The proposed self-supervised objective aims to match the features of these contrasting views representing the same video to be consistent with the variations in spatiotemporal domains. To the best of our knowledge, the proposed mechanism is one of the first works to alleviate the weakly supervised setting of GAR using the encoders in video transformers. Furthermore, using the advantage of transformer models, our proposed approach supports long-term relationship modeling along spatio-temporal dimensions. The proposed SPARTAN approach performs well on two group activity recognition benchmarks, including NBA and Volleyball datasets, by surpassing the state-of-the-art results by a significant margin in terms of MCA and MPCA metrics.	['Khoa Luu', 'Page Daniel Dobbs', 'Xin Li', 'Han-Seok Seo', 'Alexander H Nelson', 'Pha Nguyen', 'Naga VS Raviteja Chappa']	2023-03-06	null	null	null	null	['group-activity-recognition']	['computer-vision']	[ 9.90496427e-02 -4.00791645e-01 -6.82879508e-01 -2.78444678e-01 -4.99979585e-01 -4.49917436e-01 7.92568266e-01 1.69172026e-02 -1.94750056e-01 4.78520334e-01 3.77569258e-01 1.20064151e-02 -4.88343388e-01 -5.46286106e-01 -6.98320150e-01 -6.09626114e-01 -3.41541946e-01 2.13380456e-02 4.02536333e-01 7.85450563e-02 2.51596510e-01 2.93409139e-01 -1.73787403e+00 4.61095482e-01 6.00289285e-01 1.37587202e+00 2.64529921e-02 4.08374190e-01 2.05311820e-01 1.52371824e+00 -1.52669594e-01 -2.42948487e-01 2.49648869e-01 -6.38785005e-01 -8.00313950e-01 5.72032869e-01 6.77697778e-01 -1.22238964e-01 -6.70097947e-01 6.18271530e-01 1.36786535e-01 2.63683736e-01 3.65995675e-01 -1.27932739e+00 -5.52270889e-01 3.25345814e-01 -6.30877197e-01 6.49548948e-01 5.93564391e-01 6.54886514e-02 1.10201693e+00 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1cd669a6-0da9-4607-9a30-fab2bc36a1aa	exploiting-deep-generative-prior-for	2003.13659	null	https://arxiv.org/abs/2003.13659v4	https://arxiv.org/pdf/2003.13659v4.pdf	Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation	Learning a good image prior is a long-term goal for image restoration and manipulation. While existing methods like deep image prior (DIP) capture low-level image statistics, there are still gaps toward an image prior that captures rich image semantics including color, spatial coherence, textures, and high-level concepts. This work presents an effective way to exploit the image prior captured by a generative adversarial network (GAN) trained on large-scale natural images. As shown in Fig.1, the deep generative prior (DGP) provides compelling results to restore missing semantics, e.g., color, patch, resolution, of various degraded images. It also enables diverse image manipulation including random jittering, image morphing, and category transfer. Such highly flexible restoration and manipulation are made possible through relaxing the assumption of existing GAN-inversion methods, which tend to fix the generator. Notably, we allow the generator to be fine-tuned on-the-fly in a progressive manner regularized by feature distance obtained by the discriminator in GAN. We show that these easy-to-implement and practical changes help preserve the reconstruction to remain in the manifold of nature image, and thus lead to more precise and faithful reconstruction for real images. Code is available at https://github.com/XingangPan/deep-generative-prior.	['Ping Luo', 'Dahua Lin', 'Xingang Pan', 'Chen Change Loy', 'Bo Dai', 'Xiaohang Zhan']	2020-03-30	null	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3265_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123470256.pdf	eccv-2020-8	['image-morphing']	['computer-vision']	[ 4.64270055e-01 2.21158043e-02 2.79613473e-02 -1.88057363e-01 -7.28914082e-01 -7.02850997e-01 5.90218067e-01 -5.28371215e-01 1.20345511e-01 7.94252396e-01 3.08563560e-01 7.89294392e-02 -2.36538728e-03 -1.04667747e+00 -1.07368636e+00 -1.05655837e+00 3.88636023e-01 7.95333907e-02 2.07601637e-02 -4.01208878e-01 1.74336642e-01 5.08446455e-01 -1.53973722e+00 2.17101708e-01 1.00551736e+00 7.26163626e-01 5.44340253e-01 5.84152579e-01 1.57015786e-01 6.58642530e-01 -4.72267091e-01 -3.89362574e-01 4.61465776e-01 -7.58322835e-01 -4.39979553e-01 3.99815828e-01 3.62143099e-01 -6.15935385e-01 -5.42105019e-01 1.21457672e+00 4.63897556e-01 4.14101072e-02 5.54894984e-01 -1.18406665e+00 -1.19353318e+00 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a1cfce8b-ae48-4a50-9114-d57a86e3b5c4	scarcenet-animal-pose-estimation-with-scarce	2303.15023	null	https://arxiv.org/abs/2303.15023v1	https://arxiv.org/pdf/2303.15023v1.pdf	ScarceNet: Animal Pose Estimation with Scarce Annotations	Animal pose estimation is an important but under-explored task due to the lack of labeled data. In this paper, we tackle the task of animal pose estimation with scarce annotations, where only a small set of labeled data and unlabeled images are available. At the core of the solution to this problem setting is the use of the unlabeled data to compensate for the lack of well-labeled animal pose data. To this end, we propose the ScarceNet, a pseudo label-based approach to generate artificial labels for the unlabeled images. The pseudo labels, which are generated with a model trained with the small set of labeled images, are generally noisy and can hurt the performance when directly used for training. To solve this problem, we first use a small-loss trick to select reliable pseudo labels. Although effective, the selection process is improvident since numerous high-loss samples are left unused. We further propose to identify reusable samples from the high-loss samples based on an agreement check. Pseudo labels are re-generated to provide supervision for those reusable samples. Lastly, we introduce a student-teacher framework to enforce a consistency constraint since there are still samples that are neither reliable nor reusable. By combining the reliable pseudo label selection with the reusable sample re-labeling and the consistency constraint, we can make full use of the unlabeled data. We evaluate our approach on the challenging AP-10K dataset, where our approach outperforms existing semi-supervised approaches by a large margin. We also test on the TigDog dataset, where our approach can achieve better performance than domain adaptation based approaches when only very few annotations are available. Our code is available at the project website.	['Gim Hee Lee', 'Chen Li']	2023-03-27	null	http://openaccess.thecvf.com//content/CVPR2023/html/Li_ScarceNet_Animal_Pose_Estimation_With_Scarce_Annotations_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Li_ScarceNet_Animal_Pose_Estimation_With_Scarce_Annotations_CVPR_2023_paper.pdf	cvpr-2023-1	['animal-pose-estimation', 'pseudo-label']	['computer-vision', 'miscellaneous']	[ 1.70362368e-01 2.27360040e-01 -1.55770749e-01 -5.89917302e-01 -9.92031872e-01 -5.96253991e-01 2.97840863e-01 5.14167622e-02 -7.85707414e-01 9.76856530e-01 -2.03449726e-01 3.50842535e-01 1.97059974e-01 -3.83613437e-01 -1.12178266e+00 -6.09596491e-01 8.87696594e-02 6.98671818e-01 3.85224581e-01 -1.59518287e-01 -1.50141880e-01 1.30999193e-01 -1.59645712e+00 5.48806647e-03 9.60474133e-01 1.05006921e+00 4.22666758e-01 1.92298114e-01 1.79308861e-01 4.72063631e-01 -4.78647023e-01 -2.51447737e-01 4.05720323e-01 -4.77844477e-01 -8.60927105e-01 3.42363864e-01 5.69944501e-01 -5.59452534e-01 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a212a2aa-b86c-40d0-9e2e-d9d8f98baaaf	autoencoder-based-anomaly-detection-and	2210.08011	null	https://arxiv.org/abs/2210.08011v1	https://arxiv.org/pdf/2210.08011v1.pdf	Autoencoder based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems	Anomaly detection in large industrial cooling systems is very challenging due to the high data dimensionality, inconsistent sensor recordings, and lack of labels. The state of the art for automated anomaly detection in these systems typically relies on expert knowledge and thresholds. However, data is viewed isolated and complex, multivariate relationships are neglected. In this work, we present an autoencoder based end-to-end workflow for anomaly detection suitable for multivariate time series data in large industrial cooling systems, including explained fault localization and root cause analysis based on expert knowledge. We identify system failures using a threshold on the total reconstruction error (autoencoder reconstruction error including all sensor signals). For fault localization, we compute the individual reconstruction error (autoencoder reconstruction error for each sensor signal) allowing us to identify the signals that contribute most to the total reconstruction error. Expert knowledge is provided via look-up table enabling root-cause analysis and assignment to the affected subsystem. We demonstrated our findings in a cooling system unit including 34 sensors over a 8-months time period using 4-fold cross validation approaches and automatically created labels based on thresholds provided by domain experts. Using 4-fold cross validation, we reached a F1-score of 0.56, whereas the autoencoder results showed a higher consistency score (CS of 0.92) compared to the automatically created labels (CS of 0.62) -- indicating that the anomaly is recognized in a very stable manner. The main anomaly was found by the autoencoder and automatically created labels and was also recorded in the log files. Further, the explained fault localization highlighted the most affected component for the main anomaly in a very consistent manner.	['Jana Kemnitz', 'Clemens Heitzinger', 'Daniel Schall', 'Bernhard Haslhofer', 'Thomas Kaufmann', 'Peter Holzner', 'Andreas Stiftinger', 'Leopold Schoeffl', 'Denis Katic', 'Robin Heel', 'Stephanie Holly']	2022-10-14	null	null	null	null	['fault-localization']	['computer-code']	[ 1.8434735e-02 -4.2365436e-02 7.0848107e-01 -1.8053477e-01 -3.7960792e-01 -4.9673966e-01 6.3287131e-02 7.5829875e-01 5.8617599e-02 4.1838205e-01 -3.7075537e-01 -2.4953972e-01 -7.6377821e-01 -6.1941713e-01 -5.9335494e-01 -7.8966224e-01 -5.5710804e-01 5.1804447e-01 -1.6115341e-01 -1.5419528e-01 2.3510309e-01 5.8429509e-01 -2.0008221e+00 3.2501665e-01 5.3806841e-01 1.3676689e+00 -4.9165737e-02 6.8685776e-01 1.4773603e-01 8.1823897e-01 -1.2013782e+00 5.1438171e-01 1.5177445e-01 -4.0489951e-01 -4.8306996e-01 1.1933027e-01 1.8409364e-01 -3.7446991e-01 3.4478452e-02 7.3305148e-01 3.3095595e-01 3.3435136e-02 4.9297309e-01 -1.5423554e+00 1.2554327e-01 4.6515682e-01 1.4368103e-01 1.7939384e-01 4.8151344e-01 6.3837528e-02 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75e2bf62-1c8c-479a-8ce2-89106827be3b	advancing-incremental-few-shot-semantic	2305.10868	null	https://arxiv.org/abs/2305.10868v1	https://arxiv.org/pdf/2305.10868v1.pdf	Advancing Incremental Few-shot Semantic Segmentation via Semantic-guided Relation Alignment and Adaptation	Incremental few-shot semantic segmentation (IFSS) aims to incrementally extend a semantic segmentation model to novel classes according to only a few pixel-level annotated data, while preserving its segmentation capability on previously learned base categories. This task faces a severe semantic-aliasing issue between base and novel classes due to data imbalance, which makes segmentation results unsatisfactory. To alleviate this issue, we propose the Semantic-guided Relation Alignment and Adaptation (SRAA) method that fully considers the guidance of prior semantic information. Specifically, we first conduct Semantic Relation Alignment (SRA) in the base step, so as to semantically align base class representations to their semantics. As a result, the embeddings of base classes are constrained to have relatively low semantic correlations to categories that are different from them. Afterwards, based on the semantically aligned base categories, Semantic-Guided Adaptation (SGA) is employed during the incremental learning stage. It aims to ensure affinities between visual and semantic embeddings of encountered novel categories, thereby making the feature representations be consistent with their semantic information. In this way, the semantic-aliasing issue can be suppressed. We evaluate our model on the PASCAL VOC 2012 and the COCO dataset. The experimental results on both these two datasets exhibit its competitive performance, which demonstrates the superiority of our method.	['Qi Tian', 'Richang Hong', 'Shijie Hao', 'Yanrong Guo', 'Xin Chen', 'Yuan Zhou']	2023-05-18	null	null	null	null	['few-shot-image-segmentation', 'incremental-learning']	['computer-vision', 'methodology']	[ 5.35927892e-01 1.39243707e-01 -1.81815431e-01 -6.27868593e-01 -3.74484986e-01 -3.65994394e-01 3.64289075e-01 2.93142706e-01 -4.98178661e-01 4.42212045e-01 8.77418220e-02 2.50284702e-01 -1.64970383e-02 -8.13569069e-01 -5.86638749e-01 -7.59885132e-01 5.23198783e-01 2.58251369e-01 8.21497560e-01 -1.11908048e-01 2.32787937e-01 1.73390731e-01 -1.65828776e+00 1.46950543e-01 1.16053677e+00 9.28216100e-01 5.19164562e-01 -5.36893159e-02 -5.89689672e-01 2.29942754e-01 -3.63031745e-01 -2.00594634e-01 1.03546783e-01 -4.36529636e-01 -7.56575286e-01 4.31623966e-01 3.45550150e-01 -7.02765286e-02 -9.35514122e-02 1.40017200e+00 1.90252051e-01 4.28266466e-01 3.93186271e-01 -1.21233940e+00 -4.80454326e-01 2.60808289e-01 -6.09400928e-01 7.08587915e-02 -1.87341906e-02 3.79391164e-02 1.01645887e+00 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47cf3a73-50ba-4505-a9d6-1789665b6fd0	a-multi-purpose-and-large-scale-speech-corpus	1912.03627	null	https://arxiv.org/abs/1912.03627v1	https://arxiv.org/pdf/1912.03627v1.pdf	A Multi Purpose and Large Scale Speech Corpus in Persian and English for Speaker and Speech Recognition: the DeepMine Database	DeepMine is a speech database in Persian and English designed to build and evaluate text-dependent, text-prompted, and text-independent speaker verification, as well as Persian speech recognition systems. It contains more than 1850 speakers and 540 thousand recordings overall, more than 480 hours of speech are transcribed. It is the first public large-scale speaker verification database in Persian, the largest public text-dependent and text-prompted speaker verification database in English, and the largest public evaluation dataset for text-independent speaker verification. It has a good coverage of age, gender, and accents. We provide several evaluation protocols for each part of the database to allow for research on different aspects of speaker verification. We also provide the results of several experiments that can be considered as baselines: HMM-based i-vectors for text-dependent speaker verification, and HMM-based as well as state-of-the-art deep neural network based ASR. We demonstrate that the database can serve for training robust ASR models.	['Jan "Honza\'\' Černocký', 'Lukáš Burget', 'Hossein Zeinali']	2019-12-08	null	null	null	null	['text-independent-speaker-verification', 'text-dependent-speaker-verification']	['speech', 'speech']	[-1.67693213e-01 2.30878871e-02 -1.77972570e-01 -9.41310585e-01 -1.36209190e+00 -5.03792167e-01 7.69750059e-01 -1.95960596e-01 -5.78718960e-01 4.74025428e-01 6.05427146e-01 -6.23041570e-01 5.32982588e-01 -2.53601419e-03 -1.94013923e-01 -7.09287047e-01 4.81799468e-02 9.39004481e-01 -3.41769814e-01 -4.59412634e-01 -1.32933166e-02 5.62731683e-01 -1.18067849e+00 -2.00152144e-01 5.16228080e-01 5.24358928e-01 -7.68541843e-02 7.24474251e-01 3.35025191e-01 6.06439233e-01 -1.13543367e+00 -7.38527417e-01 -3.28953058e-01 -1.29699558e-01 -1.06698418e+00 -6.66311905e-02 7.21592844e-01 -1.86408073e-01 -6.89716458e-01 6.42827392e-01 1.25455117e+00 1.39406025e-01 4.47626233e-01 -1.04864872e+00 -8.72065842e-01 1.31091547e+00 -1.63255662e-01 4.45798874e-01 4.89491940e-01 -4.38801683e-02 7.20683634e-01 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2.01143846e-01 -1.28829300e-01 6.72895133e-01 -9.21560407e-01 -6.72133207e-01 -1.55521202e+00 -1.03635144e+00 8.92972201e-02 7.20720708e-01 -1.42675400e-01 -5.27563035e-01 -1.10474020e-01]	[14.281731605529785, 6.335227012634277]
7951f3c6-7a80-46e6-823a-87892386de8f	deep-learning-for-chemometric-and-non	1910.00391	null	https://arxiv.org/abs/1910.00391v4	https://arxiv.org/pdf/1910.00391v4.pdf	Deep learning for Chemometric and non-translational data	We propose a novel method to train deep convolutional neural networks which learn from multiple data sets of varying input sizes through weight sharing. This is an advantage in chemometrics where individual measurements represent exact chemical compounds and thus signals cannot be translated or resized without disturbing their interpretation. Our approach show superior performance compared to transfer learning when a medium sized and a small data set are trained together. While we observe a small improvement compared to individual training when two medium sized data sets are trained together, in particular through a reduction in the variance.	['Jacob Søgaard Larsen', 'Line Clemmensen']	2019-10-01	null	null	null	null	['small-data']	['computer-vision']	[ 6.91507041e-01 -1.59695596e-01 1.45695716e-01 -3.39312881e-01 -7.40234792e-01 -7.17616558e-01 5.89769125e-01 5.45538843e-01 -1.10275137e+00 1.24703813e+00 -2.16813549e-01 -1.89017564e-01 -1.13173105e-01 -9.20907855e-01 -1.25614679e+00 -9.59982574e-01 -2.01769963e-01 4.81322974e-01 1.41258081e-02 2.08973251e-02 4.89870757e-02 8.80670965e-01 -9.94368136e-01 1.88927650e-01 5.88498235e-01 1.05842745e+00 -1.47272244e-01 6.62828028e-01 -1.46185383e-01 3.84577036e-01 -8.73259127e-01 -1.28996596e-01 6.80042565e-01 -1.52521789e-01 -5.53227544e-01 -2.56339699e-01 7.44455278e-01 -8.87575671e-02 -1.16224632e-01 1.11308622e+00 7.08607495e-01 2.34700277e-01 7.74474084e-01 -8.81190598e-01 -8.11619937e-01 7.69954562e-01 -6.18266523e-01 2.32322976e-01 1.42696783e-01 6.81857318e-02 8.10305297e-01 -6.15094602e-01 3.13730389e-01 1.27854538e+00 8.27300489e-01 3.88447642e-01 -1.74404800e+00 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-8.95554423e-01 5.96147478e-01 6.33328021e-01 -6.95770383e-01 -2.72747189e-01 -4.11803909e-02]	[5.223744869232178, 5.728835105895996]
479f6c30-373b-4f70-93eb-53b52e3d7248	3d-dynamic-scene-graphs-actionable-spatial	2002.06289	null	https://arxiv.org/abs/2002.06289v2	https://arxiv.org/pdf/2002.06289v2.pdf	3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans	We present a unified representation for actionable spatial perception: 3D Dynamic Scene Graphs. Scene graphs are directed graphs where nodes represent entities in the scene (e.g. objects, walls, rooms), and edges represent relations (e.g. inclusion, adjacency) among nodes. Dynamic scene graphs (DSGs) extend this notion to represent dynamic scenes with moving agents (e.g. humans, robots), and to include actionable information that supports planning and decision-making (e.g. spatio-temporal relations, topology at different levels of abstraction). Our second contribution is to provide the first fully automatic Spatial PerceptIon eNgine(SPIN) to build a DSG from visual-inertial data. We integrate state-of-the-art techniques for object and human detection and pose estimation, and we describe how to robustly infer object, robot, and human nodes in crowded scenes. To the best of our knowledge, this is the first paper that reconciles visual-inertial SLAM and dense human mesh tracking. Moreover, we provide algorithms to obtain hierarchical representations of indoor environments (e.g. places, structures, rooms) and their relations. Our third contribution is to demonstrate the proposed spatial perception engine in a photo-realistic Unity-based simulator, where we assess its robustness and expressiveness. Finally, we discuss the implications of our proposal on modern robotics applications. 3D Dynamic Scene Graphs can have a profound impact on planning and decision-making, human-robot interaction, long-term autonomy, and scene prediction. A video abstract is available at https://youtu.be/SWbofjhyPzI	['Luca Carlone', 'Antoni Rosinol', 'Jingnan Shi', 'Arjun Gupta', 'Marcus Abate']	2020-02-15	null	null	null	null	['robot-task-planning']	['robots']	[-4.56945337e-02 3.50706428e-01 3.39479148e-01 -2.17530847e-01 3.08498502e-01 -6.00303531e-01 8.64702106e-01 3.42160583e-01 -3.59513789e-01 7.35039115e-01 1.56269863e-01 -1.31890863e-01 -3.33634287e-01 -8.95940423e-01 -8.16223681e-01 -3.26583028e-01 -6.28431082e-01 9.45857942e-01 9.15072799e-01 -4.68535602e-01 2.42435992e-01 9.52669144e-01 -2.08013749e+00 -8.38814676e-02 5.42167842e-01 7.69403934e-01 8.41203094e-01 9.56364751e-01 2.84087121e-01 8.38175356e-01 -2.80623674e-01 3.09652060e-01 2.14255452e-01 3.81652117e-02 -7.15211272e-01 2.40121126e-01 1.57596692e-01 -8.64139348e-02 -4.98998255e-01 7.04834104e-01 2.59420633e-01 5.29406011e-01 4.42107916e-01 -1.66465461e+00 -3.56882393e-01 1.24901950e-01 -1.49078742e-02 -6.24519773e-02 1.12506962e+00 2.75061339e-01 4.44602489e-01 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7f3e5114-0926-4f13-8bb0-158f3cfaab09	3d-carigan-an-end-to-end-solution-to-3d	2003.06841	null	https://arxiv.org/abs/2003.06841v2	https://arxiv.org/pdf/2003.06841v2.pdf	3D-CariGAN: An End-to-End Solution to 3D Caricature Generation from Face Photos	Caricature is a type of artistic style of human faces that attracts considerable attention in the entertainment industry. So far a few 3D caricature generation methods exist and all of them require some caricature information (e.g., a caricature sketch or 2D caricature) as input. This kind of input, however, is difficult to provide by non-professional users. In this paper, we propose an end-to-end deep neural network model that generates high-quality 3D caricatures directly from a normal 2D face photo. The most challenging issue for our system is that the source domain of face photos (characterized by normal 2D faces) is significantly different from the target domain of 3D caricatures (characterized by 3D exaggerated face shapes and textures). To address this challenge, we: (1) build a large dataset of 5,343 3D caricature meshes and use it to establish a PCA model in the 3D caricature shape space; (2) reconstruct a normal full 3D head from the input face photo and use its PCA representation in the 3D caricature shape space to establish correspondences between the input photo and 3D caricature shape; and (3) propose a novel character loss and a novel caricature loss based on previous psychological studies on caricatures. Experiments including a novel two-level user study show that our system can generate high-quality 3D caricatures directly from normal face photos.	['Juyong Zhang', 'MinJing Yu', 'Ran Yi', 'Yanan sun', 'Mengfei Xia', 'Yong-Jin Liu', 'Yu-Kun Lai', 'Zipeng Ye']	2020-03-15	null	null	null	null	['caricature']	['computer-vision']	[ 6.48509711e-02 3.20190668e-01 2.44368225e-01 -4.87800419e-01 -2.11560562e-01 -4.89060223e-01 3.89805645e-01 -7.67913640e-01 1.65989593e-01 3.23021084e-01 1.06060430e-01 3.05118598e-02 2.49020815e-01 -7.33134806e-01 -1.03604531e+00 -2.95774788e-01 3.55793297e-01 7.15203404e-01 -5.18616140e-01 -2.85426199e-01 9.16284844e-02 1.22517347e+00 -1.70094776e+00 1.18736036e-01 6.98157310e-01 9.44679260e-01 -1.74874552e-02 4.55559820e-01 -1.09471865e-01 -1.82847545e-01 -6.22560263e-01 -8.32818210e-01 5.67222774e-01 -4.72238034e-01 -1.70449093e-01 4.08460617e-01 1.07568431e+00 -4.97862041e-01 -2.46793538e-01 1.04996657e+00 5.84791541e-01 -2.20251277e-01 9.50978100e-01 -1.27584398e+00 -1.22619283e+00 1.19255632e-01 -8.55273485e-01 -5.97848475e-01 3.06084335e-01 3.30828577e-02 4.16707426e-01 -1.27458322e+00 7.51834750e-01 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-1.47884047e+00 -3.50515276e-01 -9.22399879e-01 -7.15920806e-01 8.84788871e-01 1.40205681e-01 -8.27479124e-01 -2.93180764e-01 1.46941140e-01]	[12.740900039672852, -0.2006881982088089]
0a5a7279-117f-4a79-8503-f981e583f6e9	conformal-prediction-with-missing-values	2306.02732	null	https://arxiv.org/abs/2306.02732v1	https://arxiv.org/pdf/2306.02732v1.pdf	Conformal Prediction with Missing Values	Conformal prediction is a theoretically grounded framework for constructing predictive intervals. We study conformal prediction with missing values in the covariates -- a setting that brings new challenges to uncertainty quantification. We first show that the marginal coverage guarantee of conformal prediction holds on imputed data for any missingness distribution and almost all imputation functions. However, we emphasize that the average coverage varies depending on the pattern of missing values: conformal methods tend to construct prediction intervals that under-cover the response conditionally to some missing patterns. This motivates our novel generalized conformalized quantile regression framework, missing data augmentation, which yields prediction intervals that are valid conditionally to the patterns of missing values, despite their exponential number. We then show that a universally consistent quantile regression algorithm trained on the imputed data is Bayes optimal for the pinball risk, thus achieving valid coverage conditionally to any given data point. Moreover, we examine the case of a linear model, which demonstrates the importance of our proposal in overcoming the heteroskedasticity induced by missing values. Using synthetic and data from critical care, we corroborate our theory and report improved performance of our methods.	['Yaniv Romano', 'Julie Josse', 'Aymeric Dieuleveut', 'Margaux Zaffran']	2023-06-05	null	null	null	null	['imputation', 'prediction-intervals', 'imputation', 'imputation']	['computer-vision', 'miscellaneous', 'miscellaneous', 'time-series']	[ 4.21319455e-01 5.71879804e-01 -5.25521278e-01 -7.30057418e-01 -1.38288176e+00 -5.24435222e-01 3.13093774e-02 2.88622767e-01 1.85873643e-01 1.28844893e+00 5.82601190e-01 -3.24530184e-01 -6.87487781e-01 -9.52734470e-01 -1.10958743e+00 -6.23223662e-01 -2.00758114e-01 7.10452616e-01 -3.46491337e-01 1.02965035e-01 1.15733586e-01 1.07954584e-01 -1.21688211e+00 3.16337436e-01 1.17691672e+00 6.59017205e-01 -5.14112055e-01 4.56989288e-01 2.16367885e-01 7.31021225e-01 -1.14646323e-01 -6.17579758e-01 1.76730558e-01 -4.55416143e-01 -3.81376684e-01 -5.47941029e-01 4.36820954e-01 -3.37528497e-01 1.91375524e-01 6.88958824e-01 2.63896197e-01 -3.61662060e-01 1.22241342e+00 -1.40866446e+00 -6.15203679e-01 9.33600247e-01 -5.06218970e-01 -3.42181206e-01 1.03181206e-01 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6c3e07f7-071d-4c0e-83ef-a22b074157ac	augmentation-methods-on-monophonic-audio-for	1911.12505	null	https://arxiv.org/abs/1911.12505v2	https://arxiv.org/pdf/1911.12505v2.pdf	Augmentation Methods on Monophonic Audio for Instrument Classification in Polyphonic Music	Instrument classification is one of the fields in Music Information Retrieval (MIR) that has attracted a lot of research interest. However, the majority of that is dealing with monophonic music, while efforts on polyphonic material mainly focus on predominant instrument recognition. In this paper, we propose an approach for instrument classification in polyphonic music from purely monophonic data, that involves performing data augmentation by mixing different audio segments. A variety of data augmentation techniques focusing on different sonic aspects, such as overlaying audio segments of the same genre, as well as pitch and tempo-based synchronization, are explored. We utilize Convolutional Neural Networks for the classification task, comparing shallow to deep network architectures. We further investigate the usage of a combination of the above classifiers, each trained on a single augmented dataset. An ensemble of VGG-like classifiers, trained on non-augmented, pitch-synchronized, tempo-synchronized and genre-similar excerpts, respectively, yields the best results, achieving slightly above 80% in terms of label ranking average precision (LRAP) in the IRMAS test set.ruments in over 2300 testing tracks.	['Petros Maragos', 'Christos Garoufis', 'Agelos Kratimenos', 'Kleanthis Avramidis', 'Athanasia Zlatintsi']	2019-11-28	null	null	null	null	['instrument-recognition']	['audio']	[ 3.97634625e-01 -2.33232960e-01 -1.73719719e-01 -4.94679734e-02 -9.94257152e-01 -8.33416998e-01 5.09660423e-01 2.96247274e-01 -3.63741279e-01 3.54031175e-01 2.99941182e-01 1.17535934e-01 -3.46712351e-01 -4.38531339e-01 -4.29875970e-01 -6.64195597e-01 -6.68612197e-02 1.64289266e-01 -2.23608926e-01 -2.99476027e-01 2.93475538e-01 3.20183188e-01 -2.00151753e+00 4.77114856e-01 3.88781011e-01 1.25304580e+00 -1.33915797e-01 8.28115821e-01 -7.22109228e-02 7.11778104e-01 -9.98184621e-01 -2.76161224e-01 1.56320527e-01 -4.84891355e-01 -7.41378725e-01 -1.93338782e-01 7.04578459e-01 1.36568129e-01 -1.71506450e-01 8.10348928e-01 7.67840445e-01 1.98681399e-01 4.61978793e-01 -8.97514522e-01 -1.48368374e-01 1.27538705e+00 -3.79540712e-01 2.77425528e-01 4.19172406e-01 -2.98175275e-01 1.37512708e+00 -7.15783954e-01 2.11782321e-01 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189b1beb-2d68-4f2d-b5c2-4a145ff68b34	optimistic-temporal-difference-learning-for	2111.1109	null	https://arxiv.org/abs/2111.11090v1	https://arxiv.org/pdf/2111.11090v1.pdf	Optimistic Temporal Difference Learning for 2048	Temporal difference (TD) learning and its variants, such as multistage TD (MS-TD) learning and temporal coherence (TC) learning, have been successfully applied to 2048. These methods rely on the stochasticity of the environment of 2048 for exploration. In this paper, we propose to employ optimistic initialization (OI) to encourage exploration for 2048, and empirically show that the learning quality is significantly improved. This approach optimistically initializes the feature weights to very large values. Since weights tend to be reduced once the states are visited, agents tend to explore those states which are unvisited or visited few times. Our experiments show that both TD and TC learning with OI significantly improve the performance. As a result, the network size required to achieve the same performance is significantly reduced. With additional tunings such as expectimax search, multistage learning, and tile-downgrading technique, our design achieves the state-of-the-art performance, namely an average score of 625 377 and a rate of 72% reaching 32768 tiles. In addition, for sufficiently large tests, 65536 tiles are reached at a rate of 0.02%.	['I-Chen Wu', 'Lung-Pin Chen', 'Hung Guei']	2021-11-22	null	null	null	null	['2048']	['playing-games']	[-2.92095721e-01 4.10796255e-02 -4.67300355e-01 1.40530691e-01 -9.89637852e-01 -2.39786997e-01 6.85853124e-01 4.29120027e-02 -7.26338148e-01 9.63801026e-01 1.67627603e-01 -4.07928109e-01 -1.65354628e-02 -6.43619835e-01 -5.66267908e-01 -9.76904452e-01 -7.72291780e-01 2.94011861e-01 6.29043758e-01 -6.49297908e-02 3.41943890e-01 1.49859235e-01 -1.65263534e+00 2.51167476e-01 8.91268432e-01 9.63812292e-01 -6.96982350e-03 5.97973168e-01 8.03924203e-02 6.10554516e-01 -6.82498157e-01 3.64221931e-01 4.45125282e-01 -2.85087138e-01 -6.34791017e-01 -3.84937860e-02 -1.30128711e-01 -2.17231467e-01 -1.30175382e-01 7.45423317e-01 4.81817544e-01 2.01810196e-01 3.42003882e-01 -1.06460595e+00 -3.64611186e-02 9.71348345e-01 -1.28731477e+00 1.97770402e-01 -3.66527289e-02 3.36824745e-01 7.51088142e-01 -8.71235609e-01 2.15240926e-01 1.20159960e+00 3.94790143e-01 4.13646549e-01 -1.20096803e+00 -9.68937337e-01 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9358e140-115d-47a9-86cf-ad03bce23bf8	event-event-relation-extraction-using	null	null	https://openreview.net/forum?id=USuyAFWEuY	https://openreview.net/pdf?id=USuyAFWEuY	Event-Event Relation Extraction using Probabilistic Box Embedding	To understand a story with multiple events, it is important to capture the proper relations across these events. However, existing event relation extraction (ERE) framework regards it as a multi-class classification task and do not guarantee any coherence between different relation types, such as anti-symmetry. If a phone line "died" after "storm", then it is obvious that the "storm" happened before the "died". Current framework of event relation extraction do not guarantee this coherence and thus enforces it via constraint loss function (Wang et al., 2020). In this work, we propose to modify the underlying ERE model to guarantee coherence by representing each event as a box representation (BERE) without applying explicit constraints. From our experiments, BERE also shows stronger conjunctive constraint satisfaction while performing on par or better in F1 compared to previous models with constraint injection.	['Anonymous']	2021-11-16	null	null	null	acl-arr-november-2021-11	['event-relation-extraction']	['natural-language-processing']	[ 3.04066449e-01 4.69549298e-01 -4.95656967e-01 -4.66254801e-01 -3.24468821e-01 -6.90699577e-01 9.59686816e-01 6.53674066e-01 -1.09380849e-01 1.02577889e+00 4.54453826e-01 -4.90353197e-01 -2.94705093e-01 -1.10923648e+00 -6.41801476e-01 -9.47094858e-02 -2.10075476e-03 4.32763875e-01 5.60762823e-01 -1.50055990e-01 -9.39716026e-02 2.59113222e-01 -1.68249619e+00 7.32887685e-01 4.20793980e-01 7.70204544e-01 -2.95640171e-01 3.32485020e-01 1.92696583e-02 1.16556978e+00 -8.91360521e-01 -7.45340884e-01 -1.05595179e-01 -5.72628498e-01 -1.25392878e+00 -1.95278317e-01 8.65492523e-02 -1.77149817e-01 -1.18984491e-01 6.05624735e-01 -2.08677799e-02 1.33921221e-01 8.46824110e-01 -1.61851835e+00 -2.68843383e-01 1.03231454e+00 -3.50592315e-01 2.35828876e-01 8.80548835e-01 -4.74622548e-01 1.61902857e+00 -5.64113379e-01 1.04561234e+00 8.51383567e-01 6.41109526e-01 3.38638783e-01 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faf1c000-d36c-40e1-bb5f-0a8d78238364	degradation-noise-aware-deep-unfolding	2305.04047	null	https://arxiv.org/abs/2305.04047v1	https://arxiv.org/pdf/2305.04047v1.pdf	Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising	Hyperspectral imaging (HI) has emerged as a powerful tool in diverse fields such as medical diagnosis, industrial inspection, and agriculture, owing to its ability to detect subtle differences in physical properties through high spectral resolution. However, hyperspectral images (HSIs) are often quite noisy because of narrow band spectral filtering. To reduce the noise in HSI data cubes, both model-driven and learning-based denoising algorithms have been proposed. However, model-based approaches rely on hand-crafted priors and hyperparameters, while learning-based methods are incapable of estimating the inherent degradation patterns and noise distributions in the imaging procedure, which could inform supervised learning. Secondly, learning-based algorithms predominantly rely on CNN and fail to capture long-range dependencies, resulting in limited interpretability. This paper proposes a Degradation-Noise-Aware Unfolding Network (DNA-Net) that addresses these issues. Firstly, DNA-Net models sparse noise, Gaussian noise, and explicitly represent image prior using transformer. Then the model is unfolded into an end-to-end network, the hyperparameters within the model are estimated from the noisy HSI and degradation model and utilizes them to control each iteration. Additionally, we introduce a novel U-Shaped Local-Non-local-Spectral Transformer (U-LNSA) that captures spectral correlation, local contents, and non-local dependencies simultaneously. By integrating U-LNSA into DNA-Net, we present the first Transformer-based deep unfolding HSI denoising method. Experimental results show that DNA-Net outperforms state-of-the-art methods, and the modeling of noise distributions helps in cases with heavy noise.	['Wilfried Philips', 'Hiep Luong', 'Hongyan zhang', 'Shaoguang Huang', 'Kai Feng', 'JieZhang Cao', 'Haijin Zeng']	2023-05-06	null	null	null	null	['medical-diagnosis']	['medical']	[ 5.96969903e-01 -3.91599983e-01 2.94773340e-01 -1.84137598e-01 -6.81457818e-01 -2.71281719e-01 5.93255572e-02 -1.97158217e-01 6.54219314e-02 5.72687984e-01 1.47157252e-01 4.14351039e-02 -4.97810423e-01 -8.74347150e-01 -5.46444058e-01 -1.34326899e+00 1.97156847e-01 -7.07674921e-02 1.44627839e-01 -1.78881407e-01 -2.45136067e-01 3.96667302e-01 -1.43346357e+00 2.41746366e-01 1.39792144e+00 1.28306067e+00 3.93960416e-01 2.88105637e-01 1.52817175e-01 5.56505561e-01 -2.04104066e-01 2.71405339e-01 2.60725409e-01 -6.31741524e-01 -3.12648304e-02 3.21370095e-01 -5.23912138e-04 -1.69537693e-01 -6.36028230e-01 1.46301234e+00 6.23308897e-01 2.09177196e-01 5.53620934e-01 -8.68981838e-01 -6.88329101e-01 3.52044702e-01 -8.10313523e-01 -1.76549852e-01 -1.32099152e-01 5.34947455e-01 3.87018859e-01 -6.63814962e-01 2.05894932e-01 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7875d2da-c468-486d-81f6-dffae6bf142e	continual-reasoning-non-monotonic-reasoning	2305.02171	null	https://arxiv.org/abs/2305.02171v1	https://arxiv.org/pdf/2305.02171v1.pdf	Continual Reasoning: Non-Monotonic Reasoning in Neurosymbolic AI using Continual Learning	Despite the extensive investment and impressive recent progress at reasoning by similarity, deep learning continues to struggle with more complex forms of reasoning such as non-monotonic and commonsense reasoning. Non-monotonicity is a property of non-classical reasoning typically seen in commonsense reasoning, whereby a reasoning system is allowed (differently from classical logic) to jump to conclusions which may be retracted later, when new information becomes available. Neural-symbolic systems such as Logic Tensor Networks (LTN) have been shown to be effective at enabling deep neural networks to achieve reasoning capabilities. In this paper, we show that by combining a neural-symbolic system with methods from continual learning, LTN can obtain a higher level of accuracy when addressing non-monotonic reasoning tasks. Continual learning is added to LTNs by adopting a curriculum of learning from knowledge and data with recall. We call this process Continual Reasoning, a new methodology for the application of neural-symbolic systems to reasoning tasks. Continual Reasoning is applied to a prototypical non-monotonic reasoning problem as well as other reasoning examples. Experimentation is conducted to compare and analyze the effects that different curriculum choices may have on overall learning and reasoning results. Results indicate significant improvement on the prototypical non-monotonic reasoning problem and a promising outlook for the proposed approach on statistical relational learning examples.	["Artur S. d'Avila Garcez", 'Sofoklis Kyriakopoulos']	2023-05-03	null	null	null	null	['tensor-networks', 'relational-reasoning']	['methodology', 'natural-language-processing']	[ 2.27205917e-01 4.22522098e-01 -1.32714063e-01 -6.30200028e-01 -1.58851102e-01 -3.90019000e-01 8.31357300e-01 1.43525884e-01 -4.19911057e-01 9.40989733e-01 3.78044210e-02 -6.98808730e-01 -8.64487171e-01 -1.12378776e+00 -7.33016849e-01 -3.29119533e-01 -1.56816289e-01 6.87888324e-01 3.38791639e-01 -7.20301628e-01 4.79199618e-01 7.45260119e-01 -1.56043530e+00 9.05415475e-01 6.90117419e-01 7.60753691e-01 -2.42978722e-01 3.96705270e-01 -5.12489736e-01 1.82748890e+00 -5.73292434e-01 -6.69084251e-01 -1.56892873e-02 -2.90726960e-01 -1.40642393e+00 -5.41396677e-01 6.59805298e-01 -4.34065551e-01 -1.30449533e-01 1.11650121e+00 -2.36717556e-02 4.43624616e-01 7.42664993e-01 -1.27051258e+00 -8.72183681e-01 1.38930070e+00 7.98385441e-02 3.33986789e-01 7.44431317e-01 -3.43164578e-02 1.05187130e+00 -4.09808964e-01 4.43825573e-01 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b7d90785-4972-4805-b1f2-c88abcbe4dab	exploring-the-state-of-the-art-language	2211.01736	null	https://arxiv.org/abs/2211.01736v2	https://arxiv.org/pdf/2211.01736v2.pdf	Transformers on Multilingual Clause-Level Morphology	This paper describes our winning systems in MRL: The 1st Shared Task on Multilingual Clause-level Morphology (EMNLP 2022 Workshop) designed by KUIS AI NLP team. We present our work for all three parts of the shared task: inflection, reinflection, and analysis. We mainly explore transformers with two approaches: (i) training models from scratch in combination with data augmentation, and (ii) transfer learning with prefix-tuning at multilingual morphological tasks. Data augmentation significantly improves performance for most languages in the inflection and reinflection tasks. On the other hand, Prefix-tuning on a pre-trained mGPT model helps us to adapt analysis tasks in low-data and multilingual settings. While transformer architectures with data augmentation achieved the most promising results for inflection and reinflection tasks, prefix-tuning on mGPT received the highest results for the analysis task. Our systems received 1st place in all three tasks in MRL 2022.	['Deniz Yuret', 'Gözde Gül Şahin', 'Müge Kural', 'Tilek Chubakov', 'Emre Can Acikgoz']	2022-11-03	null	null	null	null	['lemmatization', 'morphological-analysis']	['natural-language-processing', 'natural-language-processing']	[-6.35815784e-02 3.14963609e-01 -1.84180841e-01 -5.18984675e-01 -1.24913967e+00 -9.27088797e-01 5.72912276e-01 4.56857890e-01 -8.37968826e-01 7.59428561e-01 3.66104841e-01 -7.59977102e-01 1.72193170e-01 -4.92392838e-01 -8.88602853e-01 -2.54883885e-01 -1.26479805e-01 1.13264155e+00 4.03273525e-03 -7.42111444e-01 -2.44331494e-01 2.60292888e-01 -7.71094203e-01 6.84830368e-01 1.28632486e+00 6.28309906e-01 3.23843807e-01 5.16048551e-01 -4.52344030e-01 6.56689525e-01 -4.95166898e-01 -8.71705472e-01 2.80093223e-01 -1.15712769e-02 -1.01580322e+00 -5.71751118e-01 6.95014775e-01 3.01460028e-01 2.30869621e-01 9.32443976e-01 4.78685051e-01 -6.99790046e-02 3.52233559e-01 -9.23825920e-01 -9.38200712e-01 1.86239636e+00 -4.62790579e-01 4.01037961e-01 2.26588354e-01 7.16945529e-02 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4bba99f6-e09b-45b2-b7e8-8bc456bb28a2	learning-c-to-x86-translation-an-experiment	2108.07639	null	https://arxiv.org/abs/2108.07639v2	https://arxiv.org/pdf/2108.07639v2.pdf	Learning C to x86 Translation: An Experiment in Neural Compilation	Deep learning has had a significant impact on many fields. Recently, code-to-code neural models have been used in code translation, code refinement and decompilation. However, the question of whether these models can automate compilation has yet to be investigated. In this work, we explore neural compilation, building and evaluating Transformer models that learn how to produce x86 assembler from C code. Although preliminary results are relatively weak, we make our data, models and code publicly available to encourage further research in this area.	["Michael F. P. O'Boyle", 'Jordi Armengol-Estapé']	2021-08-17	null	https://openreview.net/forum?id=444ug_EYXet	https://openreview.net/pdf?id=444ug_EYXet	neurips-workshop-aiplans-2021-12	['code-translation']	['computer-code']	[-5.45461569e-03 -1.76935513e-02 -3.86010885e-01 -5.68240106e-01 -5.63061059e-01 -6.56337976e-01 4.67603445e-01 1.28999084e-01 1.50374714e-02 6.75398171e-01 2.95633107e-01 -1.08252501e+00 4.67933506e-01 -7.51822710e-01 -1.09765780e+00 -1.27855957e-01 -3.75772230e-02 6.84077144e-02 -1.93294555e-01 -2.86695927e-01 3.64713311e-01 2.53359526e-01 -1.41170204e+00 6.33030951e-01 9.27358449e-01 4.85516667e-01 1.22236833e-01 8.48047078e-01 -1.90479100e-01 1.10846019e+00 -5.32626688e-01 -4.78030443e-01 -2.61412207e-02 -5.91480076e-01 -1.05415750e+00 -4.04049844e-01 1.46582007e-01 -2.11336657e-01 7.17149153e-02 1.26343894e+00 3.72201413e-01 -3.83740902e-01 2.87732750e-01 -9.30754542e-01 -9.64068949e-01 1.15197730e+00 -2.31868759e-01 1.23262711e-01 3.20523530e-01 2.22160086e-01 8.73176992e-01 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16e3e0ff-27c6-4996-bccd-a7467a5738ef	automatic-word-association-norms-awan	null	null	https://aclanthology.org/2020.cogalex-1.17	https://aclanthology.org/2020.cogalex-1.17.pdf	Automatic Word Association Norms (AWAN)	Word Association Norms (WAN) are collections that present stimuli words and the set of their associated responses. The corpus is widely used in diverse areas of expertise. In order to reduce the effort to have a good quality resource that can be reproduced in many languages with minimum sources, a methodology to build Automatic Word Association Norms is proposed (AWAN). The methodology has an input of two simple elements: a) dictionary, and b) pre-processed Word Embeddings. This new kind of WAN is evaluated in two ways: i) learning word embeddings based on the node2vec algorithm and comparing them with human annotated benchmarks, and ii) performing a lexical search for a reverse dictionary. Both evaluations are done in a weighted graph with the AWAN lexical elements. The results showed that the methodology produces good quality AWANs.	['Helena Gomez-Adorno', 'Gemma Bel-Enguix', 'Gerardo Sierra Martínez', 'Jorge Reyes-Magaña']	null	null	null	null	coling-cogalex-2020-12	['learning-word-embeddings', 'reverse-dictionary']	['methodology', 'natural-language-processing']	[-5.93524426e-02 -1.10853612e-01 -8.79122987e-02 -1.70963526e-01 -2.41791233e-01 -4.56517667e-01 8.48277926e-01 6.87092185e-01 -1.04943335e+00 5.52506804e-01 5.85027397e-01 -2.16602281e-01 -2.95391172e-01 -9.34667528e-01 8.96053240e-02 -3.60544950e-01 -3.60875949e-03 5.34013450e-01 3.75342309e-01 -6.22648478e-01 5.12132764e-01 4.28817421e-01 -1.73012042e+00 1.37119696e-01 6.93021178e-01 6.36076868e-01 1.42235860e-01 3.64058197e-01 -6.76371157e-01 4.96746331e-01 -3.62464756e-01 -5.81337333e-01 2.32565552e-01 -4.76165682e-01 -8.68512571e-01 -2.45121658e-01 5.80118708e-02 1.99183926e-01 5.13015613e-02 1.13067424e+00 5.35774052e-01 6.11097813e-01 8.65845263e-01 -8.79922986e-01 -8.44133496e-01 7.32937515e-01 -1.44213572e-01 2.15696424e-01 6.13608658e-01 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8.11712325e-01 -2.44142875e-01 5.01831889e-01 4.44407821e-01 -5.08250833e-01 -2.87466705e-01 -1.28059614e+00 -5.78959048e-01 9.61917996e-01 -3.83557260e-01 -7.28845060e-01 -3.57002288e-01 -1.29959315e-01]	[10.43235969543457, 8.915478706359863]
4f394287-343d-445d-9ba7-7673e93d735d	stereogan-bridging-synthetic-to-real-domain	2005.01927	null	https://arxiv.org/abs/2005.01927v1	https://arxiv.org/pdf/2005.01927v1.pdf	StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching	Large-scale synthetic datasets are beneficial to stereo matching but usually introduce known domain bias. Although unsupervised image-to-image translation networks represented by CycleGAN show great potential in dealing with domain gap, it is non-trivial to generalize this method to stereo matching due to the problem of pixel distortion and stereo mismatch after translation. In this paper, we propose an end-to-end training framework with domain translation and stereo matching networks to tackle this challenge. First, joint optimization between domain translation and stereo matching networks in our end-to-end framework makes the former facilitate the latter one to the maximum extent. Second, this framework introduces two novel losses, i.e., bidirectional multi-scale feature re-projection loss and correlation consistency loss, to help translate all synthetic stereo images into realistic ones as well as maintain epipolar constraints. The effective combination of above two contributions leads to impressive stereo-consistent translation and disparity estimation accuracy. In addition, a mode seeking regularization term is added to endow the synthetic-to-real translation results with higher fine-grained diversity. Extensive experiments demonstrate the effectiveness of the proposed framework on bridging the synthetic-to-real domain gap on stereo matching.	['Hongsheng Li', 'Chengxi Yang', 'Wenxiu Sun', 'Rui Liu', 'Xiaogang Wang']	2020-05-05	stereogan-bridging-synthetic-to-real-domain-1	http://openaccess.thecvf.com/content_CVPR_2020/html/Liu_StereoGAN_Bridging_Synthetic-to-Real_Domain_Gap_by_Joint_Optimization_of_Domain_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_StereoGAN_Bridging_Synthetic-to-Real_Domain_Gap_by_Joint_Optimization_of_Domain_CVPR_2020_paper.pdf	cvpr-2020-6	['synthetic-to-real-translation']	['computer-vision']	[ 3.59451503e-01 6.63631558e-02 -1.75982654e-01 -4.26707298e-01 -7.10759938e-01 -3.81978959e-01 6.77303195e-01 -6.12320602e-01 -9.54568312e-02 9.15448189e-01 2.41721407e-01 5.02275134e-06 1.15262054e-01 -8.27563941e-01 -8.99572551e-01 -7.27312922e-01 6.09459519e-01 2.66777962e-01 1.23945564e-01 -4.24901366e-01 2.11507976e-01 2.84006149e-01 -1.37300956e+00 1.30203277e-01 1.43040514e+00 1.04607666e+00 3.37575674e-01 2.74657109e-03 -4.09281999e-02 6.08429790e-01 5.48188612e-02 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fa082731-bb5f-42fe-8356-75fd5eb93442	decoupling-pseudo-label-disambiguation-and	2305.17699	null	https://arxiv.org/abs/2305.17699v1	https://arxiv.org/pdf/2305.17699v1.pdf	Decoupling Pseudo Label Disambiguation and Representation Learning for Generalized Intent Discovery	Generalized intent discovery aims to extend a closed-set in-domain intent classifier to an open-world intent set including in-domain and out-of-domain intents. The key challenges lie in pseudo label disambiguation and representation learning. Previous methods suffer from a coupling of pseudo label disambiguation and representation learning, that is, the reliability of pseudo labels relies on representation learning, and representation learning is restricted by pseudo labels in turn. In this paper, we propose a decoupled prototype learning framework (DPL) to decouple pseudo label disambiguation and representation learning. Specifically, we firstly introduce prototypical contrastive representation learning (PCL) to get discriminative representations. And then we adopt a prototype-based label disambiguation method (PLD) to obtain pseudo labels. We theoretically prove that PCL and PLD work in a collaborative fashion and facilitate pseudo label disambiguation. Experiments and analysis on three benchmark datasets show the effectiveness of our method.	['Weiran Xu', 'Yunsen Xian', 'Jingang Wang', 'Pei Wang', 'Chen Zeng', 'Keqing He', 'Xiaoshuai Song', 'Yutao Mou']	2023-05-28	null	null	null	null	['pseudo-label', 'intent-discovery']	['miscellaneous', 'natural-language-processing']	[ 3.80379528e-01 -2.56911889e-02 -5.54634035e-01 -5.02685845e-01 -8.77856612e-01 -8.04351807e-01 7.74915516e-01 2.17549697e-01 -1.56027079e-01 5.74970007e-01 4.48018163e-01 3.53246406e-02 -2.52907276e-01 -4.68354166e-01 -1.30589381e-01 -4.83532161e-01 2.12490223e-02 5.63620329e-01 -2.02407733e-01 -1.32147029e-01 1.92706332e-01 9.20617878e-02 -1.34927702e+00 2.34617412e-01 8.71274948e-01 1.00998795e+00 1.67887852e-01 1.05220653e-01 -3.27215195e-01 1.06579351e+00 -3.80256295e-01 7.38746375e-02 3.29379767e-01 -1.55324146e-01 -1.14715898e+00 1.56354949e-01 2.15186715e-01 -1.82985723e-01 -1.92038566e-01 1.01671362e+00 1.45850480e-01 4.68147665e-01 1.03566957e+00 -1.43503416e+00 -7.77298152e-01 5.58495522e-01 -6.54997826e-01 -2.11101532e-01 7.80969322e-01 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1ba02195-00fa-4d7c-8c2e-c6e1073f07be	pa-gm-position-aware-learning-of-embedding	2301.01932	null	https://arxiv.org/abs/2301.01932v1	https://arxiv.org/pdf/2301.01932v1.pdf	PA-GM: Position-Aware Learning of Embedding Networks for Deep Graph Matching	Graph matching can be formalized as a combinatorial optimization problem, where there are corresponding relationships between pairs of nodes that can be represented as edges. This problem becomes challenging when there are potential ambiguities present due to nodes and edges with high similarity, and there is a need to find accurate results for similar content matching. In this paper, we introduce a novel end-to-end neural network that can map the linear assignment problem into a high-dimensional space augmented with node-level relative position information, which is crucial for improving the method's performance for similar content matching. Our model constructs the anchor set for the relative position of nodes and then aggregates the feature information of the target node and each anchor node based on a measure of relative position. It then learns the node feature representation by integrating the topological structure and the relative position information, thus realizing the linear assignment between the two graphs. To verify the effectiveness and generalizability of our method, we conduct graph matching experiments, including cross-category matching, on different real-world datasets. Comparisons with different baselines demonstrate the superiority of our method. Our source code is available under https://github.com/anonymous.	['Zhihong Zhang', 'Lichi Zhang', 'Yuxing Dai', 'Dongdong Chen']	2023-01-05	null	null	null	null	['graph-matching']	['graphs']	[-1.45551890e-01 7.21082911e-02 -5.05317390e-01 -4.85919625e-01 -5.98487556e-01 -5.39417863e-01 2.48169780e-01 5.24847150e-01 -6.95111752e-02 7.44431242e-02 4.10408705e-01 -1.94475427e-01 -3.68700683e-01 -1.20868027e+00 -5.71103990e-01 -3.81024897e-01 -2.68098563e-01 3.67365122e-01 -5.65511473e-02 -2.06053078e-01 1.46387324e-01 3.58501256e-01 -1.16244602e+00 -1.33614847e-02 9.35497403e-01 1.00853169e+00 1.35920852e-01 2.49197245e-01 -3.36898834e-01 1.81520954e-01 -3.10701191e-01 -5.35038352e-01 5.43974161e-01 -1.32849485e-01 -7.64377534e-01 -1.29638031e-01 8.86770427e-01 -2.67702430e-01 -8.66938174e-01 1.29285049e+00 4.81579363e-01 1.32321417e-01 4.12427574e-01 -1.63523531e+00 -1.07504320e+00 6.43220782e-01 -8.08674634e-01 -9.77231413e-02 6.38851762e-01 -1.18980117e-01 1.58315969e+00 -5.75293481e-01 4.44126427e-01 1.43867028e+00 8.65189254e-01 1.11232489e-01 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-6.16164029e-01 -5.10991454e-01 8.66354406e-01 2.50980228e-01 -6.22013271e-01 -3.37649047e-01 -5.62770844e-01]	[7.163776397705078, 6.40528678894043]
8585f9b8-b3c4-486f-acda-ad2b256b4177	a-personal-model-of-trumpery-deception	1811.01938	null	http://arxiv.org/abs/1811.01938v1	http://arxiv.org/pdf/1811.01938v1.pdf	A personal model of trumpery: Deception detection in a real-world high-stakes setting	Language use reveals information about who we are and how we feel1-3. One of the pioneers in text analysis, Walter Weintraub, manually counted which types of words people used in medical interviews and showed that the frequency of first-person singular pronouns (i.e., I, me, my) was a reliable indicator of depression, with depressed people using I more often than people who are not depressed4. Several studies have demonstrated that language use also differs between truthful and deceptive statements5-7, but not all differences are consistent across people and contexts, making prediction difficult8. Here we show how well linguistic deception detection performs at the individual level by developing a model tailored to a single individual: the current US president. Using tweets fact-checked by an independent third party (Washington Post), we found substantial linguistic differences between factually correct and incorrect tweets and developed a quantitative model based on these differences. Next, we predicted whether out-of-sample tweets were either factually correct or incorrect and achieved a 73% overall accuracy. Our results demonstrate the power of linguistic analysis in real-world deception research when applied at the individual level and provide evidence that factually incorrect tweets are not random mistakes of the sender.	['Sophie van der Zee', 'Ronald Poppe', 'Aurelien Baillon', 'Alice Havrileck']	2018-11-05	null	null	null	null	['deception-detection']	['miscellaneous']	[-1.72634602e-01 3.42416018e-02 -5.61330557e-01 -5.50108075e-01 -7.90146708e-01 -6.74433529e-01 7.06509829e-01 7.05569267e-01 -8.46180499e-01 7.09953547e-01 7.69662917e-01 -6.12548947e-01 3.04603964e-01 -6.93927705e-01 -2.49539968e-02 -1.96744218e-01 2.43184999e-01 2.14774400e-01 -4.65091884e-01 -3.26456934e-01 6.39351070e-01 4.63391185e-01 -7.10140944e-01 4.20129478e-01 8.26102555e-01 3.97407800e-01 -6.51022851e-01 4.74343717e-01 -3.40836406e-01 1.29556680e+00 -1.11888444e+00 -9.12104547e-01 -3.53195012e-01 -4.56093937e-01 -8.67593765e-01 -1.46536127e-01 3.88903558e-01 -4.68124747e-01 -1.99504972e-01 1.05530190e+00 3.36138517e-01 -2.30703697e-01 6.47317350e-01 -8.83828342e-01 -7.89232492e-01 6.62355423e-01 -4.70599622e-01 6.67508721e-01 7.77584136e-01 2.57433832e-01 6.11403823e-01 -4.25289899e-01 7.51591623e-01 1.37306678e+00 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fcb5d25a-604b-4146-ba03-f41059254fbf	generalizing-multimodal-pre-training-into	2206.11091	null	https://arxiv.org/abs/2206.11091v1	https://arxiv.org/pdf/2206.11091v1.pdf	Generalizing Multimodal Pre-training into Multilingual via Language Acquisition	English-based Vision-Language Pre-training (VLP) has achieved great success in various downstream tasks. Some efforts have been taken to generalize this success to non-English languages through Multilingual Vision-Language Pre-training (M-VLP). However, due to the large number of languages, M-VLP models often require huge computing resources and cannot be flexibly extended to new languages. In this work, we propose a \textbf{M}ulti\textbf{L}ingual \textbf{A}cquisition (MLA) framework that can easily generalize a monolingual Vision-Language Pre-training model into multilingual. Specifically, we design a lightweight language acquisition encoder based on state-of-the-art monolingual VLP models. We further propose a two-stage training strategy to optimize the language acquisition encoder, namely the Native Language Transfer stage and the Language Exposure stage. With much less multilingual training data and computing resources, our model achieves state-of-the-art performance on multilingual image-text and video-text retrieval benchmarks.	['Qin Jin', 'Anwen Hu', 'Liang Zhang']	2022-05-29	null	null	null	null	['video-text-retrieval', 'language-acquisition']	['computer-vision', 'natural-language-processing']	[-1.39309257e-01 -3.30008924e-01 -2.74917483e-01 -4.41951752e-01 -1.21239591e+00 -6.29375279e-01 9.16072488e-01 -1.02611706e-01 -1.04700077e+00 5.39483964e-01 8.86862502e-02 -7.81312108e-01 5.69412768e-01 -4.82596070e-01 -1.09148216e+00 -2.23426774e-01 4.74165589e-01 5.08595228e-01 1.99508648e-02 -2.64538914e-01 -1.63863510e-01 1.33918092e-01 -1.09113145e+00 4.99864966e-01 1.08927917e+00 4.63335961e-01 7.97478795e-01 7.37021506e-01 -3.36333036e-01 8.29940975e-01 -8.81529599e-02 -7.37424731e-01 3.17602843e-01 -2.87534058e-01 -8.56941223e-01 -3.85794193e-02 7.91550636e-01 -4.97708887e-01 -5.35406590e-01 1.14953578e+00 5.10157049e-01 -2.47731686e-01 5.00866055e-01 -1.03231990e+00 -1.21750212e+00 5.97663045e-01 -6.49956167e-01 3.80331352e-02 4.14594471e-01 2.27110833e-01 8.34598243e-01 -1.46610141e+00 7.68887758e-01 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4ba94ca3-075e-4ac6-a9b1-4a1137e317c3	quasi-score-matching-estimation-for-spatial	2305.19721	null	https://arxiv.org/abs/2305.19721v1	https://arxiv.org/pdf/2305.19721v1.pdf	Quasi-Score Matching Estimation for Spatial Autoregressive Model with Random Weights Matrix and Regressors	With the rapid advancements in technology for data collection, the application of the spatial autoregressive (SAR) model has become increasingly prevalent in real-world analysis, particularly when dealing with large datasets. However, the commonly used quasi-maximum likelihood estimation (QMLE) for the SAR model is not computationally scalable to handle the data with a large size. In addition, when establishing the asymptotic properties of the parameter estimators of the SAR model, both weights matrix and regressors are assumed to be nonstochastic in classical spatial econometrics, which is perhaps not realistic in real applications. Motivated by the machine learning literature, this paper proposes quasi-score matching estimation for the SAR model. This new estimation approach is still likelihood-based, but significantly reduces the computational complexity of the QMLE. The asymptotic properties of parameter estimators under the random weights matrix and regressors are established, which provides a new theoretical framework for the asymptotic inference of the SAR-type models. The usefulness of the quasi-score matching estimation and its asymptotic inference is illustrated via extensive simulation studies and a case study of an anti-conflict social network experiment for middle school students.	['Tao Zou', 'Xuan Liang']	2023-05-31	null	null	null	null	['econometrics']	['miscellaneous']	[ 6.86225966e-02 -1.03252083e-01 -3.40307534e-01 -2.75137752e-01 -6.83956921e-01 -1.46340251e-01 4.35989708e-01 7.50893578e-02 -3.42957884e-01 8.54229867e-01 1.34216640e-02 -6.69613659e-01 -7.92403817e-01 -8.61585259e-01 -6.79507256e-01 -8.58785331e-01 -3.65913957e-01 1.11345567e-01 6.09227782e-03 1.39060035e-01 3.01886886e-01 4.82516050e-01 -1.46683300e+00 -5.80882668e-01 1.39809728e+00 6.65555716e-01 3.61327112e-01 4.15383071e-01 3.55574816e-01 5.22815228e-01 -4.20034021e-01 -4.05347347e-01 2.69907862e-01 -1.89547509e-01 -3.34395915e-01 1.15127631e-01 2.96688765e-01 -4.00411338e-01 -2.31203407e-01 1.24085808e+00 3.15808296e-01 5.79287052e-01 9.96668100e-01 -1.28691590e+00 -4.88843620e-01 4.16174352e-01 -9.63791072e-01 2.70898044e-01 1.95443094e-01 -4.05057490e-01 9.08369601e-01 -8.19403112e-01 1.63952023e-01 1.42039120e+00 6.48995280e-01 -3.20840359e-01 -1.23654747e+00 -8.46343219e-01 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6a6ea55e-6c2b-4353-88aa-4dfc501b7d12	fha-kitchens-a-novel-dataset-for-fine-grained	2306.10858	null	https://arxiv.org/abs/2306.10858v1	https://arxiv.org/pdf/2306.10858v1.pdf	FHA-Kitchens: A Novel Dataset for Fine-Grained Hand Action Recognition in Kitchen Scenes	A typical task in the field of video understanding is hand action recognition, which has a wide range of applications. Existing works either mainly focus on full-body actions, or the defined action categories are relatively coarse-grained. In this paper, we propose FHA-Kitchens, a novel dataset of fine-grained hand actions in kitchen scenes. In particular, we focus on human hand interaction regions and perform deep excavation to further refine hand action information and interaction regions. Our FHA-Kitchens dataset consists of 2,377 video clips and 30,047 images collected from 8 different types of dishes, and all hand interaction regions in each image are labeled with high-quality fine-grained action classes and bounding boxes. We represent the action information in each hand interaction region as a triplet, resulting in a total of 878 action triplets. Based on the constructed dataset, we benchmark representative action recognition and detection models on the following three tracks: (1) supervised learning for hand interaction region and object detection, (2) supervised learning for fine-grained hand action recognition, and (3) intra- and inter-class domain generalization for hand interaction region detection. The experimental results offer compelling empirical evidence that highlights the challenges inherent in fine-grained hand action recognition, while also shedding light on potential avenues for future research, particularly in relation to pre-training strategy, model design, and domain generalization. The dataset will be released at https://github.com/tingZ123/FHA-Kitchens.	['DaCheng Tao', 'Yonggang Wen', 'Bo Du', 'Han Hu', 'Yong Luo', 'Jing Zhang', 'YongQian Li', 'Ting Zhe']	2023-06-19	null	null	null	null	['action-recognition-in-videos', 'video-understanding', 'domain-generalization']	['computer-vision', 'computer-vision', 'methodology']	[ 4.9363843e-01 -2.6690355e-01 -6.1807913e-01 -5.7312835e-02 -5.8866650e-01 -6.9790280e-01 3.9695442e-01 -5.5426037e-01 -1.3674207e-02 3.7605420e-01 6.5979034e-01 1.1934051e-01 -2.7543488e-01 -5.8610958e-01 -7.0935941e-01 -8.9777654e-01 6.8585940e-02 3.7238947e-01 3.1756294e-01 6.1984800e-02 2.1343444e-01 6.2330592e-01 -1.5933627e+00 9.2507064e-01 3.0159909e-01 1.1951919e+00 1.6991210e-01 7.5228035e-01 4.4849446e-01 1.0515635e+00 -6.3836259e-01 -2.1886404e-01 4.5774975e-01 -4.9708503e-01 -1.0576601e+00 6.4390409e-01 5.4494774e-01 -7.8409541e-01 -6.0370642e-01 7.4095011e-01 3.9906213e-01 1.9002099e-01 8.6900288e-01 -1.1488901e+00 -5.1589149e-01 4.0776545e-01 -6.6320270e-01 2.5645724e-01 6.6228652e-01 4.4964427e-01 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af31d262-177a-4224-9399-81323047211f	flipping-coins-to-estimate-pseudocounts-for	2306.03186	null	https://arxiv.org/abs/2306.03186v1	https://arxiv.org/pdf/2306.03186v1.pdf	Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning	We propose a new method for count-based exploration in high-dimensional state spaces. Unlike previous work which relies on density models, we show that counts can be derived by averaging samples from the Rademacher distribution (or coin flips). This insight is used to set up a simple supervised learning objective which, when optimized, yields a state's visitation count. We show that our method is significantly more effective at deducing ground-truth visitation counts than previous work; when used as an exploration bonus for a model-free reinforcement learning algorithm, it outperforms existing approaches on most of 9 challenging exploration tasks, including the Atari game Montezuma's Revenge.	['George Konidaris', 'Akhil Bagaria', 'Sam Lobel']	2023-06-05	null	null	null	null	['montezumas-revenge']	['playing-games']	[-2.36787694e-03 6.13626912e-02 -5.74254870e-01 6.61037639e-02 -9.65777457e-01 -6.91487253e-01 7.01142550e-01 1.36888623e-01 -8.39063168e-01 1.44740641e+00 3.67102437e-02 -9.33178186e-01 -2.49117211e-01 -8.64792049e-01 -6.94295228e-01 -7.89022326e-01 -5.59394658e-01 9.98503208e-01 -1.22198820e-01 -2.15164348e-01 5.52134573e-01 2.16524690e-01 -1.13007498e+00 -4.08302009e-01 6.68863356e-01 7.87593246e-01 2.18865350e-02 8.43370914e-01 1.48682594e-01 1.00785232e+00 -6.46280050e-01 -9.45400447e-02 3.14838171e-01 -7.31383562e-01 -1.12658131e+00 -2.51593649e-01 -2.09700555e-01 -5.98339975e-01 -8.61632824e-02 1.21123946e+00 4.04413044e-02 3.53103399e-01 7.41881132e-01 -1.18487096e+00 7.01764077e-02 9.53524113e-01 -5.91905117e-01 3.43863517e-01 3.89074296e-01 3.92351240e-01 1.25201643e+00 -3.71316895e-02 7.54774272e-01 1.23860049e+00 3.78488690e-01 4.63287681e-01 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-8.85268807e-01 -9.37907219e-01 1.23596892e-01 2.69205034e-01 -1.02028704e+00 -7.04007208e-01 -6.86852694e-01]	[4.01582145690918, 1.9450535774230957]
c66d2dca-e942-46f7-8e2a-9d30c211f290	automated-heartbeat-classification-using-3-d	null	null	https://xueshu.baidu.com/usercenter/paper/show?paperid=18c6ee969fc95c965365f1209b341c19&site=xueshu_se	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8734061	Automated Heartbeat Classification Using 3-D Inputs Based on Convolutional Neural Network With Multi-Fields of View	A high-performance method of automated heartbeat classification based on Convolutional Neural Network (CNN) is proposed in this paper. To make full use of the electrocardiogram information acquired from different parts of the human body, we present a novel 3-D data structure as the input of the CNN. The 3-D structure consists of multiple feature maps, each of which indicates the information collected from one lead and contains morphological characteristic, RR-interval, and beat-to-beat correlation feature. Besides, atrous spatial pyramid pooling (ASPP) module which uses filters with different resolutions is adopted to extract deep features in multi-fields of view. Validated on the MIT-BIH arrhythmia database, the proposed method yields an overall accuracy of 91.44% in the inter-patient practice. In particular, this method achieves 89.05% and 95.15% in the sensitivities of the supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB) classes, respectively. With the high performance in detecting these two pathological classes, this method has potential clinical application.	['AND ZHENYAN LIU2', 'ZHIJIAN CHEN 1', 'YIN XU 1', 'FEITENG LI 1']	2019-06-10	null	null	null	ieeexplore-2019-6	['heartbeat-classification']	['medical']	[-1.15053624e-01 -3.63813728e-01 1.49443507e-01 -2.25901380e-01 -4.31421578e-01 -3.39485705e-01 -2.34331384e-01 1.91983938e-01 -2.82570660e-01 6.81588829e-01 -9.72338170e-02 -1.70107409e-02 -1.98499262e-01 -7.13513434e-01 4.70282137e-02 -6.79352820e-01 -3.37191731e-01 -1.41056567e-01 -1.66931406e-01 8.32439438e-02 7.31185079e-02 8.73007357e-01 -9.23364341e-01 4.04311091e-01 6.63747609e-01 1.47249901e+00 -7.74357915e-02 7.12065816e-01 3.64759475e-01 3.58864963e-01 -9.49171126e-01 4.93925810e-02 1.87384322e-01 -6.71057045e-01 -3.14672232e-01 -2.14471728e-01 -7.53650516e-02 -2.83961326e-01 -2.77221471e-01 6.31734133e-01 1.22350812e+00 -4.57841337e-01 3.94772768e-01 -7.49388933e-01 -2.62742400e-01 3.53476316e-01 -6.18507206e-01 8.51102889e-01 -1.31002411e-01 6.30435497e-02 6.12592280e-01 -9.33597267e-01 3.94515187e-01 5.32908082e-01 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f988f80f-db0a-4ae4-b9ee-071be8ec057a	weakly-supervised-video-anomaly-detection-1	2212.08506	null	https://arxiv.org/abs/2212.08506v1	https://arxiv.org/pdf/2212.08506v1.pdf	Weakly Supervised Video Anomaly Detection Based on Cross-Batch Clustering Guidance	Weakly supervised video anomaly detection (WSVAD) is a challenging task since only video-level labels are available for training. In previous studies, the discriminative power of the learned features is not strong enough, and the data imbalance resulting from the mini-batch training strategy is ignored. To address these two issues, we propose a novel WSVAD method based on cross-batch clustering guidance. To enhance the discriminative power of features, we propose a batch clustering based loss to encourage a clustering branch to generate distinct normal and abnormal clusters based on a batch of data. Meanwhile, we design a cross-batch learning strategy by introducing clustering results from previous mini-batches to reduce the impact of data imbalance. In addition, we propose to generate more accurate segment-level anomaly scores based on batch clustering guidance further improving the performance of WSVAD. Extensive experiments on two public datasets demonstrate the effectiveness of our approach.	['Yanning Zhang', 'Peng Wang', 'Shizhou Zhang', 'Xin Zhang', 'Congqi Cao']	2022-12-16	null	null	null	null	['video-anomaly-detection']	['computer-vision']	[-1.08709149e-01 -3.44600141e-01 -2.98007429e-01 -6.71650410e-01 -5.57453275e-01 -1.93555042e-01 2.18164340e-01 2.78590083e-01 -2.58655965e-01 2.70977527e-01 2.17578515e-01 -5.88499643e-02 -5.42836674e-02 -5.13325095e-01 -6.62758529e-01 -7.43957698e-01 -1.72487691e-01 -1.15091816e-01 4.97105062e-01 2.08512858e-01 1.19233564e-01 2.11671621e-01 -1.42633724e+00 3.81652951e-01 1.38376570e+00 1.16259813e+00 5.71334874e-03 3.75485048e-02 -2.06070155e-01 7.52061963e-01 -4.63629752e-01 -9.35112238e-02 5.66513300e-01 -6.65207684e-01 -2.79322267e-01 6.62977517e-01 2.98431575e-01 -6.13839984e-01 -3.21977973e-01 9.89238381e-01 3.77134293e-01 1.40422240e-01 4.30991530e-01 -1.51912916e+00 -1.26571640e-01 3.05547118e-01 -1.03360689e+00 3.86163384e-01 1.73311070e-01 2.15904236e-01 1.05795598e+00 -7.11595297e-01 1.96247041e-01 9.10289049e-01 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-3.10738951e-01 -7.61747360e-01 -6.15726888e-01 7.23369658e-01 2.46528223e-01 -6.10226631e-01 -4.92322981e-01 5.15797175e-02]	[7.828383445739746, 1.6434059143066406]
503717e7-e425-419d-93a7-a2457e13ee09	combating-covid-19-using-generative	2205.07236	null	https://arxiv.org/abs/2205.07236v1	https://arxiv.org/pdf/2205.07236v1.pdf	Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review	This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes the different GANs methods and the lungs images datasets for COVID-19. It attempts to answer the questions related to applications of GANs, popular GAN architectures, frequently used image modalities, and the availability of source code. This review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lungs images data. Most of the studies (n=42) used GANs for data augmentation to enhance the performance of AI techniques for COVID-19 diagnosis. Other popular applications of GANs were segmentation of lungs and super-resolution of the lungs images. The cycleGAN and the conditional GAN were the most commonly used architectures used in nine studies each. 29 studies used chest X-Ray images while 21 studies used CT images for the training of GANs. For majority of the studies (n=47), the experiments were done and results were reported using publicly available data. A secondary evaluation of the results by radiologists/clinicians was reported by only two studies. Conclusion: Studies have shown that GANs have great potential to address the data scarcity challenge for lungs images of COVID-19. Data synthesized with GANs have been helpful to improve the training of the Convolutional Neural Network (CNN) models trained for the diagnosis of COVID-19. Besides, GANs have also contributed to enhancing the CNNs performance through the super-resolution of the images and segmentation. This review also identified key limitations of the potential transformation of GANs based methods in clinical applications.	['Zubair Shah', 'Hazrat Ali']	2022-05-15	null	null	null	null	['covid-19-detection']	['medical']	[ 2.36403152e-01 3.99857253e-01 -2.28224456e-01 -7.80258775e-02 -7.77773440e-01 -2.90706784e-01 2.27384523e-01 -5.26999652e-01 -2.50985742e-01 8.20226192e-01 4.38330323e-01 -2.28813812e-01 1.29756974e-02 -8.05541813e-01 -3.79827619e-01 -1.06166399e+00 3.80737811e-01 7.42099881e-01 3.67637128e-02 1.09708987e-01 -5.11557400e-01 5.12745023e-01 -9.16401505e-01 6.17806435e-01 3.97219479e-01 6.51058316e-01 3.38483572e-01 1.14111042e+00 -6.87758774e-02 1.08346725e+00 -8.18087816e-01 -1.85162410e-01 2.75713235e-01 -1.12106311e+00 -6.97831929e-01 -2.35359415e-01 5.14730632e-01 -5.80009997e-01 -3.95023912e-01 2.06285834e-01 1.09859788e+00 -1.49764672e-01 8.03947091e-01 -1.01340449e+00 -6.68264985e-01 5.38289309e-01 -2.81058520e-01 8.88163567e-01 -1.92922890e-01 4.57824349e-01 2.21081376e-01 -3.83720070e-01 4.11964297e-01 9.72823262e-01 1.05968976e+00 1.26707423e+00 -6.72557712e-01 -9.94137704e-01 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-2.92413086e-01 1.00724481e-01 3.88924807e-01 -4.20623928e-01 -4.11451757e-01 8.48944038e-02]	[14.705095291137695, -1.9458351135253906]
8c1ff591-7db5-48d2-b113-f70e6b143beb	from-phonemes-to-images-levels-of	1610.03342	null	http://arxiv.org/abs/1610.03342v1	http://arxiv.org/pdf/1610.03342v1.pdf	From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning	We present a model of visually-grounded language learning based on stacked gated recurrent neural networks which learns to predict visual features given an image description in the form of a sequence of phonemes. The learning task resembles that faced by human language learners who need to discover both structure and meaning from noisy and ambiguous data across modalities. We show that our model indeed learns to predict features of the visual context given phonetically transcribed image descriptions, and show that it represents linguistic information in a hierarchy of levels: lower layers in the stack are comparatively more sensitive to form, whereas higher layers are more sensitive to meaning.	['Grzegorz Chrupała', 'Lieke Gelderloos']	2016-10-11	from-phonemes-to-images-levels-of-1	https://aclanthology.org/C16-1124	https://aclanthology.org/C16-1124.pdf	coling-2016-12	['grounded-language-learning']	['natural-language-processing']	[ 5.59346199e-01 1.47670224e-01 -5.52075617e-02 -7.86448658e-01 -9.02425766e-01 -7.61964262e-01 7.45572388e-01 1.31614313e-01 -4.25700098e-01 5.06129682e-01 7.59419858e-01 -3.30732405e-01 2.85445333e-01 -7.14063346e-01 -9.43893254e-01 -7.06863105e-01 -8.53860304e-02 2.28975996e-01 -7.01770931e-02 -2.61697680e-01 5.38585782e-01 4.08773869e-01 -1.66282809e+00 1.18990541e+00 -5.22605814e-02 8.42333257e-01 5.96508384e-01 8.99745286e-01 -5.40933073e-01 1.45600021e+00 -2.83376336e-01 1.05012260e-01 -1.57219574e-01 -8.24912488e-01 -1.08107126e+00 2.41043732e-01 7.74368465e-01 -7.20719472e-02 -1.47978052e-01 8.86615515e-01 5.74956872e-02 5.88352159e-02 7.29644120e-01 -7.42062390e-01 -1.16441548e+00 4.59486693e-01 -9.57459584e-02 3.36810917e-01 4.95694131e-01 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4f6780ff-515f-4997-94f8-cfb4b2e18a32	modeling-content-emotion-duality-via	2209.12495	null	https://arxiv.org/abs/2209.12495v1	https://arxiv.org/pdf/2209.12495v1.pdf	Modeling Content-Emotion Duality via Disentanglement for Empathetic Conversation	The task of empathetic response generation aims to understand what feelings a speaker expresses on his/her experiences and then reply to the speaker appropriately. To solve the task, it is essential to model the content-emotion duality of a dialogue, which is composed of the content view (i.e., what personal experiences are described) and the emotion view (i.e., the feelings of the speaker on these experiences). To this end, we design a framework to model the Content-Emotion Duality (CEDual) via disentanglement for empathetic response generation. With disentanglement, we encode the dialogue history from both the content and emotion views, and then generate the empathetic response based on the disentangled representations, thereby both the content and emotion information of the dialogue history can be embedded in the generated response. The experiments on the benchmark dataset EMPATHETICDIALOGUES show that the CEDual model achieves state-of-the-art performance on both automatic and human metrics, and it also generates more empathetic responses than previous methods.	['Wenjie Li', 'Hinrich Schütze', 'Jiashuo Wang', 'Peiqin Lin']	2022-09-26	null	null	null	null	['empathetic-response-generation']	['natural-language-processing']	[-2.47762039e-01 3.68477821e-01 -8.49337783e-03 -6.45962059e-01 -3.72172207e-01 -3.96700054e-01 9.22499418e-01 -2.62062415e-03 2.27594879e-02 7.50554144e-01 1.29656208e+00 4.47063565e-01 2.83199757e-01 -8.59960556e-01 2.74491072e-01 -7.48174906e-01 5.46520591e-01 4.47924674e-01 -7.49407291e-01 -8.21740270e-01 2.46029988e-01 -2.57381685e-02 -1.18724954e+00 7.65392184e-01 5.68275034e-01 8.91170800e-01 -4.01144296e-01 4.99551266e-01 -1.89130396e-01 1.60290933e+00 -7.72775173e-01 -7.90784895e-01 -3.20536256e-01 -1.09478724e+00 -1.21071196e+00 -1.92665756e-01 -4.96447384e-01 -2.43616670e-01 -2.74330825e-01 8.57076347e-01 6.68789625e-01 2.98771888e-01 9.03248549e-01 -1.36946332e+00 -8.66178989e-01 8.48138571e-01 -2.22745687e-01 -2.31840238e-01 9.33783650e-01 3.30897346e-02 1.17584133e+00 -9.28029239e-01 6.43895566e-01 1.55560553e+00 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672c7963-511d-4d9c-a276-453c6898ccf7	transliteration-of-foreign-words-in-burmese	2110.03163	null	https://arxiv.org/abs/2110.03163v2	https://arxiv.org/pdf/2110.03163v2.pdf	Transliteration of Foreign Words in Burmese	This manuscript provides general descriptions on transliteration of foreign words in the Burmese language. Phenomena caused by phonetic and orthographic issues are discussed. Based on this work, we expect to gradually establish prescriptive guidelines to normalize the transliteration on modern words in Burmese.	['Chenchen Ding']	2021-10-07	null	null	null	null	['transliteration']	['natural-language-processing']	[-3.29812676e-01 -3.55488211e-01 -1.80632904e-01 -3.44977647e-01 -3.16398501e-01 -5.81656039e-01 5.42673826e-01 1.32810488e-01 -7.52271116e-01 9.47329938e-01 6.68658197e-01 -9.58815515e-01 8.29808563e-02 -3.75268549e-01 -4.64174747e-01 -3.08767945e-01 4.49659377e-01 3.31788391e-01 -1.04123829e-02 -8.23015928e-01 5.96952200e-01 6.17052853e-01 -1.05915701e+00 9.40579697e-02 1.15661585e+00 -1.95356146e-01 5.62040329e-01 3.71159226e-01 -3.65927279e-01 4.36979145e-01 -9.85453427e-01 -5.58410048e-01 1.57033727e-01 -6.49575174e-01 -9.87152100e-01 -1.14620402e-01 4.58211392e-01 -3.11835647e-01 -2.53586024e-01 1.03513157e+00 4.51243281e-01 1.00326918e-01 9.93470788e-01 -2.57523149e-01 -1.45615053e+00 1.08773255e+00 2.16721311e-01 8.52261841e-01 4.83555228e-01 -1.06240608e-01 5.83190978e-01 -1.17811441e+00 6.39722943e-01 1.44534171e+00 4.20086086e-01 5.79508066e-01 -8.31949890e-01 -6.30263984e-01 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3f5c07c4-d59a-40d4-902c-34a1d3337b55	temporal-word-analogies-identifying-lexical	null	null	https://aclanthology.org/P17-2071	https://aclanthology.org/P17-2071.pdf	Temporal Word Analogies: Identifying Lexical Replacement with Diachronic Word Embeddings	This paper introduces the concept of temporal word analogies: pairs of words which occupy the same semantic space at different points in time. One well-known property of word embeddings is that they are able to effectively model traditional word analogies ({``}word $w_1$ is to word $w_2$ as word $w_3$ is to word $w_4${''}) through vector addition. Here, I show that temporal word analogies ({``}word $w_1$ at time $t_\alpha$ is like word $w_2$ at time $t_\beta${''}) can effectively be modeled with diachronic word embeddings, provided that the independent embedding spaces from each time period are appropriately transformed into a common vector space. When applied to a diachronic corpus of news articles, this method is able to identify temporal word analogies such as {``}Ronald Reagan in 1987 is like Bill Clinton in 1997{''}, or {``}Walkman in 1987 is like iPod in 2007{''}.	['Terrence Szymanski']	2017-07-01	null	null	null	acl-2017-7	['diachronic-word-embeddings']	['natural-language-processing']	[-2.45073423e-01 -2.79839545e-01 -3.36916327e-01 -2.33355761e-01 -4.74806249e-01 -6.88803971e-01 1.05549407e+00 7.93851614e-01 -1.05247247e+00 5.03169656e-01 4.58731085e-01 -7.67709851e-01 -6.41379416e-01 -9.34475243e-01 -4.39716399e-01 -2.21593633e-01 -4.80185807e-01 3.44121188e-01 6.17064759e-02 -7.49318898e-01 3.97855341e-01 2.31581837e-01 -1.20340264e+00 -1.52443185e-01 2.89693803e-01 7.69636810e-01 9.46611166e-02 4.91093606e-01 -6.35970235e-01 3.15158933e-01 -5.82749724e-01 -6.34650588e-01 1.75153196e-01 -4.03655887e-01 -8.69123161e-01 -5.93135893e-01 2.46261209e-01 1.23035088e-02 -6.20885313e-01 1.21973097e+00 6.82853982e-02 7.04159260e-01 7.33545959e-01 -1.21603262e+00 -1.27840400e+00 8.58859539e-01 -3.28056127e-01 5.65223813e-01 3.44946384e-01 6.18185662e-02 1.56309199e+00 -8.22430432e-01 5.63049197e-01 1.20755577e+00 8.15304399e-01 2.97032267e-01 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-1.11704552e+00 1.02138209e+00 -1.20969802e-01 -7.15973020e-01 -3.72390002e-01 -1.20127067e-01]	[10.232377052307129, 8.880875587463379]
26d85902-5002-4d21-8876-2dca628ff571	attributed-network-embedding-model-for	2209.09448	null	https://arxiv.org/abs/2209.09448v2	https://arxiv.org/pdf/2209.09448v2.pdf	Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes	The spread of COVID-19 revealed that transmission risk patterns are not homogenous across different cities and communities, and various heterogeneous features can influence the spread trajectories. Hence, for predictive pandemic monitoring, it is essential to explore latent heterogeneous features in cities and communities that distinguish their specific pandemic spread trajectories. To this end, this study creates a network embedding model capturing cross-county visitation networks, as well as heterogeneous features to uncover clusters of counties in the United States based on their pandemic spread transmission trajectories. We collected and computed location intelligence features from 2,787 counties from March 3 to June 29, 2020 (initial wave). Second, we constructed a human visitation network, which incorporated county features as node attributes, and visits between counties as network edges. Our attributed network embeddings approach integrates both typological characteristics of the cross-county visitation network, as well as heterogeneous features. We conducted clustering analysis on the attributed network embeddings to reveal four archetypes of spread risk trajectories corresponding to four clusters of counties. Subsequently, we identified four features as important features underlying the distinctive transmission risk patterns among the archetypes. The attributed network embedding approach and the findings identify and explain the non-homogenous pandemic risk trajectories across counties for predictive pandemic monitoring. The study also contributes to data-driven and deep learning-based approaches for pandemic analytics to complement the standard epidemiological models for policy analysis in pandemics.	['Ali Mostafavi', 'Chao Fan', 'Qingchun Li', 'Bo Li', 'Junwei Ma']	2022-09-20	null	null	null	null	['network-embedding']	['methodology']	[-4.23519254e-01 4.70357761e-03 -3.01559925e-01 2.83329841e-03 -8.68565217e-02 -5.62706590e-01 1.06228817e+00 7.68932700e-01 -1.85874701e-01 3.14837515e-01 1.01656353e+00 -5.54547310e-01 -5.70635259e-01 -1.42187059e+00 -3.40401791e-02 -6.15369678e-01 -9.70063865e-01 5.39229035e-01 -1.86716184e-01 -5.55578947e-01 -2.25789055e-01 4.24429417e-01 -9.21144128e-01 4.53413352e-02 1.02536345e+00 3.34929138e-01 -1.21008970e-01 5.75752199e-01 -7.09041283e-02 2.93298185e-01 -3.58099014e-01 -1.93298608e-01 2.30295677e-02 -4.89159971e-02 -4.81028825e-01 -4.32032198e-01 -5.88824570e-01 -3.68016481e-01 -7.01768219e-01 6.49509668e-01 3.74273181e-01 -4.79122587e-02 1.22614729e+00 -1.60240197e+00 -9.62271690e-01 6.31058931e-01 -5.45262992e-01 5.67711234e-01 2.87395716e-01 1.81107551e-01 1.00942314e+00 -6.22192264e-01 5.62345147e-01 9.08506751e-01 1.11796689e+00 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-6.80011749e-01 5.23222744e-01 8.05863678e-01 -4.79532033e-01 -6.16073310e-01 -5.23421407e-01]	[6.588459491729736, 2.3084113597869873]
99883a6d-4beb-4a2b-b947-db848e65ceb0	a-robust-completed-local-binary-pattern-rclbp	2112.04021	null	https://arxiv.org/abs/2112.04021v1	https://arxiv.org/pdf/2112.04021v1.pdf	A Robust Completed Local Binary Pattern (RCLBP) for Surface Defect Detection	In this paper, we present a Robust Completed Local Binary Pattern (RCLBP) framework for a surface defect detection task. Our approach uses a combination of Non-Local (NL) means filter with wavelet thresholding and Completed Local Binary Pattern (CLBP) to extract robust features which are fed into classifiers for surface defects detection. This paper combines three components: A denoising technique based on Non-Local (NL) means filter with wavelet thresholding is established to denoise the noisy image while preserving the textures and edges. Second, discriminative features are extracted using the CLBP technique. Finally, the discriminative features are fed into the classifiers to build the detection model and evaluate the performance of the proposed framework. The performance of the defect detection models are evaluated using a real-world steel surface defect database from Northeastern University (NEU). Experimental results demonstrate that the proposed approach RCLBP is noise robust and can be applied for surface defect detection under varying conditions of intra-class and inter-class changes and with illumination changes.	['Daniel Opoku', 'Abdollah Homaifar', 'Shamila Nateghi', 'Mahmoud Nabil Mahmoud', 'Abenezer Girma', 'Nana Kankam Gyimah']	2021-12-07	null	null	null	null	['defect-detection']	['computer-vision']	[ 5.25019228e-01 -6.30297124e-01 4.88061160e-01 -2.70810425e-01 -8.71046484e-01 9.11755860e-02 2.35506132e-01 3.80873770e-01 -2.39544630e-01 2.90818840e-01 -9.65611115e-02 1.28926471e-01 -3.59316409e-01 -1.01263142e+00 -2.32778922e-01 -1.12156308e+00 1.90127507e-01 -1.79434329e-01 9.21820462e-01 -2.59653360e-01 7.60432959e-01 9.14057016e-01 -2.08302236e+00 6.55105412e-01 6.29550874e-01 1.36529994e+00 5.08801639e-01 7.81148255e-01 1.84502870e-01 5.79805136e-01 -6.80409729e-01 5.88827193e-01 2.80147403e-01 -1.52416244e-01 -5.16413808e-01 5.55089474e-01 9.76045579e-02 -8.94943401e-02 2.16113165e-01 1.09496474e+00 7.46008098e-01 2.09261760e-01 7.84854054e-01 -5.71203113e-01 -1.20931961e-01 -2.39270672e-01 -6.50900126e-01 6.35531247e-01 3.55613410e-01 -1.32384629e-03 4.42445040e-01 -1.15734959e+00 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3.71742368e-01 7.76707351e-01 -9.15918887e-01 -5.16296029e-01 -1.30128360e+00 -1.17273760e+00 7.13061154e-01 3.31320554e-01 -9.59164798e-01 -7.62714863e-01 2.76320994e-01]	[7.497071266174316, 1.6320879459381104]
4241b691-cf84-46aa-b8eb-d7ca8b5a9c8c	learning-how-to-robustly-estimate-camera-pose	2304.08023	null	https://arxiv.org/abs/2304.08023v1	https://arxiv.org/pdf/2304.08023v1.pdf	Learning How To Robustly Estimate Camera Pose in Endoscopic Videos	Purpose: Surgical scene understanding plays a critical role in the technology stack of tomorrow's intervention-assisting systems in endoscopic surgeries. For this, tracking the endoscope pose is a key component, but remains challenging due to illumination conditions, deforming tissues and the breathing motion of organs. Method: We propose a solution for stereo endoscopes that estimates depth and optical flow to minimize two geometric losses for camera pose estimation. Most importantly, we introduce two learned adaptive per-pixel weight mappings that balance contributions according to the input image content. To do so, we train a Deep Declarative Network to take advantage of the expressiveness of deep-learning and the robustness of a novel geometric-based optimization approach. We validate our approach on the publicly available SCARED dataset and introduce a new in-vivo dataset, StereoMIS, which includes a wider spectrum of typically observed surgical settings. Results: Our method outperforms state-of-the-art methods on average and more importantly, in difficult scenarios where tissue deformations and breathing motion are visible. We observed that our proposed weight mappings attenuate the contribution of pixels on ambiguous regions of the images, such as deforming tissues. Conclusion: We demonstrate the effectiveness of our solution to robustly estimate the camera pose in challenging endoscopic surgical scenes. Our contributions can be used to improve related tasks like simultaneous localization and mapping (SLAM) or 3D reconstruction, therefore advancing surgical scene understanding in minimally-invasive surgery.	['Raphael Sznitman', 'Maximilian Allan', 'Thomas Kurmann', 'Daniel Candinas', 'Mathias Gallardo', 'Christopher Hahne', 'Michel Hayoz']	2023-04-17	null	null	null	null	['simultaneous-localization-and-mapping', '3d-reconstruction']	['computer-vision', 'computer-vision']	[ 1.58318996e-01 3.41148257e-01 -4.04159911e-02 2.57272199e-02 -8.10439348e-01 -1.06755900e+00 6.54836446e-02 3.77486722e-04 -6.07041180e-01 2.58722067e-01 3.18036109e-01 -3.48989397e-01 -1.97667792e-01 -1.91629410e-01 -9.17231023e-01 -7.71744251e-01 1.61811598e-02 8.45963880e-02 7.73201734e-02 -2.00663283e-01 1.15871996e-01 6.79253101e-01 -1.10792804e+00 5.75980023e-02 6.88519359e-01 9.57959235e-01 4.35725838e-01 6.66122854e-01 3.63561809e-01 3.21870625e-01 1.00287996e-01 -6.07562624e-02 6.90011859e-01 -1.19253270e-01 -6.05975568e-01 -6.37004226e-02 6.33174777e-01 -4.19458210e-01 -3.60525399e-01 1.12058282e+00 6.44618690e-01 6.14990033e-02 1.15183711e-01 -4.38943833e-01 1.79321080e-01 2.56528795e-01 -4.27271783e-01 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1.16415644e+00 6.17643178e-01 3.40458483e-01 8.49588096e-01 4.75861013e-01 -1.16666341e+00 -4.63605881e-01 -3.57774317e-01 -4.00804579e-01 5.11170566e-01 7.84239590e-01 -5.12649179e-01 -6.36894464e-01 -3.98695543e-02]	[13.908585548400879, -3.1758763790130615]
d2a40c27-8249-4c83-a20a-99e454f54a9c	automatic-generation-of-realistic-training	null	null	https://elib.dlr.de/147156/	https://elib.dlr.de/147156/1/Final%20version%20V2opt.pdf	Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images	This paper proposes a novel approach to automat- ically generate labeled training data for predicting parallel-jaw grasps from stereo-matched depth images. We generate realistic depth images using Semi-Global Matching to compute disparity maps from synthetic data, which allows producing images that mimic the typical artifacts from real stereo matching in our data, thus reducing the gap from simulation to real execu- tion. Our pipeline automatically generates grasp annotations for single or multiple objects on the synthetically rendered scenes, avoiding any manual image pre-processing steps such as inpainting or denoising. The labeled data is then used to train a CNN-model that predicts parallel-jaw grasps, even in scenarios with large amount of unknown depth values. We further show that scene properties such as the presence of obstacles (a bin, for instance) can be added to our pipeline, and the training process results in grasp prediction success rates of up to 90%.	['Màximo A. Roa', 'Jochen Steil', 'Michael Suppa', 'Elena Gambaro', 'Carlos X. Garcia', 'Justus Drögemüller']	2021-12-07	null	null	null	ieee-int-conf-advanced-robotics-2021-12	['grasp-generation', 'stereo-matching-1', 'robotic-grasping', 'grasp-rectangle-generation']	['computer-vision', 'computer-vision', 'robots', 'robots']	[ 4.77595210e-01 3.46210569e-01 4.28948790e-01 -4.36644614e-01 -6.74831271e-01 -5.69333315e-01 1.94553092e-01 1.96064591e-01 -1.18770845e-01 4.93322760e-01 1.95132568e-02 1.19376339e-01 2.07440138e-01 -9.72874582e-01 -1.27615941e+00 -4.51582640e-01 -1.98498309e-01 9.31391597e-01 6.66485488e-01 -1.08468369e-01 5.19654810e-01 6.85668766e-01 -2.08791876e+00 5.06302297e-01 8.46427798e-01 9.59122300e-01 8.69771957e-01 8.25203240e-01 -1.43648371e-01 4.55747873e-01 -6.48829103e-01 -2.02712938e-01 7.43193924e-01 7.95993879e-02 -5.36684334e-01 3.62040728e-01 4.34437633e-01 -9.44639564e-01 -3.19392890e-01 7.53095508e-01 3.29137653e-01 -3.17279905e-01 5.71046352e-01 -1.09286726e+00 3.41991684e-03 3.96641791e-01 -5.93371451e-01 -5.98704875e-01 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b180cdf6-f9de-4518-be72-39c57a8c11a2	methodology-for-jointly-assessing-myocardial	2306.15281	null	https://arxiv.org/abs/2306.15281v1	https://arxiv.org/pdf/2306.15281v1.pdf	Methodology for Jointly Assessing Myocardial Infarct Extent and Regional Contraction in 3-D CMRI	Automated extraction of quantitative parameters from Cardiac Magnetic Resonance Images (CMRI) is crucial for the management of patients with myocardial infarct. This work proposes a post-processing procedure to jointly analyze Cine and Delayed-Enhanced (DE) acquisitions in order to provide an automatic quantification of myocardial contraction and enhancement parameters and a study of their relationship. For that purpose, the following processes are performed: 1) DE/Cine temporal synchronization and 3D scan alignment, 2) 3D DE/Cine rigid registration in a region about the heart, 3) segmentation of the myocardium on Cine MRI and superimposition of the epicardial and endocardial contours on the DE images, 4) quantification of the Myocardial Infarct Extent (MIE), 5) study of the regional contractile function using a new index, the Amplitude to Time Ratio (ATR). The whole procedure was applied to 10 patients with clinically proven myocardial infarction. The comparison between the MIE and the visually assessed regional function scores demonstrated that the MIE is highly related to the severity of the wall motion abnormality. In addition, it was shown that the newly developed regional myocardial contraction parameter (ATR) decreases significantly in delayed enhanced regions. This largely automated approach enables a combined study of regional MIE and left ventricular function.	['F. Frouin', 'E. Mousseaux', 'E. Roullot', 'M. Lefort', 'R. El Berbari', 'C. Constantinides', 'C. Pellot-Barakat', 'Y. Chenoune']	2023-06-27	null	null	null	null	['management']	['miscellaneous']	[ 2.86093295e-01 -1.96400687e-01 1.58256233e-01 -1.21881917e-01 -4.46768582e-01 -7.15113640e-01 1.96602643e-01 3.69096726e-01 -7.29169548e-01 4.65778917e-01 7.45198578e-02 -2.99009115e-01 -2.73576379e-01 -5.42196989e-01 1.18603587e-01 -7.68630922e-01 -5.86246431e-01 7.01289058e-01 4.31158155e-01 2.30871409e-01 1.66831687e-01 8.71161282e-01 -5.63821435e-01 -8.96503702e-02 5.21729946e-01 5.25951147e-01 4.02926713e-01 1.13014185e+00 2.26491705e-01 5.66816509e-01 -3.58695865e-01 2.59819984e-01 1.04769498e-01 -8.43445420e-01 -8.41991901e-01 2.21270591e-01 -1.01596534e-01 -5.16644716e-01 1.69948772e-01 6.88658714e-01 6.99349344e-01 -5.61232194e-02 8.39183271e-01 -4.45677310e-01 2.59315342e-01 5.79051256e-01 -5.09177923e-01 7.85430074e-01 -2.51205951e-01 -3.44551206e-02 8.60427022e-02 -8.74526799e-01 8.81527841e-01 3.01104903e-01 6.18772984e-01 2.59248394e-04 -1.37203169e+00 -1.64078727e-01 -5.83362937e-01 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1.34427071e-01 -1.68629050e-01 -2.71883339e-01 6.97855413e-01]	[14.110881805419922, -2.462512254714966]
7b984b38-23f8-4820-b394-80453b5d5ac8	pash-at-trec-2021-deep-learning-track	2205.11245	null	https://arxiv.org/abs/2205.11245v3	https://arxiv.org/pdf/2205.11245v3.pdf	PASH at TREC 2021 Deep Learning Track: Generative Enhanced Model for Multi-stage Ranking	This paper describes the PASH participation in TREC 2021 Deep Learning Track. In the recall stage, we adopt a scheme combining sparse and dense retrieval method. In the multi-stage ranking phase, point-wise and pair-wise ranking strategies are used one after another based on model continual pre-trained on general knowledge and document-level data. Compared to TREC 2020 Deep Learning Track, we have additionally introduced the generative model T5 to further enhance the performance.	['Wenfeng Xie', 'Xin Tang', 'Jun Wang', 'Guotong Xie', 'Peng Gao', 'Rui Fang', 'Xianbin Ye', 'Tuozhen Liu', 'Yongquan Lai', 'Hao Chen', 'Yixuan Qiao']	2022-05-18	null	null	null	null	['general-knowledge']	['miscellaneous']	[-3.89891714e-01 -3.67842257e-01 3.61393914e-02 -4.84089047e-01 -1.70312381e+00 -6.57447815e-01 1.24902391e+00 3.54313612e-01 -9.33806300e-01 8.11901033e-01 7.27286994e-01 2.43825931e-02 -7.12880790e-01 -6.69354200e-01 -5.66847563e-01 -3.58485937e-01 -1.31829619e-01 1.14548695e+00 3.20522815e-01 -5.75228214e-01 5.18041790e-01 1.69078901e-01 -1.32272935e+00 1.05012774e+00 4.70210403e-01 8.11154485e-01 -2.82438193e-03 6.95115387e-01 -1.63574904e-01 7.40780294e-01 -4.40215915e-01 -5.62368989e-01 3.21609527e-01 -1.02880346e-02 -8.72033596e-01 -6.98860466e-01 6.64952397e-01 -6.17293417e-01 -5.89022160e-01 7.78627992e-01 9.60905194e-01 4.93730903e-01 8.27153742e-01 -2.86535054e-01 -3.57385278e-01 4.60142255e-01 -4.99299198e-01 1.56064108e-01 5.85603297e-01 -4.89434600e-01 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6102ac67-f41f-4ff4-9611-f15104e7e147	deconvolutional-paragraph-representation	1708.04729	null	http://arxiv.org/abs/1708.04729v3	http://arxiv.org/pdf/1708.04729v3.pdf	Deconvolutional Paragraph Representation Learning	Learning latent representations from long text sequences is an important first step in many natural language processing applications. Recurrent Neural Networks (RNNs) have become a cornerstone for this challenging task. However, the quality of sentences during RNN-based decoding (reconstruction) decreases with the length of the text. We propose a sequence-to-sequence, purely convolutional and deconvolutional autoencoding framework that is free of the above issue, while also being computationally efficient. The proposed method is simple, easy to implement and can be leveraged as a building block for many applications. We show empirically that compared to RNNs, our framework is better at reconstructing and correcting long paragraphs. Quantitative evaluation on semi-supervised text classification and summarization tasks demonstrate the potential for better utilization of long unlabeled text data.	['Lawrence Carin', 'Yizhe Zhang', 'Zhe Gan', 'Ricardo Henao', 'Guoyin Wang', 'Dinghan Shen']	2017-08-16	deconvolutional-paragraph-representation-1	http://papers.nips.cc/paper/7005-deconvolutional-paragraph-representation-learning	http://papers.nips.cc/paper/7005-deconvolutional-paragraph-representation-learning.pdf	neurips-2017-12	['semi-supervised-text-classification-1']	['natural-language-processing']	[ 3.92557323e-01 5.27051091e-02 -2.36251980e-01 -3.19920689e-01 -9.31153476e-01 -5.66811383e-01 6.99999213e-01 4.64542508e-02 -4.42486584e-01 8.83275688e-01 9.41666603e-01 -4.54795927e-01 3.27964932e-01 -5.66075265e-01 -7.06298888e-01 -7.00987399e-01 3.88731539e-01 4.65780914e-01 -2.73878038e-01 -1.73344612e-01 3.96941960e-01 1.88404053e-01 -1.32389402e+00 4.15749162e-01 7.57866502e-01 5.36998570e-01 5.16835332e-01 8.85213733e-01 -4.46776539e-01 1.06473851e+00 -3.80805314e-01 -3.87404114e-01 -1.52733013e-01 -6.92557514e-01 -8.78596783e-01 2.08662778e-01 6.86196461e-02 -4.55969244e-01 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98568a5d-a361-44fa-81eb-512885c51708	transformer-based-deep-image-matching-for	2105.14432	null	https://arxiv.org/abs/2105.14432v2	https://arxiv.org/pdf/2105.14432v2.pdf	TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification	Transformers have recently gained increasing attention in computer vision. However, existing studies mostly use Transformers for feature representation learning, e.g. for image classification and dense predictions, and the generalizability of Transformers is unknown. In this work, we further investigate the possibility of applying Transformers for image matching and metric learning given pairs of images. We find that the Vision Transformer (ViT) and the vanilla Transformer with decoders are not adequate for image matching due to their lack of image-to-image attention. Thus, we further design two naive solutions, i.e. query-gallery concatenation in ViT, and query-gallery cross-attention in the vanilla Transformer. The latter improves the performance, but it is still limited. This implies that the attention mechanism in Transformers is primarily designed for global feature aggregation, which is not naturally suitable for image matching. Accordingly, we propose a new simplified decoder, which drops the full attention implementation with the softmax weighting, keeping only the query-key similarity computation. Additionally, global max pooling and a multilayer perceptron (MLP) head are applied to decode the matching result. This way, the simplified decoder is computationally more efficient, while at the same time more effective for image matching. The proposed method, called TransMatcher, achieves state-of-the-art performance in generalizable person re-identification, with up to 6.1% and 5.7% performance gains in Rank-1 and mAP, respectively, on several popular datasets. Code is available at https://github.com/ShengcaiLiao/QAConv.	['Ling Shao', 'Shengcai Liao']	2021-05-30	transmatcher-deep-image-matching-through	http://proceedings.neurips.cc/paper/2021/hash/0f49c89d1e7298bb9930789c8ed59d48-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/0f49c89d1e7298bb9930789c8ed59d48-Paper.pdf	neurips-2021-12	['generalizable-person-re-identification']	['computer-vision']	[ 1.57181293e-01 -2.87169456e-01 -2.30289325e-02 -4.10700262e-01 -7.53493309e-01 -3.36393476e-01 6.60919487e-01 1.62453264e-01 -6.09920621e-01 3.09602559e-01 4.04397361e-02 -2.01012671e-01 5.96195161e-02 -8.52444589e-01 -7.71924973e-01 -5.76019824e-01 4.83875155e-01 1.94798797e-01 1.85555339e-01 1.77900381e-02 9.70131829e-02 2.63439000e-01 -1.63382649e+00 2.67464876e-01 9.73792374e-01 1.35781419e+00 4.27166879e-01 3.66115481e-01 1.63313560e-02 6.29638374e-01 -3.43805879e-01 -1.07952631e+00 2.75959760e-01 -4.24462050e-01 -6.80113912e-01 -6.38990775e-02 7.30986655e-01 -4.27575499e-01 -7.71279037e-01 1.20177770e+00 7.89174080e-01 1.04394481e-01 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88786650-56f2-4801-a0a5-7bff7b321ae9	gradient-based-geometry-learning-for-fan-beam	2212.02177	null	https://arxiv.org/abs/2212.02177v1	https://arxiv.org/pdf/2212.02177v1.pdf	Gradient-Based Geometry Learning for Fan-Beam CT Reconstruction	Incorporating computed tomography (CT) reconstruction operators into differentiable pipelines has proven beneficial in many applications. Such approaches usually focus on the projection data and keep the acquisition geometry fixed. However, precise knowledge of the acquisition geometry is essential for high quality reconstruction results. In this paper, the differentiable formulation of fan-beam CT reconstruction is extended to the acquisition geometry. This allows to propagate gradient information from a loss function on the reconstructed image into the geometry parameters. As a proof-of-concept experiment, this idea is applied to rigid motion compensation. The cost function is parameterized by a trained neural network which regresses an image quality metric from the motion affected reconstruction alone. Using the proposed method, we are the first to optimize such an autofocus-inspired algorithm based on analytical gradients. The algorithm achieves a reduction in MSE by 35.5 % and an improvement in SSIM by 12.6 % over the motion affected reconstruction. Next to motion compensation, we see further use cases of our differentiable method for scanner calibration or hybrid techniques employing deep models.	['Andreas Maier', 'Michael Manhart', 'Felix Denzinger', 'Jonas Utz', 'Mingxuan Gu', 'Laura Pfaff', 'Linda-Sophie Schneider', 'Maximilian Rohleder', 'Manuela Meier', 'Lukas Folle', 'Noah Maul', 'Fabian Wagner', 'Mareike Thies']	2022-12-05	null	null	null	null	['motion-compensation']	['computer-vision']	[ 3.46372545e-01 2.90698409e-01 2.05146566e-01 -5.31846702e-01 -8.52814019e-01 -2.18954280e-01 3.46046448e-01 -1.51682764e-01 -6.72250032e-01 3.79954100e-01 9.39391181e-02 -2.56499708e-01 -2.56305993e-01 -6.03471279e-01 -7.84945667e-01 -8.93332899e-01 -4.58460748e-02 5.55034876e-01 2.13550746e-01 -4.01451476e-02 3.16423684e-01 9.05625105e-01 -8.18362236e-01 -1.00568019e-01 3.89412284e-01 8.16102386e-01 3.84815186e-01 5.72161198e-01 1.06169328e-01 8.10063660e-01 -1.70889571e-01 -1.31410152e-01 4.52014655e-01 -4.28993940e-01 -7.89524615e-01 1.33392498e-01 3.25732797e-01 -5.06559551e-01 -1.70020089e-01 9.65212941e-01 7.24627316e-01 1.26118794e-01 4.18505758e-01 -4.99358773e-01 6.84390962e-02 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96166548-c2b6-4486-8215-dd676a2c6192	incorporating-commonsense-knowledge-into	2010.10044	null	https://arxiv.org/abs/2010.10044v1	https://arxiv.org/pdf/2010.10044v1.pdf	Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks	Abstractive dialogue summarization is the task of capturing the highlights of a dialogue and rewriting them into a concise version. In this paper, we present a novel multi-speaker dialogue summarizer to demonstrate how large-scale commonsense knowledge can facilitate dialogue understanding and summary generation. In detail, we consider utterance and commonsense knowledge as two different types of data and design a Dialogue Heterogeneous Graph Network (D-HGN) for modeling both information. Meanwhile, we also add speakers as heterogeneous nodes to facilitate information flow. Experimental results on the SAMSum dataset show that our model can outperform various methods. We also conduct zero-shot setting experiments on the Argumentative Dialogue Summary Corpus, the results show that our model can better generalized to the new domain.	['Ting Liu', 'Bing Qin', 'Xiaocheng Feng', 'Xiachong Feng']	2020-10-20	null	https://aclanthology.org/2021.ccl-1.86	https://aclanthology.org/2021.ccl-1.86.pdf	ccl-2021-8	['dialogue-understanding']	['natural-language-processing']	[ 1.66557267e-01 9.32894289e-01 -2.28906676e-01 -4.01613832e-01 -7.35168159e-01 -5.28130472e-01 9.53785181e-01 2.39340544e-01 7.33250752e-02 1.16050076e+00 1.39000249e+00 -1.20209329e-01 3.67783576e-01 -7.10908234e-01 -2.06172243e-01 2.88534351e-02 2.52076834e-01 5.54755092e-01 4.17229444e-01 -1.07923508e+00 3.32190067e-01 -3.35205853e-01 -8.48185599e-01 5.65923035e-01 1.55586207e+00 3.14211130e-01 2.24278286e-01 9.66692090e-01 -6.51007056e-01 1.50769281e+00 -1.42761338e+00 -7.94597805e-01 -2.85351008e-01 -1.18878973e+00 -1.47045636e+00 1.54302120e-01 1.08159043e-01 -6.00278258e-01 -5.88726401e-01 9.36553836e-01 6.93018079e-01 6.34752631e-01 6.26129866e-01 -1.01774418e+00 -8.41071486e-01 1.42078578e+00 -7.51582608e-02 1.76914901e-01 9.61570621e-01 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b171fab3-68a4-40bd-8e5f-015ed1f5c7cc	unsupervised-flow-aligned-sequence-to	2205.10195	null	https://arxiv.org/abs/2205.10195v2	https://arxiv.org/pdf/2205.10195v2.pdf	Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration	How to properly model the inter-frame relation within the video sequence is an important but unsolved challenge for video restoration (VR). In this work, we propose an unsupervised flow-aligned sequence-to-sequence model (S2SVR) to address this problem. On the one hand, the sequence-to-sequence model, which has proven capable of sequence modeling in the field of natural language processing, is explored for the first time in VR. Optimized serialization modeling shows potential in capturing long-range dependencies among frames. On the other hand, we equip the sequence-to-sequence model with an unsupervised optical flow estimator to maximize its potential. The flow estimator is trained with our proposed unsupervised distillation loss, which can alleviate the data discrepancy and inaccurate degraded optical flow issues of previous flow-based methods. With reliable optical flow, we can establish accurate correspondence among multiple frames, narrowing the domain difference between 1D language and 2D misaligned frames and improving the potential of the sequence-to-sequence model. S2SVR shows superior performance in multiple VR tasks, including video deblurring, video super-resolution, and compressed video quality enhancement. Code and models are publicly available at https://github.com/linjing7/VR-Baseline	['Luc van Gool', 'Yulun Zhang', 'Xueyi Zou', 'Youliang Yan', 'Haoqian Wang', 'Yuanhao Cai', 'Xiaowan Hu', 'Jing Lin']	2022-05-20	null	null	null	null	['video-super-resolution', 'video-enhancement', 'video-restoration']	['computer-vision', 'computer-vision', 'computer-vision']	[ 3.84036809e-01 -3.54654282e-01 -2.12572813e-01 -1.63581640e-01 -5.55950463e-01 -4.41926658e-01 3.52009118e-01 -5.53985834e-01 -2.18452543e-01 7.89607048e-01 6.05938256e-01 -1.75706238e-01 4.30378579e-02 -2.93860614e-01 -6.32791340e-01 -6.14423096e-01 6.85603023e-02 -3.07443976e-01 2.24391103e-01 -1.47297367e-01 4.17944461e-01 2.90176958e-01 -1.28124261e+00 2.97463804e-01 1.09431541e+00 7.58127868e-01 7.10699022e-01 1.03050244e+00 3.01030725e-02 1.44296849e+00 -2.39592552e-01 -1.11992523e-01 3.30891401e-01 -6.43400669e-01 -8.01027358e-01 1.98753372e-01 8.46780241e-01 -8.65187407e-01 -9.27269995e-01 1.21318865e+00 4.56688970e-01 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341c6844-e8cb-4093-b651-0e3baa3e5c01	fingerspelling-recognition-in-the-wild-with	1908.10546	null	https://arxiv.org/abs/1908.10546v1	https://arxiv.org/pdf/1908.10546v1.pdf	Fingerspelling recognition in the wild with iterative visual attention	Sign language recognition is a challenging gesture sequence recognition problem, characterized by quick and highly coarticulated motion. In this paper we focus on recognition of fingerspelling sequences in American Sign Language (ASL) videos collected in the wild, mainly from YouTube and Deaf social media. Most previous work on sign language recognition has focused on controlled settings where the data is recorded in a studio environment and the number of signers is limited. Our work aims to address the challenges of real-life data, reducing the need for detection or segmentation modules commonly used in this domain. We propose an end-to-end model based on an iterative attention mechanism, without explicit hand detection or segmentation. Our approach dynamically focuses on increasingly high-resolution regions of interest. It outperforms prior work by a large margin. We also introduce a newly collected data set of crowdsourced annotations of fingerspelling in the wild, and show that performance can be further improved with this additional data set.	['Karen Livescu', 'Greg Shakhnarovich', 'Diane Brentari', 'Jonathan Keane', 'Bowen Shi', 'Aurora Martinez Del Rio']	2019-08-28	fingerspelling-recognition-in-the-wild-with-1	http://openaccess.thecvf.com/content_ICCV_2019/html/Shi_Fingerspelling_Recognition_in_the_Wild_With_Iterative_Visual_Attention_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Shi_Fingerspelling_Recognition_in_the_Wild_With_Iterative_Visual_Attention_ICCV_2019_paper.pdf	iccv-2019-10	['hand-detection']	['computer-vision']	[ 3.13301802e-01 -3.63144636e-01 -2.71832436e-01 -2.50345349e-01 -9.24183369e-01 -8.42905343e-01 6.01502776e-01 -7.82824874e-01 -1.04950166e+00 3.40559691e-01 6.84399724e-01 3.95663194e-02 2.16292202e-01 6.03055209e-02 -4.68226582e-01 -5.13963699e-01 6.59761503e-02 5.01667917e-01 9.25947607e-01 -1.48775056e-01 2.21753940e-01 6.05389178e-01 -1.73927987e+00 2.65554130e-01 4.22458172e-01 3.87307674e-01 3.67723078e-01 1.25362051e+00 1.25479266e-01 9.41734672e-01 -4.29929227e-01 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94c246a1-063c-45e5-b68c-725ab7f95885	ensemble-of-multi-view-learning-classifiers	1811.10068	null	http://arxiv.org/abs/1811.10068v1	http://arxiv.org/pdf/1811.10068v1.pdf	Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection	The adoption of large-scale iris recognition systems around the world has brought to light the importance of detecting presentation attack images (textured contact lenses and printouts). This work presents a new approach in iris Presentation Attack Detection (PAD), by exploring combinations of Convolutional Neural Networks (CNNs) and transformed input spaces through binarized statistical image features (BSIF). Our method combines lightweight CNNs to classify multiple BSIF views of the input image. Following explorations on complementary input spaces leading to more discriminative features to detect presentation attacks, we also propose an algorithm to select the best (and most discriminative) predictors for the task at hand.An ensemble of predictors makes use of their expected individual performances to aggregate their results into a final prediction. Results show that this technique improves on the current state of the art in iris PAD, outperforming the winner of LivDet-Iris2017 competition both for intra- and cross-dataset scenarios, and illustrating the very difficult nature of the cross-dataset scenario.	['Adam Czajka', 'Kevin Bowyer', 'Andrey Kuehlkamp', 'Anderson Rocha', 'Allan Pinto']	2018-11-25	null	null	null	null	['cross-domain-iris-presentation-attack']	['computer-vision']	[ 5.48278868e-01 -1.32157892e-01 -2.16250539e-01 -2.34580502e-01 -6.61503494e-01 -5.85065663e-01 9.06488299e-01 2.03046069e-01 -3.28187793e-01 1.62071973e-01 5.35904109e-01 -4.63214040e-01 -5.57137072e-01 -4.06250000e-01 -4.65316385e-01 -4.46221888e-01 -2.47615278e-01 9.78335962e-02 -5.03701493e-02 9.65492427e-03 4.41228241e-01 9.50393081e-01 -2.13574338e+00 8.84676993e-01 7.14452803e-01 1.21498621e+00 -6.47197723e-01 9.96339440e-01 4.16553408e-01 5.60961246e-01 -7.14775681e-01 -6.26412392e-01 6.03370130e-01 -4.01048481e-01 -7.11633146e-01 1.73880085e-01 1.37755120e+00 -2.29490802e-01 -6.45182952e-02 9.38681066e-01 7.70877898e-01 -2.69267261e-01 3.59975427e-01 -8.28920186e-01 -3.87768477e-01 1.73166156e-01 -6.38747692e-01 5.39109528e-01 5.93510211e-01 4.14115161e-01 7.07259238e-01 -5.13223290e-01 6.86721027e-01 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47f35413-ec9a-4748-a944-65e907eac64c	reinforced-swin-convs-transformer-for	2205.00434	null	https://arxiv.org/abs/2205.00434v1	https://arxiv.org/pdf/2205.00434v1.pdf	Reinforced Swin-Convs Transformer for Underwater Image Enhancement	Underwater Image Enhancement (UIE) technology aims to tackle the challenge of restoring the degraded underwater images due to light absorption and scattering. To address problems, a novel U-Net based Reinforced Swin-Convs Transformer for the Underwater Image Enhancement method (URSCT-UIE) is proposed. Specifically, with the deficiency of U-Net based on pure convolutions, we embedded the Swin Transformer into U-Net for improving the ability to capture the global dependency. Then, given the inadequacy of the Swin Transformer capturing the local attention, the reintroduction of convolutions may capture more local attention. Thus, we provide an ingenious manner for the fusion of convolutions and the core attention mechanism to build a Reinforced Swin-Convs Transformer Block (RSCTB) for capturing more local attention, which is reinforced in the channel and the spatial attention of the Swin Transformer. Finally, the experimental results on available datasets demonstrate that the proposed URSCT-UIE achieves state-of-the-art performance compared with other methods in terms of both subjective and objective evaluations. The code will be released on GitHub after acceptance.	['Ting Luo', 'Mei Yu', 'Gangyi Jiang', 'Haiyong Xu', 'Tingdi Ren']	2022-05-01	null	null	null	null	['uie']	['computer-vision']	[ 2.00035021e-01 2.01447904e-02 9.95707572e-01 -1.71866491e-01 -3.21845412e-01 6.85473382e-02 9.80660766e-02 -1.63703978e-01 -7.48298764e-01 5.26442826e-01 4.12752062e-01 -1.22489780e-01 -1.91941500e-01 -9.04245436e-01 -8.03230703e-01 -1.03939557e+00 -2.40041941e-01 -8.11890543e-01 3.70810360e-01 -6.44779980e-01 1.98766336e-01 7.84132034e-02 -1.39502609e+00 7.32532665e-02 1.26089728e+00 1.12818038e+00 7.02872634e-01 4.09298718e-01 4.79323491e-02 7.61547863e-01 -2.78863728e-01 -2.84327924e-01 2.59415030e-01 -3.98608327e-01 -3.44231308e-01 -2.30429858e-01 2.69709438e-01 -7.97121406e-01 -5.77356815e-01 1.31027591e+00 9.11416352e-01 3.55947256e-01 2.51129061e-01 -7.91808605e-01 -7.57610619e-01 4.41229284e-01 -6.14357829e-01 5.15895367e-01 -1.27580136e-01 -3.59578878e-02 6.99025869e-01 -9.67765749e-01 2.75943950e-02 1.16984642e+00 8.43212008e-01 3.66040170e-01 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25538fd9-9206-4d7a-bcd2-9938c3cb5f82	towards-confidence-guided-shape-completion	2209.043	null	https://arxiv.org/abs/2209.04300v1	https://arxiv.org/pdf/2209.04300v1.pdf	Towards Confidence-guided Shape Completion for Robotic Applications	Many robotic tasks involving some form of 3D visual perception greatly benefit from a complete knowledge of the working environment. However, robots often have to tackle unstructured environments and their onboard visual sensors can only provide incomplete information due to limited workspaces, clutter or object self-occlusion. In recent years, deep learning architectures for shape completion have begun taking traction as effective means of inferring a complete 3D object representation from partial visual data. Nevertheless, most of the existing state-of-the-art approaches provide a fixed output resolution in the form of voxel grids, strictly related to the size of the neural network output stage. While this is enough for some tasks, e.g. obstacle avoidance in navigation, grasping and manipulation require finer resolutions and simply scaling up the neural network outputs is computationally expensive. In this paper, we address this limitation by proposing an object shape completion method based on an implicit 3D representation providing a confidence value for each reconstructed point. As a second contribution, we propose a gradient-based method for efficiently sampling such implicit function at an arbitrary resolution, tunable at inference time. We experimentally validate our approach by comparing reconstructed shapes with ground truths, and by deploying our shape completion algorithm in a robotic grasping pipeline. In both cases, we compare results with a state-of-the-art shape completion approach.	['Lorenzo Natale', 'Michele Colledanchise', 'Fabrizio Bottarel', 'Stefano Berti', 'Andrea Rosasco']	2022-09-09	null	null	null	null	['robotic-grasping']	['robots']	[ 5.77456094e-02 1.87060237e-01 1.67086825e-01 -1.96939409e-01 -3.57399315e-01 -6.51475549e-01 4.81055886e-01 3.88217837e-01 -4.53562170e-01 5.23094356e-01 -3.26546192e-01 -5.53987138e-02 -3.11373264e-01 -8.52894843e-01 -1.07334507e+00 -3.82407039e-01 6.46456853e-02 1.03678060e+00 4.41094577e-01 -1.70126945e-01 3.42835397e-01 8.51804912e-01 -1.77557659e+00 1.58386588e-01 8.13928723e-01 1.28627849e+00 7.31256187e-01 2.90364116e-01 -2.97628075e-01 7.59031624e-02 -2.76956975e-01 -1.87100768e-02 5.59777856e-01 3.16522539e-01 -5.51678598e-01 3.08142453e-01 3.55545104e-01 -5.56170821e-01 -1.80895373e-01 9.42814410e-01 1.99173719e-01 1.46295518e-01 7.64378965e-01 -1.08191264e+00 -4.98597920e-01 2.84872115e-01 -2.96511680e-01 -6.68644309e-01 1.92734078e-01 3.25787663e-01 6.27919376e-01 -1.07438290e+00 9.07696545e-01 1.32640207e+00 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8.94048139e-02 -1.78057384e+00 -1.10103190e+00 -9.77540672e-01 -2.80502796e-01 4.02397066e-01 2.75018811e-01 -1.18617439e+00 -3.17988778e-03 -1.28061011e-01]	[5.939987659454346, -0.9042958617210388]
a93369e8-bb54-49fa-b98f-04a50f735095	guided-source-separation	2011.04569	null	https://arxiv.org/abs/2011.04569v4	https://arxiv.org/pdf/2011.04569v4.pdf	Informed Source Extraction With Application to Acoustic Echo Reduction	Informed speaker extraction aims to extract a target speech signal from a mixture of sources given prior knowledge about the desired speaker. Recent deep learning-based methods leverage a speaker discriminative model that maps a reference snippet uttered by the target speaker into a single embedding vector that encapsulates the characteristics of the target speaker. However, such modeling deliberately neglects the time-varying properties of the reference signal. In this work, we assume that a reference signal is available that is temporally correlated with the target signal. To take this correlation into account, we propose a time-varying source discriminative model that captures the temporal dynamics of the reference signal. We also show that existing methods and the proposed method can be generalized to non-speech sources as well. Experimental results demonstrate that the proposed method significantly improves the extraction performance when applied in an acoustic echo reduction scenario.	['Wolfgang Mack', 'Emanuël A. P. Habets', 'Mohamed Elminshawi']	2020-11-09	null	null	null	null	['acoustic-echo-cancellation', 'speaker-separation', 'acoustic-echo-cancellation']	['medical', 'speech', 'speech']	[ 2.70805717e-01 -3.75468992e-02 1.27022579e-01 -3.04232329e-01 -1.12325013e+00 -4.67721611e-01 6.75393522e-01 -2.33081251e-01 -3.87190655e-02 1.47589222e-01 6.72885776e-01 1.57866612e-01 -2.35857293e-02 -2.64475048e-01 -5.20097077e-01 -1.04649174e+00 -1.56631172e-01 -9.15592983e-02 -1.52551726e-01 -1.26421064e-01 -1.69676542e-01 4.02725995e-01 -1.26681566e+00 -1.17002591e-01 3.67220610e-01 8.97141218e-01 4.30196285e-01 7.85883844e-01 7.29723126e-02 6.24399424e-01 -9.09982860e-01 -2.32498869e-02 2.33213454e-01 -5.89409053e-01 -1.27940297e-01 1.39342919e-01 3.88723224e-01 -2.53005743e-01 -8.73507082e-01 1.09175444e+00 6.87735140e-01 1.77533433e-01 4.52333570e-01 -9.90210116e-01 -2.14968264e-01 8.57908905e-01 -3.49755824e-01 4.60724860e-01 -4.11038734e-02 -9.64632928e-02 1.04348934e+00 -9.94893670e-01 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5ac589ba-264e-4d31-b59c-d4bae92ab986	implicit-and-efficient-point-cloud-completion	2209.00522	null	https://arxiv.org/abs/2209.00522v2	https://arxiv.org/pdf/2209.00522v2.pdf	Implicit and Efficient Point Cloud Completion for 3D Single Object Tracking	The point cloud based 3D single object tracking has drawn increasing attention. Although many breakthroughs have been achieved, we also reveal two severe issues. By extensive analysis, we find the prediction manner of current approaches is non-robust, i.e., exposing a misalignment gap between prediction score and actually localization accuracy. Another issue is the sparse point returns will damage the feature matching procedure of the SOT task. Based on these insights, we introduce two novel modules, i.e., Adaptive Refine Prediction (ARP) and Target Knowledge Transfer (TKT), to tackle them, respectively. To this end, we first design a strong pipeline to extract discriminative features and conduct the matching with the attention mechanism. Then, ARP module is proposed to tackle the misalignment issue by aggregating all predicted candidates with valuable clues. Finally, TKT module is designed to effectively overcome incomplete point cloud due to sparse and occlusion issues. We call our overall framework PCET. By conducting extensive experiments on the KITTI and Waymo Open Dataset, our model achieves state-of-the-art performance while maintaining a lower computational cost.	['Xiaoping Li', 'Hangcheng Yu', 'En Yu', 'Jinrong Yang', 'Shengkai Wu', 'Liangliang Ren', 'Pan Wang']	2022-09-01	null	null	null	null	['point-cloud-completion', '3d-single-object-tracking']	['computer-vision', 'computer-vision']	[-1.66990831e-02 -2.91883081e-01 -1.79415837e-01 -2.28303552e-01 -9.41339016e-01 -4.34148341e-01 5.34939528e-01 -1.34865835e-01 6.24999814e-02 2.78842598e-01 4.91484404e-02 -5.21807112e-02 -1.64022803e-01 -5.46607971e-01 -7.96026826e-01 -6.27006233e-01 2.08353326e-01 3.32141906e-01 7.61925101e-01 1.04123592e-01 5.06636977e-01 4.75722909e-01 -1.57424819e+00 -3.83122056e-03 9.31752801e-01 1.16409850e+00 3.47501904e-01 3.29427719e-02 -1.59109876e-01 4.66992676e-01 -2.42807597e-01 -3.60543609e-01 4.66796130e-01 1.53685182e-01 -3.72576743e-01 7.28841573e-02 4.99918461e-01 -2.89538741e-01 -3.91451865e-01 1.14348304e+00 5.60607851e-01 -1.26325622e-01 3.79152477e-01 -1.40170014e+00 -4.98354197e-01 2.03513891e-01 -9.50569212e-01 1.11875251e-01 1.97346687e-01 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043f47bd-4060-4299-98c4-050524dac3fe	robot-basics-representation-rotation-and	2211.02786	null	https://arxiv.org/abs/2211.02786v2	https://arxiv.org/pdf/2211.02786v2.pdf	Robot Basics: Representation, Rotation and Velocity	In this article, we plan to provide an introduction about some basics about robots for readers. Several key topics of classic robotics will be introduced, including robot representation, robot rotational motion, coordinates transformation and velocity transformation. By now, classic rigid-body robot analysis is still the main-stream approach in robot controlling and motion planning. In this article, no data-driven or machine learning based methods will be introduced. Most of the materials covered in this article are based on the rigid-body kinematics that the readers probably have learned from the physics course at high-school or college. Meanwhile, these classic robot kinematics analyses will serve as the foundation for the latest intelligent robot control algorithms in modern robotics studies.	['Jiawei Zhang']	2022-11-05	null	null	null	null	['motion-planning']	['robots']	[-2.09076762e-01 1.06399707e-01 -7.50862360e-01 -3.33423950e-02 3.38601559e-01 -2.78331429e-01 3.47569436e-01 -2.84852147e-01 -3.63346040e-01 5.36902130e-01 -4.22683179e-01 -2.65297830e-01 -3.32496136e-01 -5.83056450e-01 -5.87684035e-01 -7.31624126e-01 -2.95396209e-01 3.43278259e-01 6.18002191e-02 -1.15032089e+00 5.46049953e-01 8.71650279e-01 -1.24633920e+00 -8.74494553e-01 5.79563200e-01 4.28252965e-01 8.58171701e-01 5.14853239e-01 2.49512359e-01 6.17727816e-01 -1.79087788e-01 7.99563229e-02 2.23191559e-01 -2.87865192e-01 -8.16446304e-01 -1.00026026e-01 -6.68734074e-01 1.34301390e-02 -6.53114378e-01 9.83447671e-01 2.57053971e-01 6.14584208e-01 9.17892694e-01 -1.32495856e+00 -2.97447890e-01 7.42644787e-01 -4.19850737e-01 -3.56610417e-01 6.79910123e-01 -2.28287783e-02 3.44332308e-01 -8.53137136e-01 7.20710635e-01 1.18938732e+00 7.23651767e-01 4.70457792e-01 -2.34084710e-01 -5.95887154e-02 -1.07008383e-01 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47b5baeb-0933-4afa-837b-5306e40c093b	fakeout-leveraging-out-of-domain-self	2212.00773	null	https://arxiv.org/abs/2212.00773v1	https://arxiv.org/pdf/2212.00773v1.pdf	FakeOut: Leveraging Out-of-domain Self-supervision for Multi-modal Video Deepfake Detection	Video synthesis methods rapidly improved in recent years, allowing easy creation of synthetic humans. This poses a problem, especially in the era of social media, as synthetic videos of speaking humans can be used to spread misinformation in a convincing manner. Thus, there is a pressing need for accurate and robust deepfake detection methods, that can detect forgery techniques not seen during training. In this work, we explore whether this can be done by leveraging a multi-modal, out-of-domain backbone trained in a self-supervised manner, adapted to the video deepfake domain. We propose FakeOut; a novel approach that relies on multi-modal data throughout both the pre-training phase and the adaption phase. We demonstrate the efficacy and robustness of FakeOut in detecting various types of deepfakes, especially manipulations which were not seen during training. Our method achieves state-of-the-art results in cross-manipulation and cross-dataset generalization. This study shows that, perhaps surprisingly, training on out-of-domain videos (i.e., videos with no speaking humans), can lead to better deepfake detection systems. Code is available on GitHub.	['Ohad Fried', 'Gil Knafo']	2022-12-01	null	null	null	null	['face-swapping']	['computer-vision']	[ 5.60719632e-02 -2.12208077e-01 -2.78299372e-03 -6.10438325e-02 -5.72978199e-01 -8.15088987e-01 7.55094051e-01 -2.47524410e-01 -4.08673346e-01 6.00204587e-01 2.19514728e-01 9.48791802e-02 2.49757513e-01 -6.88995600e-01 -1.06236541e+00 -4.03500348e-01 -1.59617007e-01 1.63580567e-01 3.28125656e-01 -4.36930686e-01 1.14426017e-01 4.10927445e-01 -1.72281659e+00 7.10525751e-01 6.91674948e-01 8.09182286e-01 -8.85786265e-02 7.21117496e-01 4.54801768e-01 9.26171243e-01 -1.11103201e+00 -7.84177601e-01 2.51735955e-01 -5.40840924e-01 -6.22927070e-01 1.82227209e-01 7.85759985e-01 -1.00820148e+00 -6.81612313e-01 1.08158171e+00 5.32868147e-01 7.54702836e-02 3.83437514e-01 -1.32512105e+00 -6.14471078e-01 3.63447666e-01 -3.34675759e-01 4.36328202e-01 5.37466645e-01 6.03862464e-01 4.24397290e-01 -8.55531633e-01 8.07915092e-01 1.34863067e+00 6.70562983e-01 8.01303148e-01 -1.06211197e+00 -1.00777245e+00 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38c204a7-9f31-47a6-963d-566a4bfb13d5	deep-attention-diffusion-graph-neural	null	null	https://aclanthology.org/2021.emnlp-main.642	https://aclanthology.org/2021.emnlp-main.642.pdf	Deep Attention Diffusion Graph Neural Networks for Text Classification	Text classification is a fundamental task with broad applications in natural language processing. Recently, graph neural networks (GNNs) have attracted much attention due to their powerful representation ability. However, most existing methods for text classification based on GNNs consider only one-hop neighborhoods and low-frequency information within texts, which cannot fully utilize the rich context information of documents. Moreover, these models suffer from over-smoothing issues if many graph layers are stacked. In this paper, a Deep Attention Diffusion Graph Neural Network (DADGNN) model is proposed to learn text representations, bridging the chasm of interaction difficulties between a word and its distant neighbors. Experimental results on various standard benchmark datasets demonstrate the superior performance of the present approach.	['Xiaoyue Feng', 'Yanchun Liang', 'Fausto Giunchiglia', 'Renchu Guan', 'Yonghao Liu']	null	null	null	null	emnlp-2021-11	['deep-attention', 'deep-attention']	['computer-vision', 'natural-language-processing']	[-6.86501190e-02 -9.09171700e-02 -3.26032430e-01 -2.18987644e-01 2.24352449e-01 -7.43973162e-03 7.31026292e-01 7.17198849e-01 -3.30560982e-01 3.55376273e-01 4.07112449e-01 -5.52223742e-01 -3.12421501e-01 -1.11870015e+00 -1.43077597e-01 -6.54854476e-01 -9.70361289e-03 2.14372471e-01 3.13953578e-01 -4.78413969e-01 2.54735738e-01 1.76551908e-01 -7.97871351e-01 1.08896485e-02 1.15767527e+00 7.92209387e-01 2.21244752e-01 3.46565396e-01 -8.22108626e-01 8.80591452e-01 -5.42395055e-01 -4.46348101e-01 -2.39504158e-01 -5.39775550e-01 -6.19316876e-01 -8.91095251e-02 1.53065309e-01 -1.12286873e-01 -1.08145440e+00 1.42305398e+00 2.67477900e-01 4.48958665e-01 5.95233262e-01 -9.86676633e-01 -1.45532894e+00 9.69119787e-01 -8.23586702e-01 4.67471004e-01 1.56165704e-01 -1.99258491e-01 1.26982868e+00 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8f28e349-4d91-4609-b853-593337c11507	gconet-a-stronger-group-collaborative-co	2205.15469	null	https://arxiv.org/abs/2205.15469v4	https://arxiv.org/pdf/2205.15469v4.pdf	GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector	In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in natural scenes. The proposed GCoNet+ achieves the new state-of-the-art performance for co-salient object detection (CoSOD) through mining consensus representations based on the following two essential criteria: 1) intra-group compactness to better formulate the consistency among co-salient objects by capturing their inherent shared attributes using our novel group affinity module (GAM); 2) inter-group separability to effectively suppress the influence of noisy objects on the output by introducing our new group collaborating module (GCM) conditioning on the inconsistent consensus. To further improve the accuracy, we design a series of simple yet effective components as follows: i) a recurrent auxiliary classification module (RACM) promoting model learning at the semantic level; ii) a confidence enhancement module (CEM) assisting the model in improving the quality of the final predictions; and iii) a group-based symmetric triplet (GST) loss guiding the model to learn more discriminative features. Extensive experiments on three challenging benchmarks, i.e., CoCA, CoSOD3k, and CoSal2015, demonstrate that our GCoNet+ outperforms the existing 12 cutting-edge models. Code has been released at https://github.com/ZhengPeng7/GCoNet_plus.	['Chi-Keung Tang', 'Yu-Wing Tai', 'Luc van Gool', 'Jie Qin', 'Qi Fan', 'Deng-Ping Fan', 'Huazhu Fu', 'Peng Zheng']	2022-05-30	null	null	null	null	['co-saliency-detection']	['computer-vision']	[ 6.98397961e-03 -1.02563702e-01 -1.40964642e-01 -2.87798584e-01 -8.07900310e-01 1.07365549e-02 4.95972991e-01 2.07110733e-01 -2.57268816e-01 1.60590798e-01 1.31946340e-01 -3.03873862e-03 -2.21207052e-01 -4.09514815e-01 -8.08038712e-01 -7.64424026e-01 1.02223083e-03 1.41673505e-01 6.16965473e-01 -1.61024816e-02 2.61079997e-01 1.83792576e-01 -1.71375573e+00 4.06745940e-01 1.24182606e+00 1.41278517e+00 5.80187023e-01 9.58217382e-02 2.88551390e-01 8.89420092e-01 9.96426865e-03 -1.73589677e-01 3.35338801e-01 -2.13842317e-01 -4.67668265e-01 2.03967765e-01 3.63244981e-01 -1.06388582e-02 -1.21106915e-02 1.04795134e+00 5.51875710e-01 2.94398040e-01 4.99031991e-01 -1.35027492e+00 -6.03428721e-01 5.05362213e-01 -9.47199285e-01 2.84816653e-01 -1.76183745e-01 1.31425470e-01 1.27587664e+00 -1.45664895e+00 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0794cd0d-883e-43a7-831a-56181eebdede	diversity-measurable-anomaly-detection	2303.05047	null	https://arxiv.org/abs/2303.05047v1	https://arxiv.org/pdf/2303.05047v1.pdf	Diversity-Measurable Anomaly Detection	Reconstruction-based anomaly detection models achieve their purpose by suppressing the generalization ability for anomaly. However, diverse normal patterns are consequently not well reconstructed as well. Although some efforts have been made to alleviate this problem by modeling sample diversity, they suffer from shortcut learning due to undesired transmission of abnormal information. In this paper, to better handle the tradeoff problem, we propose Diversity-Measurable Anomaly Detection (DMAD) framework to enhance reconstruction diversity while avoid the undesired generalization on anomalies. To this end, we design Pyramid Deformation Module (PDM), which models diverse normals and measures the severity of anomaly by estimating multi-scale deformation fields from reconstructed reference to original input. Integrated with an information compression module, PDM essentially decouples deformation from prototypical embedding and makes the final anomaly score more reliable. Experimental results on both surveillance videos and industrial images demonstrate the effectiveness of our method. In addition, DMAD works equally well in front of contaminated data and anomaly-like normal samples.	['Xilin Chen', 'Shiguang Shan', 'Bingpeng Ma', 'Hong Chang', 'Wenrui Liu']	2023-03-09	null	http://openaccess.thecvf.com//content/CVPR2023/html/Liu_Diversity-Measurable_Anomaly_Detection_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_Diversity-Measurable_Anomaly_Detection_CVPR_2023_paper.pdf	cvpr-2023-1	['anomaly-detection-in-surveillance-videos', 'defect-detection', 'anomaly-detection-in-surveillance-videos', 'one-class-classification']	['computer-vision', 'computer-vision', 'methodology', 'miscellaneous']	[ 3.80647361e-01 -7.16891587e-02 2.09398702e-01 -3.72584850e-01 -3.97063971e-01 -2.55650818e-01 5.10390162e-01 1.32252619e-01 1.95065081e-01 1.87960014e-01 3.57520610e-01 1.11042462e-01 -2.16144204e-01 -9.13652599e-01 -6.36229157e-01 -7.75474608e-01 -7.89887831e-02 -1.49226993e-01 1.93600744e-01 -2.49407962e-01 3.48363847e-01 5.23794830e-01 -1.47092509e+00 2.89060503e-01 1.29904759e+00 1.15077329e+00 -9.22803506e-02 1.82089582e-01 -1.13807656e-01 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ddefad95-cf11-4ae3-90ac-ec58b337dff8	multimodal-side-tuning-for-document	2301.07502	null	https://arxiv.org/abs/2301.07502v2	https://arxiv.org/pdf/2301.07502v2.pdf	Multimodal Side-Tuning for Document Classification	In this paper, we propose to exploit the side-tuning framework for multimodal document classification. Side-tuning is a methodology for network adaptation recently introduced to solve some of the problems related to previous approaches. Thanks to this technique it is actually possible to overcome model rigidity and catastrophic forgetting of transfer learning by fine-tuning. The proposed solution uses off-the-shelf deep learning architectures leveraging the side-tuning framework to combine a base model with a tandem of two side networks. We show that side-tuning can be successfully employed also when different data sources are considered, e.g. text and images in document classification. The experimental results show that this approach pushes further the limit for document classification accuracy with respect to the state of the art.	['Maurizio Gabbrielli', 'Giuseppe Lisanti', 'Stefano Pio Zingaro']	2023-01-16	null	null	null	null	['document-image-classification', 'document-classification']	['computer-vision', 'natural-language-processing']	[ 6.94729090e-02 2.10792184e-01 -5.30750863e-02 -3.33146542e-01 -3.46948415e-01 -5.77200711e-01 8.45449150e-01 1.14571907e-01 -7.73941398e-01 7.80379832e-01 -1.25274286e-01 -2.46130839e-01 -3.01696181e-01 -7.06363142e-01 -6.58735454e-01 -6.36423707e-01 1.32686555e-01 7.90308893e-01 4.24655825e-01 -5.40873766e-01 3.04750532e-01 6.55592799e-01 -1.63026822e+00 7.54359126e-01 4.73810107e-01 7.53763914e-01 1.61121428e-01 7.44202256e-01 -5.36444187e-01 3.67945105e-01 -4.77006346e-01 -6.56948984e-01 5.10094799e-02 -8.66554752e-02 -8.28726530e-01 -3.46793756e-02 4.45230871e-01 -1.63735643e-01 6.76056892e-02 7.69596636e-01 6.10023320e-01 7.40832314e-02 7.49991477e-01 -9.28854704e-01 -3.95948619e-01 7.82228351e-01 -3.71145248e-01 1.07092030e-01 7.35891908e-02 -1.51736334e-01 5.82493424e-01 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a27103b8-1ee8-4519-abfc-6e3e1ee043c9	learning-based-sound-speed-reconstruction-and	2306.11034	null	https://arxiv.org/abs/2306.11034v1	https://arxiv.org/pdf/2306.11034v1.pdf	Learning-based sound speed reconstruction and aberration correction in linear-array photoacoustic/ultrasound imaging	Photoacoustic (PA) image reconstruction involves acoustic inversion that necessitates the specification of the speed of sound (SoS) within the medium of propagation. Due to the lack of information on the spatial distribution of the SoS within heterogeneous soft tissue, a homogeneous SoS distribution (such as 1540 m/s) is typically assumed in PA image reconstruction, similar to that of ultrasound (US) imaging. Failure to compensate the SoS variations leads to aberration artefacts, deteriorating the image quality. In this work, we developed a deep learning framework for SoS reconstruction and subsequent aberration correction in a dual-modal PA/US imaging system sharing a clinical US probe. As the PA and US data were inherently co-registered, the reconstructed SoS distribution from US channel data using deep neural networks was utilised for accurate PA image reconstruction. On a numerical and a tissue-mimicking phantom, this framework was able to significantly suppress US aberration artefacts, with the structural similarity index measure (SSIM) of up to 0.8109 and 0.8128 as compared to the conventional approach (0.6096 and 0.5985, respectively). The networks, trained only on simulated US data, also demonstrated a good generalisation ability on data from ex vivo tissues and the wrist and fingers of healthy human volunteers, and thus could be valuable in various in vivo applications to enhance PA image reconstruction.	['Wenfeng Xia', 'Tom Vercauteren', 'Mengjie Shi']	2023-06-19	null	null	null	null	['image-reconstruction']	['computer-vision']	[ 6.16581798e-01 7.02432543e-02 9.09333229e-01 9.11481753e-02 -9.59186018e-01 -2.20698163e-01 2.53377497e-01 2.13716328e-01 -6.05958879e-01 5.01416624e-01 1.19437486e-01 -2.58509129e-01 -4.07150894e-01 -6.21454298e-01 -5.57454586e-01 -1.28111660e+00 -4.84223515e-01 1.98766589e-01 4.64460641e-01 -2.75624264e-02 -1.11566104e-01 4.46375042e-01 -1.38047838e+00 1.69948205e-01 7.43113577e-01 1.23027492e+00 3.17228079e-01 8.51812780e-01 2.43255585e-01 5.27996600e-01 -5.20257115e-01 -1.80975482e-01 1.68882817e-01 -3.13950866e-01 -5.24333775e-01 -4.53263640e-01 2.90905029e-01 -5.20754158e-01 -4.13772523e-01 8.93857896e-01 1.10441232e+00 1.50999770e-01 8.70579004e-01 -4.78682816e-01 -2.68655330e-01 6.54422104e-01 -5.27431607e-01 3.05507064e-01 3.46554101e-01 1.62836254e-01 3.26318294e-01 -5.21145284e-01 4.37309474e-01 3.39309871e-01 9.79502976e-01 4.17169511e-01 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-9.32228386e-01 4.54950213e-01 6.79995775e-01 -4.33425814e-01 -7.02592552e-01 -6.57640845e-02]	[12.703429222106934, -2.583390712738037]
cc4d22f4-fe1e-4993-9bc2-d826c9208d51	prnu-based-source-camera-identification-for-1	2201.11737	null	https://arxiv.org/abs/2201.11737v1	https://arxiv.org/pdf/2201.11737v1.pdf	PRNU Based Source Camera Identification for Webcam and Smartphone Videos	This communication is about an application of image forensics where we use camera sensor fingerprints to identify source camera (SCI: Source Camera Identification) in webcam/smartphone videos. Sensor or camera fingerprints are based on computing the intrinsic noise that is always present in this kind of sensors due to manufacturing imperfections. This is an unavoidable characteristic that links each sensor with its noise pattern. PRNU (Photo Response Non-Uniformity) has become the default technique to compute a camera fingerprint. There are many applications nowadays dealing with PRNU patterns for camera identification using still images. In this work we focus on video, first on webcam video and afterwards on smartphone video. Webcams and smartphones are the most used video cameras nowadays. Three possible methods for SCI are implemented and assessed in this work.	['Fernando Isasi-de-Vicente', 'Fernando Martín-Rodríguez']	2022-01-27	null	null	null	null	['image-forensics']	['computer-vision']	[ 6.37640595e-01 -6.09360516e-01 -1.54222846e-01 2.11500645e-01 -4.20727074e-01 -9.82156396e-01 5.73355496e-01 -1.11672208e-01 -3.46991360e-01 5.10813594e-01 -1.79015234e-01 -2.21002579e-01 -2.00586677e-01 -6.30261123e-01 -8.03061128e-01 -6.01899624e-01 3.40449154e-01 -6.38196841e-02 4.32223558e-01 3.61225367e-01 6.34817302e-01 8.21116090e-01 -1.93436050e+00 2.99230337e-01 2.32288793e-01 1.11489356e+00 2.52458036e-01 9.91257071e-01 -2.14395896e-02 7.59182751e-01 -6.93962753e-01 -5.43291688e-01 2.22130150e-01 -4.52042103e-01 -2.85939455e-01 8.97201225e-02 4.33451802e-01 -3.71675730e-01 7.32772127e-02 1.58465731e+00 1.16024591e-01 -3.71970534e-01 7.59171307e-01 -1.26056921e+00 -2.51210868e-01 5.37524045e-01 -6.09388709e-01 2.45282963e-01 8.90440643e-01 -1.67238042e-01 9.53217521e-02 -3.74550700e-01 5.15944123e-01 9.21002507e-01 9.44956779e-01 2.36217171e-01 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