Split (2)

train · 44.6k rows

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90984327-93d5-439f-b795-bcc4a94c0034	stability-of-q-learning-through-design-and	2307.02632	null	https://arxiv.org/abs/2307.02632v1	https://arxiv.org/pdf/2307.02632v1.pdf	Stability of Q-Learning Through Design and Optimism	Q-learning has become an important part of the reinforcement learning toolkit since its introduction in the dissertation of Chris Watkins in the 1980s. The purpose of this paper is in part a tutorial on stochastic approximation and Q-learning, providing details regarding the INFORMS APS inaugural Applied Probability Trust Plenary Lecture, presented in Nancy France, June 2023. The paper also presents new approaches to ensure stability and potentially accelerated convergence for these algorithms, and stochastic approximation in other settings. Two contributions are entirely new: 1. Stability of Q-learning with linear function approximation has been an open topic for research for over three decades. It is shown that with appropriate optimistic training in the form of a modified Gibbs policy, there exists a solution to the projected Bellman equation, and the algorithm is stable (in terms of bounded parameter estimates). Convergence remains one of many open topics for research. 2. The new Zap Zero algorithm is designed to approximate the Newton-Raphson flow without matrix inversion. It is stable and convergent under mild assumptions on the mean flow vector field for the algorithm, and compatible statistical assumption on an underlying Markov chain. The algorithm is a general approach to stochastic approximation which in particular applies to Q-learning with "oblivious" training even with non-linear function approximation.	['Sean Meyn']	2023-07-05	null	null	null	null	['q-learning']	['methodology']	[-3.51761311e-01 2.56477714e-01 -5.46907604e-01 9.94907469e-02 -1.08540785e+00 -3.98984015e-01 4.06815678e-01 -1.65912822e-01 -6.35644555e-01 1.36865687e+00 -3.31159048e-02 -6.67099774e-01 -4.13904935e-01 -4.71799701e-01 -8.71727407e-01 -1.07169962e+00 -2.22992793e-01 5.19094825e-01 -1.11981757e-01 -3.05990487e-01 5.31013906e-01 5.00038862e-01 -1.20114636e+00 -3.66417676e-01 8.52666557e-01 8.13263774e-01 -3.51750031e-02 1.23048496e+00 -1.53751761e-01 1.08624935e+00 -4.90563035e-01 -3.97610843e-01 4.52248126e-01 -7.25354552e-01 -7.35851765e-01 -7.12085748e-03 3.11501086e-01 -5.82642496e-01 -5.06595373e-01 1.30278099e+00 5.65685809e-01 5.73711336e-01 6.89105570e-01 -1.29283583e+00 -5.17547056e-02 6.30608976e-01 -4.98103023e-01 5.02431035e-01 2.50570089e-01 3.11306596e-01 9.65446770e-01 -6.38289213e-01 1.23150758e-01 1.37487030e+00 9.16784883e-01 6.66748166e-01 -1.35451758e+00 -4.30968612e-01 -8.05537507e-04 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4e5d46f0-a539-46e9-808e-7c0233a79998	handling-imbalanced-classification-problems	2204.10231	null	https://arxiv.org/abs/2204.10231v1	https://arxiv.org/pdf/2204.10231v1.pdf	Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization	Support vector machines (SVMs) are popular learning algorithms to deal with binary classification problems. They traditionally assume equal misclassification costs for each class; however, real-world problems may have an uneven class distribution. This article introduces EBCS-SVM: evolutionary bilevel cost-sensitive SVMs. EBCS-SVM handles imbalanced classification problems by simultaneously learning the support vectors and optimizing the SVM hyperparameters, which comprise the kernel parameter and misclassification costs. The resulting optimization problem is a bilevel problem, where the lower level determines the support vectors and the upper level the hyperparameters. This optimization problem is solved using an evolutionary algorithm (EA) at the upper level and sequential minimal optimization (SMO) at the lower level. These two methods work in a nested fashion, that is, the optimal support vectors help guide the search of the hyperparameters, and the lower level is initialized based on previous successful solutions. The proposed method is assessed using 70 datasets of imbalanced classification and compared with several state-of-the-art methods. The experimental results, supported by a Bayesian test, provided evidence of the effectiveness of EBCS-SVM when working with highly imbalanced datasets.	['Francisco Herrera', 'Salvador García', 'Alejandro Rosales-Pérez']	2022-04-21	null	null	null	null	['imbalanced-classification']	['miscellaneous']	[ 1.70314535e-01 -3.17063212e-01 -4.48172450e-01 -4.26015705e-01 -1.14325128e-01 -1.32057905e-01 1.05658680e-01 2.87518859e-01 -4.10411716e-01 1.03158998e+00 -4.98852283e-01 -2.42762595e-01 -5.13768554e-01 -6.59724891e-01 -2.61881202e-01 -1.10923374e+00 9.18038934e-02 7.94389963e-01 4.99729395e-01 -3.53005767e-01 8.08025241e-01 3.99346143e-01 -1.98067498e+00 4.21157390e-01 1.29528749e+00 1.24305916e+00 -2.29668632e-01 5.04235625e-01 -3.79263237e-02 4.50453669e-01 -7.67665327e-01 -2.86145091e-01 1.06983349e-01 -1.95011869e-01 -5.61270952e-01 -9.94643569e-02 1.53444618e-01 2.46714398e-01 2.40280345e-01 9.45450008e-01 3.66695136e-01 2.38260105e-01 8.27332675e-01 -1.83193231e+00 -2.95279711e-01 -8.55645165e-03 -3.46412748e-01 4.58704203e-01 -2.98051182e-02 -5.51785007e-02 8.50665390e-01 -7.79687107e-01 3.49461019e-01 8.07497740e-01 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016b3cd5-1fb2-4e13-bc22-a57ebb4cb925	an-integrated-inverse-space-sparse	1803.03562	null	http://arxiv.org/abs/1803.03562v4	http://arxiv.org/pdf/1803.03562v4.pdf	An Integrated Inverse Space Sparse Representation Framework for Tumor Classification	Microarray gene expression data-based tumor classification is an active and challenging issue. In this paper, an integrated tumor classification framework is presented, which aims to exploit information in existing available samples, and focuses on the small sample problem and unbalanced classification problem. Firstly, an inverse space sparse representation based classification (ISSRC) model is proposed by considering the characteristics of gene-based tumor data, such as sparsity and a small number of training samples. A decision information factors (DIF)-based gene selection method is constructed to enhance the representation ability of the ISSRC. It is worth noting that the DIF is established from reducing clinical misdiagnosis rate and dimension of small sample data. For further improving the representation ability and classification stability of the ISSRC, feature learning is conducted on the selected gene subset. The feature learning method is constructed by complementing the advantages of non-negative matrix factorization (NMF) and deep learning. Without confusion, the ISSRC combined with gene selection and feature learning is called the integrated ISSRC, whose stability, optimization and the corresponding convergence are analyzed. Extensive experiments on six public microarray gene expression datasets show the integrated ISSRC-based tumor classification framework is superior to classical and state-of-the-art methods. There are significant improvements in classification accuracy, specificity and sensitivity, whether there is a tumor in the early diagnosis, what kind of tumor, or whether metastasis occurs after tumor surgery.	['Wen-Ming Wu', 'Li-Jun Yang', 'Yun-Mei Chen', 'Xiaohui Yang', 'Xianqi Li', 'Dan Long', 'Juan Zhang']	2018-03-09	null	null	null	null	['sparse-representation-based-classification']	['computer-vision']	[ 2.85654873e-01 -4.38206166e-01 -4.93451864e-01 -2.11013556e-01 -5.00297666e-01 1.94133639e-01 3.20872702e-02 -1.40975013e-01 -2.17209592e-01 7.62196898e-01 3.86506200e-01 -9.20503289e-02 -6.37726963e-01 -6.77601755e-01 -1.69645697e-01 -1.43082368e+00 1.09756522e-01 1.17461279e-01 -3.40639412e-01 -2.85234541e-01 5.00903502e-02 4.72080737e-01 -1.47041845e+00 4.45068210e-01 1.07613397e+00 1.21394289e+00 2.32194185e-01 1.38642251e-01 -3.23331624e-01 6.39731646e-01 -3.52905720e-01 2.77663171e-01 -2.69517787e-02 -4.10954982e-01 -4.44847345e-01 3.47613215e-01 -4.53448176e-01 2.48477414e-01 -4.60143626e-01 1.02661228e+00 6.36718631e-01 6.66544214e-02 4.79429901e-01 -1.33884966e+00 -3.43651175e-01 3.88775021e-01 -7.03292668e-01 2.18984723e-01 -2.83889733e-02 -1.62046641e-01 6.76013649e-01 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aa873092-6fbb-4b45-8fa5-1a8fabf82166	dataset2vec-learning-dataset-meta-features	1905.11063	null	https://arxiv.org/abs/1905.11063v4	https://arxiv.org/pdf/1905.11063v4.pdf	Dataset2Vec: Learning Dataset Meta-Features	Meta-learning, or learning to learn, is a machine learning approach that utilizes prior learning experiences to expedite the learning process on unseen tasks. As a data-driven approach, meta-learning requires meta-features that represent the primary learning tasks or datasets, and are estimated traditonally as engineered dataset statistics that require expert domain knowledge tailored for every meta-task. In this paper, first, we propose a meta-feature extractor called Dataset2Vec that combines the versatility of engineered dataset meta-features with the expressivity of meta-features learned by deep neural networks. Primary learning tasks or datasets are represented as hierarchical sets, i.e., as a set of sets, esp. as a set of predictor/target pairs, and then a DeepSet architecture is employed to regress meta-features on them. Second, we propose a novel auxiliary meta-learning task with abundant data called dataset similarity learning that aims to predict if two batches stem from the same dataset or different ones. In an experiment on a large-scale hyperparameter optimization task for 120 UCI datasets with varying schemas as a meta-learning task, we show that the meta-features of Dataset2Vec outperform the expert engineered meta-features and thus demonstrate the usefulness of learned meta-features for datasets with varying schemas for the first time.	['Josif Grabocka', 'Lars Schmidt-Thieme', 'Hadi S. Jomaa']	2019-05-27	null	null	null	null	['auxiliary-learning']	['methodology']	[ 1.69065639e-01 3.76944020e-02 -2.43341878e-01 -6.52366877e-01 -9.06431437e-01 -4.22706157e-01 7.15418935e-01 2.73464233e-01 -3.61834913e-01 6.97564662e-01 1.01177976e-01 9.77314487e-02 -4.07799095e-01 -7.28959620e-01 -9.41659272e-01 -7.09273279e-01 2.28047311e-01 4.06724781e-01 -2.39827141e-01 -4.67774123e-01 2.99448729e-01 1.00212522e-01 -2.07021093e+00 8.82854402e-01 8.92124951e-01 1.33814406e+00 1.69061452e-01 4.98275995e-01 -3.31912160e-01 7.36021221e-01 -6.97621465e-01 -5.33120334e-01 3.01253408e-01 -2.43038177e-01 -6.39353871e-01 -3.14435177e-02 6.31172776e-01 1.01898707e-01 -2.80145139e-01 7.72078633e-01 4.24972683e-01 1.86097592e-01 8.16069186e-01 -1.38293612e+00 -7.07223892e-01 7.42372751e-01 -2.01436281e-01 2.98314214e-01 -1.67850778e-01 2.74352372e-01 1.21075737e+00 -1.17301488e+00 3.24797183e-01 1.02418423e+00 5.82075238e-01 6.70404911e-01 -1.10380578e+00 -6.56869471e-01 1.43889412e-01 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-5.37849188e-01 -1.39206126e-01]	[9.905158996582031, 3.1631836891174316]
c92feec0-150c-46a8-9b9d-b3f315398b1f	florunito-trac-2-retrofitting-word-embeddings	null	null	https://aclanthology.org/2020.trac-1.17	https://aclanthology.org/2020.trac-1.17.pdf	FlorUniTo@TRAC-2: Retrofitting Word Embeddings on an Abusive Lexicon for Aggressive Language Detection	This paper describes our participation to the TRAC-2 Shared Tasks on Aggression Identification. Our team, FlorUniTo, investigated the applicability of using an abusive lexicon to enhance word embeddings towards improving detection of aggressive language. The embeddings used in our paper are word-aligned pre-trained vectors for English, Hindi, and Bengali, to reflect the languages in the shared task data sets. The embeddings are retrofitted to a multilingual abusive lexicon, HurtLex. We experimented with an LSTM model using the original as well as the transformed embeddings and different language and setting variations. Overall, our systems placed toward the middle of the official rankings based on weighted F1 score. However, the results on the development and test sets show promising improvements across languages, especially on the misogynistic aggression sub-task.	['Anna Koufakou', 'Viviana Patti', 'Valerio Basile']	2020-05-01	null	null	null	lrec-2020-5	['aggression-identification']	['natural-language-processing']	[-5.97745121e-01 -2.99721748e-01 8.10202733e-02 -3.91202211e-01 -7.81873345e-01 -4.39858466e-01 5.85139513e-01 3.73497419e-02 -1.29683256e+00 4.72286344e-01 6.58830225e-01 3.80198620e-02 -2.64784694e-01 -2.95815498e-01 1.88932478e-01 -3.87538403e-01 -7.52418414e-02 7.86052287e-01 -1.00865424e-01 -7.83552527e-01 2.12965474e-01 5.70675194e-01 -9.17420447e-01 3.15264136e-01 5.91726542e-01 1.29549608e-01 -5.41021049e-01 7.40721762e-01 -3.22059757e-04 7.68198609e-01 -7.27271616e-01 -1.08914673e+00 1.73839912e-01 -1.25116542e-01 -9.27867293e-01 -4.58626837e-01 7.34153807e-01 -5.88924944e-01 -6.03039145e-01 8.37113142e-01 7.85284281e-01 2.60305494e-01 6.43630147e-01 -9.65578854e-01 -9.51998532e-01 8.14127207e-01 -4.63128388e-01 7.87637591e-01 3.76236707e-01 3.30843151e-01 1.23829079e+00 -1.02526915e+00 5.89299917e-01 1.38473558e+00 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72e8bef3-bf76-4ecc-86af-d89d27379820	state-regularized-policy-optimization-on-data	2306.03552	null	https://arxiv.org/abs/2306.03552v1	https://arxiv.org/pdf/2306.03552v1.pdf	State Regularized Policy Optimization on Data with Dynamics Shift	In many real-world scenarios, Reinforcement Learning (RL) algorithms are trained on data with dynamics shift, i.e., with different underlying environment dynamics. A majority of current methods address such issue by training context encoders to identify environment parameters. Data with dynamics shift are separated according to their environment parameters to train the corresponding policy. However, these methods can be sample inefficient as data are used \textit{ad hoc}, and policies trained for one dynamics cannot benefit from data collected in all other environments with different dynamics. In this paper, we find that in many environments with similar structures and different dynamics, optimal policies have similar stationary state distributions. We exploit such property and learn the stationary state distribution from data with dynamics shift for efficient data reuse. Such distribution is used to regularize the policy trained in a new environment, leading to the SRPO (\textbf{S}tate \textbf{R}egularized \textbf{P}olicy \textbf{O}ptimization) algorithm. To conduct theoretical analyses, the intuition of similar environment structures is characterized by the notion of homomorphous MDPs. We then demonstrate a lower-bound performance guarantee on policies regularized by the stationary state distribution. In practice, SRPO can be an add-on module to context-based algorithms in both online and offline RL settings. Experimental results show that SRPO can make several context-based algorithms far more data efficient and significantly improve their overall performance.	['Bo An', 'Kun Gai', 'Peng Jiang', 'Dong Zheng', 'Shuchang Liu', 'Qingpeng Cai', 'Zhenghai Xue']	2023-06-06	null	null	null	null	['offline-rl']	['playing-games']	[ 7.18517900e-02 -3.38812232e-01 -6.18979692e-01 -1.06361300e-01 -3.17834139e-01 -8.89807880e-01 5.30429542e-01 1.18061505e-01 -8.53850186e-01 9.40288484e-01 2.91804910e-01 -3.98508519e-01 -1.95498496e-01 -7.31287062e-01 -1.10925841e+00 -1.10546386e+00 -4.56802815e-01 5.30376732e-01 1.25927135e-01 -3.53442401e-01 2.87224054e-01 3.30290079e-01 -1.59543657e+00 -5.29031530e-02 7.09208846e-01 6.76251888e-01 9.23267543e-01 7.63274312e-01 5.08511513e-02 7.64849067e-01 -5.49827278e-01 1.82434693e-01 7.38623977e-01 -3.27472955e-01 -5.06235838e-01 -1.15689725e-01 9.38011855e-02 -5.98730326e-01 -7.74710715e-01 1.04885721e+00 5.27742207e-01 6.63954854e-01 2.25808933e-01 -1.10149336e+00 -5.54297030e-01 6.69048727e-01 -4.59142894e-01 4.25735831e-01 8.75792652e-02 3.18765610e-01 8.79524052e-01 -3.79063427e-01 7.79629648e-01 1.26880300e+00 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-4.57574278e-01 -1.21235096e+00 -5.31865954e-01 -6.14240944e-01 4.94918376e-01 5.47357082e-01 -9.01612520e-01 -5.28454781e-02 -6.50095269e-02]	[4.105900764465332, 2.1710898876190186]
ad27c03d-166d-4479-b3dc-1a53a08947de	racial-bias-trends-in-the-text-of-us-legal	2307.01693	null	https://arxiv.org/abs/2307.01693v1	https://arxiv.org/pdf/2307.01693v1.pdf	Racial Bias Trends in the Text of US Legal Opinions	Although there is widespread recognition of racial bias in US law, it is unclear how such bias appears in the language of law, namely judicial opinions, and whether it varies across time period or region. Building upon approaches for measuring implicit racial bias in large-scale corpora, we approximate GloVe word embeddings for over 6 million US federal and state court cases from 1860 to 2009. We find strong evidence of racial bias across nearly all regions and time periods, as traditionally Black names are more closely associated with pre-classified "unpleasant" terms whereas traditionally White names are more closely associated with pre-classified "pleasant" terms. We also test whether legal opinions before 1950 exhibit more implicit racial bias than those after 1950, as well as whether opinions from Southern states exhibit less change in racial bias than those from Northeastern states. We do not find evidence of elevated bias in legal opinions before 1950, or evidence that legal opinions from Northeastern states show greater change in racial bias over time compared to Southern states. These results motivate further research into institutionalized racial bias.	['Rohan Jinturkar']	2023-07-04	null	null	null	null	['word-embeddings']	['methodology']	[-3.43350470e-01 -2.91845560e-01 -8.34090531e-01 -5.82928717e-01 -4.13411111e-01 -1.04390359e+00 9.44130361e-01 5.74407279e-01 -1.06632411e+00 8.18412542e-01 1.11603045e+00 -1.15464938e+00 -2.11722367e-02 -9.14511323e-01 -2.70804405e-01 -4.12757665e-01 6.05035782e-01 -1.20626859e-01 -8.33566308e-01 -3.40373427e-01 8.40691447e-01 5.48229754e-01 -6.33999765e-01 -1.39312316e-02 1.08706546e+00 2.00875066e-02 -7.14824796e-01 1.96672216e-01 -2.05286950e-01 9.42312896e-01 -7.73432553e-01 -8.04002047e-01 4.43301648e-01 -1.42797530e-01 -6.64259374e-01 -5.05246818e-01 1.36318612e+00 -3.60464394e-01 -5.90706706e-01 1.46431911e+00 4.15203750e-01 1.72244653e-01 1.06097484e+00 -5.77264428e-01 -1.71294606e+00 8.05164158e-01 -7.20935643e-01 7.32835650e-01 9.41913202e-02 4.64226604e-01 1.36530960e+00 -5.12341976e-01 9.31684554e-01 1.50187528e+00 4.35762793e-01 5.02070189e-01 -1.49386621e+00 -1.08525825e+00 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3bc245c4-f879-4fca-9af8-55f4c9873bef	from-hypergraph-energy-functions-to	2306.09623	null	https://arxiv.org/abs/2306.09623v2	https://arxiv.org/pdf/2306.09623v2.pdf	From Hypergraph Energy Functions to Hypergraph Neural Networks	Hypergraphs are a powerful abstraction for representing higher-order interactions between entities of interest. To exploit these relationships in making downstream predictions, a variety of hypergraph neural network architectures have recently been proposed, in large part building upon precursors from the more traditional graph neural network (GNN) literature. Somewhat differently, in this paper we begin by presenting an expressive family of parameterized, hypergraph-regularized energy functions. We then demonstrate how minimizers of these energies effectively serve as node embeddings that, when paired with a parameterized classifier, can be trained end-to-end via a supervised bilevel optimization process. Later, we draw parallels between the implicit architecture of the predictive models emerging from the proposed bilevel hypergraph optimization, and existing GNN architectures in common use. Empirically, we demonstrate state-of-the-art results on various hypergraph node classification benchmarks. Code is available at https://github.com/yxzwang/PhenomNN.	['David Wipf', 'Xuanjing Huang', 'Xipeng Qiu', 'Quan Gan', 'Yuxin Wang']	2023-06-16	null	null	null	null	['node-classification', 'bilevel-optimization']	['graphs', 'methodology']	[ 1.54153496e-01 7.63326943e-01 -5.95496416e-01 -4.12624180e-01 -1.95658103e-01 -4.76036042e-01 7.26404309e-01 4.40830231e-01 -4.15971912e-02 7.57616162e-01 3.06680381e-01 -5.92445374e-01 -5.70742428e-01 -1.02323472e+00 -6.75984263e-01 -6.12574279e-01 -5.37568271e-01 7.30346620e-01 -1.56469300e-01 -2.98782438e-01 3.57378135e-03 5.66592455e-01 -9.81926024e-01 -2.88755842e-03 7.00342178e-01 6.63366854e-01 -3.58084470e-01 6.77482247e-01 1.17488597e-02 5.95960319e-01 -5.24519533e-02 -7.74663746e-01 2.24204049e-01 -1.27476364e-01 -9.31931138e-01 -3.58258367e-01 4.04079556e-01 1.21739231e-01 -1.07705128e+00 8.44247401e-01 2.59212315e-01 2.06847072e-01 7.13260055e-01 -1.48468876e+00 -1.23618722e+00 1.00337851e+00 -2.04409942e-01 1.90584779e-01 4.43891212e-02 2.48052582e-01 1.72881436e+00 -7.50590026e-01 8.10700417e-01 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1.54243529e-01 -8.92843246e-01 -5.77789485e-01 -9.03529525e-01 -5.08196115e-01 4.11624372e-01 2.17983574e-01 -5.23254395e-01 -2.21404329e-01 -3.09434146e-01]	[6.958078384399414, 6.272940635681152]
f80b9957-331b-496f-9864-1143d0c391d2	depth-map-estimation-and-colorization-of	null	null	http://openaccess.thecvf.com/content_iccv_2015/html/Williem_Depth_Map_Estimation_ICCV_2015_paper.html	http://openaccess.thecvf.com/content_iccv_2015/papers/Williem_Depth_Map_Estimation_ICCV_2015_paper.pdf	Depth Map Estimation and Colorization of Anaglyph Images Using Local Color Prior and Reverse Intensity Distribution	In this paper, we present a joint iterative anaglyph stereo matching and colorization framework for obtaining a set of disparity maps and colorized images. Conventional stereo matching algorithms fail when addressing anaglyph images that do not have similar intensities on their two respective view images. To resolve this problem, we propose two novel data costs using local color prior and reverse intensity distribution factor for obtaining accurate depth maps. To colorize an anaglyph image, each pixel in one view is warped to another view using the obtained disparity values of non-occluded regions. A colorization algorithm using optimization is then employed with additional constraint to colorize the remaining occluded regions. Experimental results confirm that the proposed unified framework is robust and produces accurate depth maps and colorized stereo images.	['Ramesh Raskar', 'W. Williem', 'In Kyu Park']	2015-12-01	null	null	null	iccv-2015-12	['stereo-matching']	['computer-vision']	[ 5.57954192e-01 -3.70344728e-01 -2.29746662e-02 -3.91067445e-01 -2.71129251e-01 -5.73799312e-01 1.72692806e-01 -8.68174434e-02 -4.18872595e-01 9.39724028e-01 -8.03180933e-02 1.50036933e-02 1.33691579e-01 -9.93268073e-01 -4.90402073e-01 -6.91389322e-01 6.35807157e-01 1.31929651e-01 4.41639155e-01 2.66914144e-02 8.11352491e-01 5.32208204e-01 -1.78033984e+00 1.11798570e-01 1.01764870e+00 6.35836005e-01 2.96283215e-01 6.84460938e-01 -9.07124206e-02 3.18386316e-01 -1.10805154e-01 -1.23579197e-01 7.23086357e-01 -5.07636130e-01 -5.24061501e-01 4.92064327e-01 8.90794516e-01 -7.02499986e-01 -7.12635294e-02 1.38622510e+00 2.40335509e-01 1.24161437e-01 3.07543814e-01 -1.20963609e+00 -4.84345287e-01 -1.65661991e-01 -1.24305224e+00 7.44398311e-02 6.14433050e-01 -1.92359895e-01 6.21812642e-01 -9.90204275e-01 6.57103479e-01 1.15552425e+00 1.11816384e-01 3.55396122e-01 -1.32010126e+00 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6.56859934e-01 3.22937131e-01 -6.29998803e-01 -5.66625774e-01 1.66291654e-01]	[9.24197006225586, -2.4735517501831055]
d3889b88-46cf-45bc-9a35-737ad914fbc7	prototypical-residual-networks-for-anomaly	2212.02031	null	https://arxiv.org/abs/2212.02031v2	https://arxiv.org/pdf/2212.02031v2.pdf	Prototypical Residual Networks for Anomaly Detection and Localization	Anomaly detection and localization are widely used in industrial manufacturing for its efficiency and effectiveness. Anomalies are rare and hard to collect and supervised models easily over-fit to these seen anomalies with a handful of abnormal samples, producing unsatisfactory performance. On the other hand, anomalies are typically subtle, hard to discern, and of various appearance, making it difficult to detect anomalies and let alone locate anomalous regions. To address these issues, we propose a framework called Prototypical Residual Network (PRN), which learns feature residuals of varying scales and sizes between anomalous and normal patterns to accurately reconstruct the segmentation maps of anomalous regions. PRN mainly consists of two parts: multi-scale prototypes that explicitly represent the residual features of anomalies to normal patterns; a multisize self-attention mechanism that enables variable-sized anomalous feature learning. Besides, we present a variety of anomaly generation strategies that consider both seen and unseen appearance variance to enlarge and diversify anomalies. Extensive experiments on the challenging and widely used MVTec AD benchmark show that PRN outperforms current state-of-the-art unsupervised and supervised methods. We further report SOTA results on three additional datasets to demonstrate the effectiveness and generalizability of PRN.	['Yu-Gang Jiang', 'Zhineng Chen', 'Zheng Wang', 'Zuxuan Wu', 'HUI ZHANG']	2022-12-05	null	http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Prototypical_Residual_Networks_for_Anomaly_Detection_and_Localization_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Prototypical_Residual_Networks_for_Anomaly_Detection_and_Localization_CVPR_2023_paper.pdf	cvpr-2023-1	['supervised-anomaly-detection']	['computer-vision']	[ 3.59954655e-01 -2.67858148e-01 9.61731896e-02 -2.76129693e-01 -6.56871557e-01 -2.78216362e-01 3.57240677e-01 9.47978646e-02 5.51276565e-01 1.89515099e-01 -3.53251129e-01 -1.89728573e-01 -2.15347484e-01 -5.88594019e-01 -7.15820968e-01 -9.17688668e-01 -1.25826642e-01 3.71818334e-01 3.11984837e-01 -2.16808125e-01 4.33805645e-01 7.74039090e-01 -1.98579812e+00 2.58203566e-01 1.18035841e+00 1.38234735e+00 -4.53786328e-02 3.80072802e-01 -1.74153462e-01 6.55865133e-01 -8.59474778e-01 1.45850852e-01 4.07120883e-01 -4.28835630e-01 -3.42612833e-01 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e39e4b08-5d65-414f-b8c7-9f8415a6e268	statistical-performance-of-radio	1902.10448	null	http://arxiv.org/abs/1902.10448v2	http://arxiv.org/pdf/1902.10448v2.pdf	Statistical Performance of Radio Interferometric Calibration	Calibration is an essential step in radio interferometric data processing that corrects the data for systematic errors and in addition, subtracts bright foreground interference to reveal weak signals hidden in the residual. These weak and unknown signals are much sought after to reach many science goals but the effect of calibration on such signals is an ever present concern. The main reason for this is the incompleteness of the model used in calibration. Distributed calibration based on consensus optimization has been shown to mitigate the effect due to model incompleteness by calibrating data covering a wide bandwidth in a computationally efficient manner. In this paper, we study the statistical performance of direction dependent distributed calibration, i.e., the distortion caused by calibration on the residual statistics. In order to study this, we consider the mapping between the input uncalibrated data and the output residual data. We derive an analytical relationship for the influence of the input on the residual and use this to find the relationship between the input and output probability density functions. Using simulations we show that the smallest eigenvalue of the Jacobian of this mapping is a reliable indicator of the statistical performance of calibration. The analysis developed in this paper can also be applied to other data processing steps in radio interferometry such as imaging and foreground subtraction as well as to many other machine learning problems.	['Sarod Yatawatta']	2019-02-27	null	null	null	null	['radio-interferometry']	['miscellaneous']	[ 4.28624868e-01 -3.53882492e-01 4.07266438e-01 -3.59654397e-01 -7.16655672e-01 -4.22359973e-01 3.46577644e-01 -3.81443977e-01 -3.68719459e-01 8.99175346e-01 4.20329235e-02 -2.49811888e-01 -5.20135343e-01 -7.11244285e-01 -6.16800904e-01 -1.25063694e+00 2.84650475e-01 6.70801938e-01 2.70122796e-01 5.67028672e-02 2.12672681e-01 6.84618294e-01 -1.21350312e+00 -3.39299142e-01 1.00964522e+00 5.54735839e-01 2.54612565e-01 6.35865927e-01 1.93623811e-01 5.17948568e-01 -9.41997409e-01 1.40499637e-01 3.74315530e-01 -7.99306571e-01 -3.16245556e-01 1.68053180e-01 2.28870317e-01 4.19129953e-02 -1.53939083e-01 1.32478964e+00 5.70626795e-01 -7.45799690e-02 6.69525445e-01 -7.97744691e-01 7.64896870e-02 3.38554204e-01 -8.12386274e-01 4.67064887e-01 -2.22968936e-01 -6.67035207e-02 4.00894076e-01 -6.57078445e-01 5.18322349e-01 9.72364843e-01 4.77157027e-01 -3.49501073e-02 -1.47403252e+00 -6.84699953e-01 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6c2fcf28-dfab-48c4-ad12-2a534eee8418	image-correction-via-deep-reciprocating-hdr	1804.04371	null	http://arxiv.org/abs/1804.04371v1	http://arxiv.org/pdf/1804.04371v1.pdf	Image Correction via Deep Reciprocating HDR Transformation	Image correction aims to adjust an input image into a visually pleasing one. Existing approaches are proposed mainly from the perspective of image pixel manipulation. They are not effective to recover the details in the under/over exposed regions. In this paper, we revisit the image formation procedure and notice that the missing details in these regions exist in the corresponding high dynamic range (HDR) data. These details are well perceived by the human eyes but diminished in the low dynamic range (LDR) domain because of the tone mapping process. Therefore, we formulate the image correction task as an HDR transformation process and propose a novel approach called Deep Reciprocating HDR Transformation (DRHT). Given an input LDR image, we first reconstruct the missing details in the HDR domain. We then perform tone mapping on the predicted HDR data to generate the output LDR image with the recovered details. To this end, we propose a united framework consisting of two CNNs for HDR reconstruction and tone mapping. They are integrated end-to-end for joint training and prediction. Experiments on the standard benchmarks demonstrate that the proposed method performs favorably against state-of-the-art image correction methods.	['Qiang Zhang', 'Xin Yang', 'Xiaopeng Wei', 'Rynson Lau', 'Ke Xu', 'Yibing Song']	2018-04-12	image-correction-via-deep-reciprocating-hdr-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Yang_Image_Correction_via_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_Image_Correction_via_CVPR_2018_paper.pdf	cvpr-2018-6	['hdr-reconstruction', 'tone-mapping']	['computer-vision', 'computer-vision']	[ 7.54188299e-01 -4.95778769e-02 1.31415054e-02 -2.32396051e-01 -4.89495486e-01 -1.04236096e-01 4.34383482e-01 -4.33408797e-01 -1.42040551e-01 6.38787627e-01 3.11396986e-01 -7.91308135e-02 3.44790369e-01 -8.06355178e-01 -9.84056592e-01 -7.06158757e-01 6.09986365e-01 -1.85794324e-01 3.17593962e-01 -3.72275829e-01 2.92482048e-01 2.70022005e-01 -1.54364896e+00 2.19125524e-01 1.05780399e+00 8.69324625e-01 5.28487265e-01 7.70788014e-01 1.16548143e-01 1.08628118e+00 -3.10622722e-01 -2.28816047e-01 5.03790915e-01 -6.21440887e-01 -4.14462894e-01 2.49071538e-01 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5dbb4e78-8477-4cac-998f-bb9ffccdf421	deepdpm-dynamic-population-mapping-via-deep	1811.02644	null	http://arxiv.org/abs/1811.02644v2	http://arxiv.org/pdf/1811.02644v2.pdf	DeepDPM: Dynamic Population Mapping via Deep Neural Network	Dynamic high resolution data on human population distribution is of great importance for a wide spectrum of activities and real-life applications, but is too difficult and expensive to obtain directly. Therefore, generating fine-scaled population distributions from coarse population data is of great significance. However, there are three major challenges: 1) the complexity in spatial relations between high and low resolution population; 2) the dependence of population distributions on other external information; 3) the difficulty in retrieving temporal distribution patterns. In this paper, we first propose the idea to generate dynamic population distributions in full-time series, then we design dynamic population mapping via deep neural network(DeepDPM), a model that describes both spatial and temporal patterns using coarse data and point of interest information. In DeepDPM, we utilize super-resolution convolutional neural network(SRCNN) based model to directly map coarse data into higher resolution data, and a time-embedded long short-term memory model to effectively capture the periodicity nature to smooth the finer-scaled results from the previous static SRCNN model. We perform extensive experiments on a real-life mobile dataset collected from Shanghai. Our results demonstrate that DeepDPM outperforms previous state-of-the-art methods and a suite of frequent data-mining approaches. Moreover, DeepDPM breaks through the limitation from previous works in time dimension so that dynamic predictions in all-day time slots can be obtained.	['Hongzhi Shi', 'Kechun Liu', 'Zefang Zong', 'Jie Feng', 'Yong Li']	2018-10-25	null	null	null	null	['population-mapping']	['computer-vision']	[-1.72636673e-01 -5.80867231e-01 -3.52656007e-01 -8.73011649e-02 -6.04189694e-01 -2.36800890e-02 7.07586586e-01 8.31634849e-02 -3.31377774e-01 1.27712405e+00 6.36554718e-01 -2.15580896e-01 -4.78973269e-01 -1.64655256e+00 -7.47928441e-01 -6.11838639e-01 -5.65444827e-01 7.39455283e-01 1.49827152e-01 -6.52231276e-01 -2.50266463e-01 4.01717991e-01 -1.63470483e+00 8.53936523e-02 8.14788818e-01 9.78541732e-01 1.33885488e-01 3.48847359e-01 -1.86213240e-01 6.49084628e-01 -7.55603254e-01 1.11797720e-01 -1.04961672e-03 -1.46563709e-01 -5.50312817e-01 -3.53652805e-01 -1.77355066e-01 -3.45686674e-01 -4.65382665e-01 4.97131884e-01 7.71483302e-01 2.70190150e-01 5.66891253e-01 -1.02816010e+00 -1.03365242e+00 5.26359975e-01 -9.35556710e-01 7.34359026e-01 3.88720959e-01 -2.07314026e-02 3.45956683e-01 -5.20933747e-01 3.16703379e-01 1.34497261e+00 1.16693783e+00 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81e3529e-a507-4d5d-ad52-e82d1f97aed3	autonomy-2-0-the-quest-for-economies-of-scale	2307.03973	null	https://arxiv.org/abs/2307.03973v1	https://arxiv.org/pdf/2307.03973v1.pdf	Autonomy 2.0: The Quest for Economies of Scale	With the advancement of robotics and AI technologies in the past decade, we have now entered the age of autonomous machines. In this new age of information technology, autonomous machines, such as service robots, autonomous drones, delivery robots, and autonomous vehicles, rather than humans, will provide services. In this article, through examining the technical challenges and economic impact of the digital economy, we argue that scalability is both highly necessary from a technical perspective and significantly advantageous from an economic perspective, thus is the key for the autonomy industry to achieve its full potential. Nonetheless, the current development paradigm, dubbed Autonomy 1.0, scales with the number of engineers, instead of with the amount of data or compute resources, hence preventing the autonomy industry to fully benefit from the economies of scale, especially the exponentially cheapening compute cost and the explosion of available data. We further analyze the key scalability blockers and explain how a new development paradigm, dubbed Autonomy 2.0, can address these problems to greatly boost the autonomy industry.	['Yuhao Zhu', 'Shaoshan Liu', 'Bo Yu', 'Shuang Wu']	2023-07-08	null	null	null	null	['autonomous-vehicles']	['computer-vision']	[-2.90013880e-01 2.87155211e-01 -1.47308111e-01 9.70028862e-02 2.18348891e-01 -6.23419464e-01 5.71640372e-01 4.96314242e-02 -5.10272324e-01 6.27948105e-01 -1.87125877e-01 -3.56685042e-01 -7.78234079e-02 -8.38712037e-01 -3.13128382e-01 -3.50835919e-01 -2.59030432e-01 6.15726769e-01 2.05318689e-01 -6.46504104e-01 1.66589096e-01 6.53535306e-01 -1.87727690e+00 -7.26196110e-01 1.06274068e+00 1.21809161e+00 2.00267568e-01 4.06145215e-01 3.40302646e-01 4.95886296e-01 -1.86061978e-01 -1.52149290e-01 5.45267224e-01 -4.62193862e-02 -4.35008168e-01 -1.45530060e-01 -4.53282624e-01 -5.26851118e-01 -3.70778710e-01 8.31541121e-01 1.63461089e-01 -9.11455378e-02 2.00138465e-01 -1.90699077e+00 -2.51761496e-01 2.58434236e-01 -4.32772756e-01 -2.82546490e-01 1.18834428e-01 3.57449442e-01 8.32634866e-01 -6.19029701e-01 8.40559125e-01 9.29677725e-01 4.50001985e-01 2.13649213e-01 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-6.21950626e-01 -2.52402186e-01 1.44872116e-02 -7.74376512e-01 -4.17546362e-01 -1.09670997e-01]	[4.937410831451416, 1.2834296226501465]
11d590cc-5177-44e5-a1d8-f4df4550b108	ld-sds-towards-an-expressive-spoken-dialogue	1710.02973	null	http://arxiv.org/abs/1710.02973v1	http://arxiv.org/pdf/1710.02973v1.pdf	LD-SDS: Towards an Expressive Spoken Dialogue System based on Linked-Data	In this work we discuss the related challenges and describe an approach towards the fusion of state-of-the-art technologies from the Spoken Dialogue Systems (SDS) and the Semantic Web and Information Retrieval domains. We envision a dialogue system named LD-SDS that will support advanced, expressive, and engaging user requests, over multiple, complex, rich, and open-domain data sources that will leverage the wealth of the available Linked Data. Specifically, we focus on: a) improving the identification, disambiguation and linking of entities occurring in data sources and user input; b) offering advanced query services for exploiting the semantics of the data, with reasoning and exploratory capabilities; and c) expanding the typical information seeking dialogue model (slot filling) to better reflect real-world conversational search scenarios.	['Margarita Kotti', 'Alexandros Papangelis', 'Yannis Tzitzikas', 'Panagiotis Papadakos', 'Yannis Stylianou', 'Dimitris Plexousakis']	2017-10-09	null	null	null	null	['conversational-search']	['natural-language-processing']	[-2.64464408e-01 8.31247985e-01 2.22353190e-02 -3.80340278e-01 -8.45662832e-01 -8.80856514e-01 1.22651780e+00 7.61174023e-01 -3.71166766e-01 9.45835054e-01 1.05181944e+00 -1.15203224e-02 -5.37101388e-01 -6.25357211e-01 4.38789517e-01 2.71029383e-01 -3.62755209e-02 1.23147357e+00 6.61973894e-01 -1.18840194e+00 2.61920244e-01 4.05220091e-01 -1.80556595e+00 3.90031308e-01 9.93312895e-01 9.49604452e-01 1.52712837e-01 6.93481803e-01 -1.16100931e+00 8.20553184e-01 -5.94690979e-01 -2.82366306e-01 -3.18582416e-01 -4.53955568e-02 -1.68973565e+00 -1.59654140e-01 -2.07127482e-01 -2.60690868e-01 -1.14725098e-01 7.38758564e-01 6.77083492e-01 4.46616799e-01 6.14263788e-02 -1.23198330e+00 -4.99722987e-01 6.87787831e-01 3.63096356e-01 1.88628323e-02 1.39622855e+00 -1.55266583e-01 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44b65562-e483-4e27-9cb5-58eed892378a	a-comparative-study-of-transformers-on-word	2111.15417	null	https://arxiv.org/abs/2111.15417v1	https://arxiv.org/pdf/2111.15417v1.pdf	A Comparative Study of Transformers on Word Sense Disambiguation	Recent years of research in Natural Language Processing (NLP) have witnessed dramatic growth in training large models for generating context-aware language representations. In this regard, numerous NLP systems have leveraged the power of neural network-based architectures to incorporate sense information in embeddings, resulting in Contextualized Word Embeddings (CWEs). Despite this progress, the NLP community has not witnessed any significant work performing a comparative study on the contextualization power of such architectures. This paper presents a comparative study and an extensive analysis of nine widely adopted Transformer models. These models are BERT, CTRL, DistilBERT, OpenAI-GPT, OpenAI-GPT2, Transformer-XL, XLNet, ELECTRA, and ALBERT. We evaluate their contextualization power using two lexical sample Word Sense Disambiguation (WSD) tasks, SensEval-2 and SensEval-3. We adopt a simple yet effective approach to WSD that uses a k-Nearest Neighbor (kNN) classification on CWEs. Experimental results show that the proposed techniques also achieve superior results over the current state-of-the-art on both the WSD tasks	['Dr. Anil Kumar Singh', 'Gaurav Dhama', 'Vikas Bishnoi', 'Nidhi Mulay', 'Avi Chawla']	2021-11-30	null	null	null	null	['word-sense-disambiguation']	['natural-language-processing']	[-7.13444054e-02 1.80588719e-02 -2.46542796e-01 -4.48018312e-01 -7.13357449e-01 -5.43066025e-01 1.02302623e+00 7.22367227e-01 -9.60483074e-01 7.90835798e-01 6.76108241e-01 -6.02941751e-01 -1.59746841e-01 -9.39806342e-01 -3.58796977e-02 -2.81067431e-01 -9.95898098e-02 6.10196233e-01 7.70806968e-02 -8.39168668e-01 4.19695616e-01 3.88202012e-01 -1.21997857e+00 6.23724908e-02 9.23917711e-01 7.35861063e-01 2.88822234e-01 4.16690230e-01 -7.67460763e-01 1.62339672e-01 -5.66220880e-01 -2.43844286e-01 1.25085786e-01 1.63463950e-01 -7.59373367e-01 -8.58888447e-01 2.26484135e-01 3.89464915e-01 -1.43283084e-01 9.45155561e-01 8.20326567e-01 4.36645955e-01 5.71836054e-01 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e52f0111-0310-4735-aa37-ea0dd30b6c8b	sygma-system-for-generalizable-modular	2109.13430	null	https://arxiv.org/abs/2109.13430v1	https://arxiv.org/pdf/2109.13430v1.pdf	SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases	Knowledge Base Question Answering (KBQA) tasks that in-volve complex reasoning are emerging as an important re-search direction. However, most KBQA systems struggle withgeneralizability, particularly on two dimensions: (a) acrossmultiple reasoning types where both datasets and systems haveprimarily focused on multi-hop reasoning, and (b) across mul-tiple knowledge bases, where KBQA approaches are specif-ically tuned to a single knowledge base. In this paper, wepresent SYGMA, a modular approach facilitating general-izability across multiple knowledge bases and multiple rea-soning types. Specifically, SYGMA contains three high levelmodules: 1) KB-agnostic question understanding module thatis common across KBs 2) Rules to support additional reason-ing types and 3) KB-specific question mapping and answeringmodule to address the KB-specific aspects of the answer ex-traction. We demonstrate effectiveness of our system by evalu-ating on datasets belonging to two distinct knowledge bases,DBpedia and Wikidata. In addition, to demonstrate extensi-bility to additional reasoning types we evaluate on multi-hopreasoning datasets and a new Temporal KBQA benchmarkdataset on Wikidata, namedTempQA-WD1, introduced in thispaper. We show that our generalizable approach has bettercompetetive performance on multiple datasets on DBpediaand Wikidata that requires both multi-hop and temporal rea-soning	['L Venkata Subramaniam', 'Francois Luus', 'Guilherme LimaRyan Riegel', 'Alexander Gray', 'Salim Roukos', 'Rosario Uceda-Sosa', 'Maria Chang', 'Sairam Gurajada', 'Srinivas Ravishankar', 'Dinesh Khandelwal', 'G P Shrivatsa Bhargav', 'Achille Fokoue', 'Dinesh Garg', 'Saswati Dana', 'Cezar Pendus', 'Santosh Srivastava', 'Young-suk Lee', 'Nandana Mihindukulasooriya', 'Ibrahim Abdelaziz', 'Pavan Kapanipathi', 'Shajith Ikbal', 'Hima Karanam', 'Udit Sharma', 'Sumit Neelam']	2021-09-28	null	null	null	null	['knowledge-base-question-answering']	['natural-language-processing']	[-7.96065032e-01 4.30192381e-01 -3.28094721e-01 -2.86900252e-01 -1.25743222e+00 -8.59801710e-01 4.49257493e-01 3.65544349e-01 -4.27831352e-01 1.23736513e+00 2.84821570e-01 -6.12496376e-01 -7.27928162e-01 -1.30724347e+00 -7.76845217e-01 -4.05914895e-02 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2e745b7f-563e-49fb-8f1e-0acfa52a02b3	some-voices-are-too-common-building-fair	2306.03773	null	https://arxiv.org/abs/2306.03773v1	https://arxiv.org/pdf/2306.03773v1.pdf	Some voices are too common: Building fair speech recognition systems using the Common Voice dataset	Automatic speech recognition (ASR) systems become increasingly efficient thanks to new advances in neural network training like self-supervised learning. However, they are known to be unfair toward certain groups, for instance, people speaking with an accent. In this work, we use the French Common Voice dataset to quantify the biases of a pre-trained wav2vec~2.0 model toward several demographic groups. By fine-tuning the pre-trained model on a variety of fixed-size, carefully crafted training sets, we demonstrate the importance of speaker diversity. We also run an in-depth analysis of the Common Voice corpus and identify important shortcomings that should be taken into account by users of this dataset.	['Yannick Estève', 'Lucas Maison']	2023-06-01	null	null	null	null	['automatic-speech-recognition']	['speech']	[-1.32406503e-02 1.87330440e-01 -1.91588059e-01 -8.66596460e-01 -6.19165957e-01 -4.53173757e-01 7.08226740e-01 6.92042112e-02 -7.77183950e-01 5.85721254e-01 9.15355265e-01 -6.85353696e-01 2.11599767e-01 -3.98517191e-01 -2.45228022e-01 -3.79850954e-01 -2.15660315e-02 3.66213471e-01 -3.89820307e-01 -4.87468243e-01 -1.14850074e-01 4.86840039e-01 -1.13329732e+00 -7.95629472e-02 5.37709892e-01 5.73553681e-01 -2.72004336e-01 5.11319399e-01 -2.19395431e-03 5.89720190e-01 -8.38929117e-01 -7.40563631e-01 1.31596059e-01 -1.66431606e-01 -6.93859398e-01 6.18151426e-02 5.99837303e-01 -2.19592348e-01 -5.91581583e-01 1.04676843e+00 7.96662927e-01 5.78642964e-01 4.01691943e-01 -7.35434949e-01 -6.29944503e-01 1.17154336e+00 -9.50237364e-02 5.54015338e-01 1.07860990e-01 3.12323660e-01 1.23731554e+00 -6.46426320e-01 3.34203929e-01 1.47002280e+00 6.32774591e-01 8.01984847e-01 -1.23074746e+00 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ad0659d1-8b92-4769-b1ef-516e98bc2495	3d-scanning-system-for-automatic-high	1702.08112	null	http://arxiv.org/abs/1702.08112v1	http://arxiv.org/pdf/1702.08112v1.pdf	3D Scanning System for Automatic High-Resolution Plant Phenotyping	Thin leaves, fine stems, self-occlusion, non-rigid and slowly changing structures make plants difficult for three-dimensional (3D) scanning and reconstruction -- two critical steps in automated visual phenotyping. Many current solutions such as laser scanning, structured light, and multiview stereo can struggle to acquire usable 3D models because of limitations in scanning resolution and calibration accuracy. In response, we have developed a fast, low-cost, 3D scanning platform to image plants on a rotating stage with two tilting DSLR cameras centred on the plant. This uses new methods of camera calibration and background removal to achieve high-accuracy 3D reconstruction. We assessed the system's accuracy using a 3D visual hull reconstruction algorithm applied on 2 plastic models of dicotyledonous plants, 2 sorghum plants and 2 wheat plants across different sets of tilt angles. Scan times ranged from 3 minutes (to capture 72 images using 2 tilt angles), to 30 minutes (to capture 360 images using 10 tilt angles). The leaf lengths, widths, areas and perimeters of the plastic models were measured manually and compared to measurements from the scanning system: results were within 3-4% of each other. The 3D reconstructions obtained with the scanning system show excellent geometric agreement with all six plant specimens, even plants with thin leaves and fine stems.	['Chuong V. Nguyen', 'David R. Lovell', 'Robert Furbank', 'Peter Kuffner', 'Xavier Sirault', 'Helen Daily', 'Jurgen Fripp']	2017-02-26	null	null	null	null	['plant-phenotyping']	['computer-vision']	[ 4.35330302e-01 -2.72105366e-01 1.06382996e-01 -1.96187064e-01 -7.61746021e-04 -1.17375994e+00 -1.38382837e-01 1.15146279e-01 2.07000673e-01 2.38406196e-01 -4.06887293e-01 -8.26675832e-01 -7.44422972e-02 -5.31566024e-01 -1.32155448e-01 -1.44485727e-01 2.11534590e-01 8.37508082e-01 7.44447351e-01 -9.34841707e-02 1.75585613e-01 1.19859183e+00 -1.51566601e+00 -1.45551085e-01 5.75773597e-01 6.31401181e-01 1.06457138e+00 9.28358257e-01 -2.87883669e-01 -1.41755164e-01 -7.89418966e-02 1.23117946e-01 4.53739941e-01 -9.90355536e-02 -4.03411031e-01 7.68961787e-01 3.32602173e-01 -6.86278760e-01 4.84520823e-01 5.69930315e-01 2.08811551e-01 -6.42746389e-01 3.95982713e-01 -7.50602126e-01 -6.37386143e-01 5.35629205e-02 -1.28535044e+00 -4.00037467e-01 5.33807635e-01 2.58795530e-01 6.52943492e-01 -9.06753242e-01 7.95770764e-01 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3fb27faa-4f6a-4299-893a-bcf19b0a1ec7	weakly-supervised-gaze-estimation-from	2212.02997	null	https://arxiv.org/abs/2212.02997v2	https://arxiv.org/pdf/2212.02997v2.pdf	Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views	Developing gaze estimation models that generalize well to unseen domains and in-the-wild conditions remains a challenge with no known best solution. This is mostly due to the difficulty of acquiring ground truth data that cover the distribution of possible faces, head poses and environmental conditions that exist in the real world. In this work, we propose to train general gaze estimation models based on 3D geometry-aware gaze pseudo-annotations which we extract from arbitrary unlabelled face images, which are abundantly available in the internet. Additionally, we leverage the observation that head, body and hand pose estimation benefit from revising them as dense 3D coordinate prediction, and similarly express gaze estimation as regression of dense 3D eye meshes. We overcome the absence of compatible ground truth by fitting rigid 3D eyeballs on existing gaze datasets and design a multi-view supervision framework to balance the effect of pseudo-labels during training. We test our method in the task of gaze generalization, in which we demonstrate improvement of up to $30\%$ compared to state-of-the-art when no ground truth data are available, and up to $10\%$ when they are. The project material will become available for research purposes.	['Michail Christos Doukas', 'Stefanos Zafeiriou', 'Jia Guo', 'Jiankang Deng', 'Polydefkis Gkagkos', 'Evangelos Ververas']	2022-12-06	null	null	null	null	['gaze-estimation']	['computer-vision']	[ 4.42095473e-02 3.24471802e-01 4.57527675e-02 -6.40976906e-01 -4.23855156e-01 -4.19489443e-01 2.37738073e-01 -5.51371694e-01 -2.97783375e-01 6.73364639e-01 9.83408839e-02 9.73530486e-02 7.99654275e-02 -1.44478098e-01 -9.88353014e-01 -4.03267503e-01 1.39373079e-01 4.64360267e-01 -1.31601855e-01 -1.46030843e-01 1.85042247e-01 3.62296462e-01 -2.23221111e+00 -1.43203646e-01 7.23513663e-01 1.14826834e+00 -6.47830367e-02 4.79210049e-01 3.94748241e-01 1.99659333e-01 -2.24381059e-01 -6.71262264e-01 4.30884123e-01 -1.86314315e-01 -6.85152590e-01 3.31917405e-01 1.14736903e+00 -5.89941025e-01 3.40509303e-02 9.09442604e-01 5.49003243e-01 1.26140164e-02 4.89083678e-01 -1.44832337e+00 -4.49923217e-01 -2.50216544e-01 -9.36241031e-01 5.34033068e-02 6.48603022e-01 2.14072868e-01 7.93053985e-01 -9.20442343e-01 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9.68707353e-02 3.56336057e-01 -1.48374510e+00 -5.11756301e-01 -1.13155067e+00 -4.80912119e-01 4.75366145e-01 7.59806216e-01 -1.35883284e+00 -4.04796273e-01 3.84178042e-01]	[14.129305839538574, 0.015147840604186058]
5b518e05-eaeb-4d40-a5d7-ab6974f7034c	dynamic-graph-learning-with-content-guided	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Dynamic_Graph_Learning_With_Content-Guided_Spatial-Frequency_Relation_Reasoning_for_Deepfake_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Dynamic_Graph_Learning_With_Content-Guided_Spatial-Frequency_Relation_Reasoning_for_Deepfake_CVPR_2023_paper.pdf	Dynamic Graph Learning With Content-Guided Spatial-Frequency Relation Reasoning for Deepfake Detection	With the springing up of face synthesis techniques, it is prominent in need to develop powerful face forgery detection methods due to security concerns. Some existing methods attempt to employ auxiliary frequency-aware information combined with CNN backbones to discover the forged clues. Due to the inadequate information interaction with image content, the extracted frequency features are thus spatially irrelavant, struggling to generalize well on increasingly realistic counterfeit types. To address this issue, we propose a Spatial-Frequency Dynamic Graph method to exploit the relation-aware features in spatial and frequency domains via dynamic graph learning. To this end, we introduce three well-designed components: 1) Content-guided Adaptive Frequency Extraction module to mine the content-adaptive forged frequency clues. 2) Multiple Domains Attention Map Learning module to enrich the spatial-frequency contextual features with multiscale attention maps. 3) Dynamic Graph Spatial-Frequency Feature Fusion Network to explore the high-order relation of spatial and frequency features. Extensive experiments on several benchmark show that our proposed method sustainedly exceeds the state-of-the-arts by a considerable margin.	['Silong Peng', 'Xiyuan Hu', 'Chen Chen', 'Kun Yu', 'YuAn Wang']	2023-01-01	null	null	null	cvpr-2023-1	['deepfake-detection', 'face-swapping', 'face-generation']	['computer-vision', 'computer-vision', 'computer-vision']	[ 1.06252611e-01 -2.35374019e-01 -3.33525509e-01 -7.14890808e-02 -6.58265829e-01 -2.27705002e-01 5.04851162e-01 -9.03926138e-03 2.14239627e-01 3.99686098e-01 4.70392317e-01 -9.95384157e-03 -2.84924328e-01 -7.40280986e-01 -4.61786836e-01 -6.78449690e-01 -3.98861021e-01 -4.54038322e-01 2.40576029e-01 -5.40772796e-01 4.84003723e-01 5.26210308e-01 -1.52370453e+00 5.14122963e-01 8.09032559e-01 1.18124199e+00 -5.98932384e-03 3.40673745e-01 -1.38472840e-01 6.57629132e-01 -5.03114402e-01 -4.02832657e-01 1.61130577e-01 -6.94019675e-01 -5.18947065e-01 1.96825508e-02 3.56847852e-01 -3.89598787e-01 -6.60582185e-01 1.33473599e+00 4.51441705e-01 1.00416131e-01 2.58140743e-01 -1.26909459e+00 -1.11536634e+00 2.05021456e-01 -1.05741572e+00 7.97820687e-01 5.69758117e-01 4.04645234e-01 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1da86258-e1f4-4f36-b6bf-3b68ea924d48	qadi-arabic-dialect-identification-in-the	null	null	https://aclanthology.org/2021.wanlp-1.1	https://aclanthology.org/2021.wanlp-1.1.pdf	QADI: Arabic Dialect Identification in the Wild	Proper dialect identification is important for a variety of Arabic NLP applications. In this paper, we present a method for rapidly constructing a tweet dataset containing a wide range of country-level Arabic dialects —covering 18 different countries in the Middle East and North Africa region. Our method relies on applying multiple filters to identify users who belong to different countries based on their account descriptions and to eliminate tweets that either write mainly in Modern Standard Arabic or mostly use vulgar language. The resultant dataset contains 540k tweets from 2,525 users who are evenly distributed across 18 Arab countries. Using intrinsic evaluation, we show that the labels of a set of randomly selected tweets are 91.5% accurate. For extrinsic evaluation, we are able to build effective country level dialect identification on tweets with a macro-averaged F1-score of 60.6% across 18 classes.	['Kareem Darwish', 'Sabit Hassan', 'Younes Samih', 'Hamdy Mubarak', 'Ahmed Abdelali']	null	null	null	null	eacl-wanlp-2021-4	['dialect-identification']	['natural-language-processing']	[-3.02802444e-01 -1.79559365e-01 -1.13365337e-01 -4.91778404e-01 -1.03942120e+00 -1.12160468e+00 8.26084435e-01 2.76992261e-01 -4.83877599e-01 6.57971859e-01 2.12013841e-01 -3.28641862e-01 2.92873949e-01 -1.08067536e+00 -2.11437047e-01 -4.50832158e-01 8.64024237e-02 8.56762052e-01 -1.92932650e-01 -7.30868042e-01 4.33458865e-01 4.68289793e-01 -1.17586064e+00 3.11515778e-01 1.34869981e+00 4.51536596e-01 -3.44806790e-01 6.13883913e-01 -3.88303809e-02 3.88122946e-01 -8.21113169e-01 -1.01167727e+00 3.73368114e-02 -5.71205318e-01 -1.04429889e+00 7.99538642e-02 4.61032987e-01 -3.21905464e-01 1.19890243e-01 1.02860534e+00 3.38862568e-01 -2.18443051e-01 1.07793748e+00 -8.26111913e-01 -4.90781754e-01 9.67915237e-01 -6.48201227e-01 -8.86585936e-02 4.97282922e-01 -3.04706216e-01 1.07451677e+00 -1.26876390e+00 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5b078860-4166-4879-b4b0-68a1257fa93a	semi-supervised-segmentation-of-multi-vendor	null	null	https://ieeexplore.ieee.org/abstract/document/9477818	https://ieeexplore.ieee.org/abstract/document/9477818	Semi-Supervised Segmentation of Multi-vendor and Multi-center Cardiac MRI	Automatic segmentation of the heart cavity is an essential task for the diagnosis of cardiac diseases. In this paper, we propose a semi-supervised segmentation setup for leveraging unlabeled data to segment Left-ventricle, Right-ventricle, and Myocardium. We utilize an enhanced version of residual U-Net architecture on a large-scale cardiac MRI dataset. Handling the class imbalanced data issue using dice loss, the enhanced supervised model is able to achieve better dice scores in comparison with a vanilla U-Net model. We applied several augmentation techniques including histogram matching to increase the performance of our model in other domains. Also, we introduce a simple but efficient semi-supervised segmentation method to improve segmentation results without the need for large labeled data. Finally, we applied our method to two benchmark datasets, STACOM2018, and M&Ms 2020 challenges, to show the potency of the proposed model. The effectiveness of our proposed model is demonstrated by the quantitative results. The model achieves average dice scores of 0.921, 0.926, and 0.891 for Left-ventricle, Right-ventricle, and Myocardium respectively.	['Mahyar Bolhassani; Ilkay Oksuz']	2021-05-09	null	null	null	ieee-2021-5	['2d-semantic-segmentation']	['computer-vision']	[ 1.06397964e-01 2.03487262e-01 1.75124668e-02 -5.06175518e-01 -7.70314813e-01 -5.50135791e-01 2.13687159e-02 1.86898112e-01 -4.91874039e-01 7.65948296e-01 1.97277740e-02 -1.43280491e-01 1.91577211e-01 -4.47539985e-01 -3.41264546e-01 -6.11263573e-01 -3.18783745e-02 4.50405687e-01 3.89526099e-01 4.12640899e-01 1.95167586e-01 2.76737005e-01 -7.30734110e-01 -1.19131207e-01 9.44568157e-01 9.61045742e-01 6.02753870e-02 6.26182795e-01 1.77579731e-01 5.92253685e-01 -2.42450550e-01 -1.35146052e-01 4.43725467e-01 -4.90788162e-01 -8.45245123e-01 3.69701684e-01 2.86496103e-01 -4.61543262e-01 -1.73785128e-02 8.96999896e-01 7.39063680e-01 -5.65203764e-02 7.77341545e-01 -8.86282146e-01 -1.54529855e-01 5.95139623e-01 -7.83517838e-01 4.87434030e-01 -3.86501402e-01 5.04379049e-02 8.45981717e-01 -5.49228489e-01 8.96082282e-01 6.34071469e-01 7.04775989e-01 4.67639536e-01 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d6e8c2db-6fa1-4281-8ce9-b3005f25e181	convolutional-neural-network-based-image	1504.05241	null	http://arxiv.org/abs/1504.05241v1	http://arxiv.org/pdf/1504.05241v1.pdf	Convolutional Neural Network-Based Image Representation for Visual Loop Closure Detection	Deep convolutional neural networks (CNN) have recently been shown in many computer vision and pattern recog- nition applications to outperform by a significant margin state- of-the-art solutions that use traditional hand-crafted features. However, this impressive performance is yet to be fully exploited in robotics. In this paper, we focus one specific problem that can benefit from the recent development of the CNN technology, i.e., we focus on using a pre-trained CNN model as a method of generating an image representation appropriate for visual loop closure detection in SLAM (simultaneous localization and mapping). We perform a comprehensive evaluation of the outputs at the intermediate layers of a CNN as image descriptors, in comparison with state-of-the-art image descriptors, in terms of their ability to match images for detecting loop closures. The main conclusions of our study include: (a) CNN-based image representations perform comparably to state-of-the-art hand- crafted competitors in environments without significant lighting change, (b) they outperform state-of-the-art competitors when lighting changes significantly, and (c) they are also significantly faster to extract than the state-of-the-art hand-crafted features even on a conventional CPU and are two orders of magnitude faster on an entry-level GPU.	['Yi Hou', 'Shilin Zhou', 'Hong Zhang']	2015-04-20	null	null	null	null	['loop-closure-detection']	['computer-vision']	[ 1.23305120e-01 -1.95162833e-01 2.93355603e-02 -3.03078353e-01 -4.48308617e-01 -3.31269324e-01 9.06503260e-01 4.38032061e-01 -8.09776068e-01 3.09187770e-01 -9.96282324e-02 -2.67327935e-01 -9.33612958e-02 -7.85615504e-01 -9.82440770e-01 -3.31534684e-01 -3.26911211e-01 3.90840977e-01 4.56376940e-01 -5.55970967e-01 4.10409182e-01 9.38096285e-01 -2.19494224e+00 7.87695050e-02 3.06392014e-01 1.32890892e+00 1.78049996e-01 5.99746525e-01 1.21315150e-02 5.19890130e-01 -4.30137187e-01 1.52012795e-01 3.83070439e-01 -2.30242491e-01 -5.73892951e-01 -1.29232958e-01 8.69738817e-01 -3.44817154e-02 -5.14727592e-01 8.62883270e-01 3.97282153e-01 8.14898983e-02 4.52990472e-01 -1.09020770e+00 -3.91977072e-01 -2.63312578e-01 -2.81699777e-01 1.29578695e-01 4.04163986e-01 1.94372330e-02 8.71221900e-01 -1.03132558e+00 8.68507922e-01 1.15709817e+00 9.40274179e-01 1.73467040e-01 -1.20591259e+00 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-3.53783309e-01 4.42002833e-01 4.92941201e-01 -9.46900189e-01 -2.80749947e-01 -3.10234368e-01]	[7.758461952209473, -1.8449761867523193]
7bc5bdf7-2269-48e1-8979-7bbf5e042e61	interpretable-knowledge-tracing-simple-and	2112.11209	null	https://arxiv.org/abs/2112.11209v1	https://arxiv.org/pdf/2112.11209v1.pdf	Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations	Intelligent Tutoring Systems have become critically important in future learning environments. Knowledge Tracing (KT) is a crucial part of that system. It is about inferring the skill mastery of students and predicting their performance to adjust the curriculum accordingly. Deep Learning-based KT models have shown significant predictive performance compared with traditional models. However, it is difficult to extract psychologically meaningful explanations from the tens of thousands of parameters in neural networks, that would relate to cognitive theory. There are several ways to achieve high accuracy in student performance prediction but diagnostic and prognostic reasoning is more critical in learning sciences. Since KT problem has few observable features (problem ID and student's correctness at each practice), we extract meaningful latent features from students' response data by using machine learning and data mining techniques. In this work, we present Interpretable Knowledge Tracing (IKT), a simple model that relies on three meaningful latent features: individual skill mastery, ability profile (learning transfer across skills), and problem difficulty. IKT's prediction of future student performance is made using a Tree-Augmented Naive Bayes Classifier (TAN), therefore its predictions are easier to explain than deep learning-based student models. IKT also shows better student performance prediction than deep learning-based student models without requiring a huge amount of parameters. We conduct ablation studies on each feature to examine their contribution to student performance prediction. Thus, IKT has great potential for providing adaptive and personalized instructions with causal reasoning in real-world educational systems.	['Feida Zhu', 'Hisashi Kashima', 'Koh Takeuchi', 'Jill-Jenn Vie', 'Sein Minn']	2021-12-15	null	null	null	null	['skill-mastery']	['robots']	[-4.29827869e-02 3.80909175e-01 -4.49272752e-01 -5.48522770e-01 -9.01407972e-02 -4.11435604e-01 6.37775138e-02 6.18840277e-01 -6.56930916e-03 7.58904040e-01 2.04022124e-01 -8.94411445e-01 -9.57992375e-01 -9.88170981e-01 -5.08010268e-01 -2.26244003e-01 1.55407071e-01 4.28612560e-01 3.56839806e-01 -4.60559994e-01 4.89479572e-01 5.75303137e-01 -1.79454911e+00 3.61579448e-01 1.61750901e+00 5.54285347e-01 2.41145879e-01 7.27868557e-01 -2.70679981e-01 1.47920680e+00 -6.59627736e-01 -4.60209101e-01 -3.11851889e-01 -6.44279301e-01 -1.11273754e+00 -5.51079392e-01 4.44559127e-01 -3.88521582e-01 -1.24639623e-01 7.72687256e-01 1.78694457e-01 2.17484772e-01 8.53267848e-01 -1.29988301e+00 -9.31546807e-01 7.11059213e-01 -1.57838285e-01 2.57898957e-01 4.90541905e-01 -3.65814455e-02 6.74944460e-01 -2.54971802e-01 -8.67447630e-02 1.07398105e+00 6.95007861e-01 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-1.06391037e+00 -4.14649934e-01 2.55746514e-01 -5.27586676e-02 -7.63466418e-01 -2.03021705e-01 -1.63996499e-02]	[10.117523193359375, 7.247601509094238]
6ec53047-b2f2-4144-a964-1b1acb2cdbed	deep-optimal-transport-a-practical-algorithm	2306.02342	null	https://arxiv.org/abs/2306.02342v1	https://arxiv.org/pdf/2306.02342v1.pdf	Deep Optimal Transport: A Practical Algorithm for Photo-realistic Image Restoration	We propose an image restoration algorithm that can control the perceptual quality and/or the mean square error (MSE) of any pre-trained model, trading one over the other at test time. Our algorithm is few-shot: Given about a dozen images restored by the model, it can significantly improve the perceptual quality and/or the MSE of the model for newly restored images without further training. Our approach is motivated by a recent theoretical result that links between the minimum MSE (MMSE) predictor and the predictor that minimizes the MSE under a perfect perceptual quality constraint. Specifically, it has been shown that the latter can be obtained by optimally transporting the output of the former, such that its distribution matches the source data. Thus, to improve the perceptual quality of a predictor that was originally trained to minimize MSE, we approximate the optimal transport by a linear transformation in the latent space of a variational auto-encoder, which we compute in closed-form using empirical means and covariances. Going beyond the theory, we find that applying the same procedure on models that were initially trained to achieve high perceptual quality, typically improves their perceptual quality even further. And by interpolating the results with the original output of the model, we can improve their MSE on the expense of perceptual quality. We illustrate our method on a variety of degradations applied to general content images of arbitrary dimensions.	['Michael Elad', 'Tomer Michaeli', 'Guy Ohayon', 'Theo Adrai']	2023-06-04	null	null	null	null	['image-restoration']	['computer-vision']	[ 5.98106742e-01 2.64268845e-01 1.72841072e-01 -1.07303232e-01 -1.09613740e+00 -2.45850161e-01 3.75698000e-01 -2.03560606e-01 -3.32087874e-01 6.04242802e-01 1.81735098e-01 4.81391372e-03 -2.72271395e-01 -4.88113731e-01 -9.34807301e-01 -1.11443186e+00 6.57265261e-02 6.22055167e-03 -4.28264663e-02 1.74178407e-02 2.84320205e-01 2.97745645e-01 -1.57868361e+00 7.25099668e-02 1.05933154e+00 1.19059408e+00 4.99280006e-01 1.03315079e+00 5.33673584e-01 7.97679603e-01 -4.46842223e-01 -3.73219371e-01 5.48589885e-01 -8.24389160e-01 -5.98421395e-01 7.08387971e-01 4.89306808e-01 -4.52002108e-01 -6.36302650e-01 1.57693207e+00 2.67980456e-01 2.71260500e-01 7.37746775e-01 -8.40787888e-01 -1.11240709e+00 8.27102065e-02 -3.34311873e-01 1.18105829e-01 -2.90365182e-02 1.88787475e-01 8.33461106e-01 -7.76257038e-01 6.08509481e-01 1.26079738e+00 5.84192216e-01 4.16723222e-01 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-7.14802623e-01 3.19755942e-01 7.73838639e-01 -3.45893532e-01 -7.57108569e-01 6.44576132e-01]	[11.581391334533691, -2.1860554218292236]
eccc6163-f93e-402a-8e6c-710d022ab112	topical-coherence-in-lda-based-models-through	null	null	https://aclanthology.org/P17-1165	https://aclanthology.org/P17-1165.pdf	Topical Coherence in LDA-based Models through Induced Segmentation	This paper presents an LDA-based model that generates topically coherent segments within documents by jointly segmenting documents and assigning topics to their words. The coherence between topics is ensured through a copula, binding the topics associated to the words of a segment. In addition, this model relies on both document and segment specific topic distributions so as to capture fine grained differences in topic assignments. We show that the proposed model naturally encompasses other state-of-the-art LDA-based models designed for similar tasks. Furthermore, our experiments, conducted on six different publicly available datasets, show the effectiveness of our model in terms of perplexity, Normalized Pointwise Mutual Information, which captures the coherence between the generated topics, and the Micro F1 measure for text classification.	['Hesam Amoualian', 'Massih R. Amini', 'Wei Lu', 'Marianne Clausel', 'Georgios Balikas', 'Eric Gaussier']	2017-07-01	null	null	null	acl-2017-7	['ad-hoc-information-retrieval']	['natural-language-processing']	[-1.90745980e-01 1.95151582e-01 -5.99449813e-01 -4.42260534e-01 -1.17029345e+00 -7.14401186e-01 9.92751062e-01 3.48609298e-01 1.79615721e-01 7.32866585e-01 6.89690053e-01 -3.12511586e-02 -7.38720372e-02 -6.86449707e-01 -4.35892373e-01 -8.54682207e-01 -1.46318689e-01 6.85636640e-01 1.99347556e-01 1.27732813e-01 4.44737166e-01 -1.61831155e-01 -1.23745644e+00 4.07972127e-01 1.18835056e+00 5.27213812e-01 2.85270274e-01 3.83882493e-01 -4.14069831e-01 6.71947524e-02 -6.14027500e-01 -4.10366476e-01 -2.37076357e-01 -3.26856583e-01 -7.47063279e-01 2.62884647e-01 2.45120734e-01 1.64789721e-01 -6.16619922e-02 7.37995088e-01 -1.25650782e-02 1.62134945e-01 1.26442170e+00 -1.07229519e+00 -4.03901160e-01 9.65917587e-01 -7.70528197e-01 -1.01821333e-01 1.94959849e-01 -5.27734280e-01 1.68707836e+00 -7.15420127e-01 4.89499480e-01 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4c6de37b-7ca1-4597-a16a-fc16552e4606	a-generalized-alternating-method-for-bilevel	2306.02422	null	https://arxiv.org/abs/2306.02422v2	https://arxiv.org/pdf/2306.02422v2.pdf	A Generalized Alternating Method for Bilevel Learning under the Polyak-Łojasiewicz Condition	Bilevel optimization has recently regained interest owing to its applications in emerging machine learning fields such as hyperparameter optimization, meta-learning, and reinforcement learning. Recent results have shown that simple alternating (implicit) gradient-based algorithms can achieve the same convergence rate of single-level gradient descent (GD) for bilevel problems with a strongly convex lower-level objective. However, it remains unclear whether this result can be generalized to bilevel problems beyond this basic setting. In this paper, we propose a Generalized ALternating mEthod for bilevel opTimization (GALET) with a nonconvex lower-level objective that satisfies the Polyak-{\L}ojasiewicz (PL) condition. We first introduce a stationary metric for the considered bilevel problems, which generalizes the existing metric. We then establish that GALET achieves an $\epsilon$-stationary metric for the considered problem within $\tilde{\cal O}(\epsilon^{-1})$ iterations, which matches the iteration complexity of GD for smooth nonconvex problems.	['Tianyi Chen', 'Songtao Lu', 'Quan Xiao']	2023-06-04	null	null	null	null	['bilevel-optimization', 'hyperparameter-optimization']	['methodology', 'methodology']	[-2.75824815e-01 1.55825123e-01 -2.18550757e-01 -2.59891003e-01 -8.96388054e-01 -3.23438346e-01 5.80206960e-02 2.47696206e-01 -6.43445194e-01 1.14283967e+00 -1.77606612e-01 -3.89596075e-01 -6.34281814e-01 -4.32013422e-01 -7.78525174e-01 -9.69262660e-01 -1.37049913e-01 3.74746889e-01 -1.65225402e-01 -2.90155321e-01 3.34568977e-01 3.64478916e-01 -1.16163898e+00 -3.61514866e-01 1.36300933e+00 1.03488326e+00 7.89670870e-02 4.76163179e-01 8.68274644e-02 2.26585627e-01 -2.54166067e-01 -3.52442116e-01 3.72207433e-01 -4.82422292e-01 -6.61470175e-01 1.03151441e-01 5.05084336e-01 3.61330956e-01 2.03495529e-02 1.13395298e+00 3.83506417e-01 3.12172145e-01 6.28378332e-01 -1.06748796e+00 -3.42623651e-01 1.71392828e-01 -7.76206315e-01 1.83828682e-01 -1.14797473e-01 9.26017687e-02 1.12103844e+00 -1.14172494e+00 3.47374618e-01 9.18716967e-01 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455f2513-cece-45b3-9a09-4a01d3a15757	shearlet-based-detection-of-flame-fronts	1511.03753	null	http://arxiv.org/abs/1511.03753v2	http://arxiv.org/pdf/1511.03753v2.pdf	Shearlet-Based Detection of Flame Fronts	Identifying and characterizing flame fronts is the most common task in the computer-assisted analysis of data obtained from imaging techniques such as planar laser-induced fluorescence (PLIF), laser Rayleigh scattering (LRS), or particle imaging velocimetry (PIV). We present a novel edge and ridge (line) detection algorithm based on complex-valued wavelet-like analyzing functions -- so-called complex shearlets -- displaying several traits useful for the extraction of flame fronts. In addition to providing a unified approach to the detection of edges and ridges, our method inherently yields estimates of local tangent orientations and local curvatures. To examine the applicability for high-frequency recordings of combustion processes, the algorithm is applied to mock images distorted with varying degrees of noise and real-world PLIF images of both OH and CH radicals. Furthermore, we compare the performance of the newly proposed complex shearlet-based measure to well-established edge and ridge detection techniques such as the Canny edge detector, another shearlet-based edge detector, and the phase congruency measure.	['Rafael Reisenhofer', 'Johannes Kiefer', 'Emily J. King']	2015-11-12	null	null	null	null	['line-detection']	['computer-vision']	[ 2.84498423e-01 -7.40891278e-01 4.62369472e-01 2.38313004e-01 -5.72039545e-01 -6.21294439e-01 5.60332894e-01 5.19272387e-01 -6.10344589e-01 3.27752680e-01 -1.06890537e-01 -3.48327130e-01 -3.34921718e-01 -7.26987004e-01 -1.13148600e-01 -8.64378631e-01 -2.76827455e-01 1.43276319e-01 3.86730373e-01 -4.60490137e-02 5.96180737e-01 1.07420528e+00 -1.57056344e+00 -1.01164140e-01 5.27653396e-01 1.04239738e+00 5.66263460e-02 9.30590510e-01 5.62161990e-02 4.74929124e-01 -2.46502101e-01 1.35790080e-01 3.22446764e-01 -3.44440103e-01 -2.59928674e-01 6.43965183e-03 1.75839424e-01 8.33906513e-03 1.23496942e-01 9.27574277e-01 3.17899376e-01 4.81086105e-01 9.69413638e-01 -8.01244974e-01 -2.97498927e-02 -2.98410058e-01 -8.34289849e-01 5.55346251e-01 7.11938918e-01 1.87804490e-01 4.80067641e-01 -1.00788987e+00 8.06765378e-01 8.75966370e-01 8.41305435e-01 -1.47652086e-02 -1.02033162e+00 -2.62186617e-01 -8.10908258e-01 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88b4bd6a-2d2c-406f-9327-1389c0a28954	sequence-to-segment-networks-for-segment	null	null	http://papers.nips.cc/paper/7610-sequence-to-segment-networks-for-segment-detection	http://papers.nips.cc/paper/7610-sequence-to-segment-networks-for-segment-detection.pdf	Sequence-to-Segment Networks for Segment Detection	Detecting segments of interest from an input sequence is a challenging problem which often requires not only good knowledge of individual target segments, but also contextual understanding of the entire input sequence and the relationships between the target segments. To address this problem, we propose the Sequence-to-Segment Network (S$^2$N), a novel end-to-end sequential encoder-decoder architecture. S$^2$N first encodes the input into a sequence of hidden states that progressively capture both local and holistic information. It then employs a novel decoding architecture, called Segment Detection Unit (SDU), that integrates the decoder state and encoder hidden states to detect segments sequentially. During training, we formulate the assignment of predicted segments to ground truth as bipartite matching and use the Earth Mover's Distance to calculate the localization errors. We experiment with S$^2$N on temporal action proposal generation and video summarization and show that S$^2$N achieves state-of-the-art performance on both tasks.	['Minh Hoai Nguyen', 'Boyu Wang', 'Xiaohui Shen', 'Zijun Wei', 'Zhe Lin', 'Radomir Mech', 'Jianming Zhang', 'Dimitris Samaras']	2018-12-01	null	null	null	neurips-2018-12	['temporal-action-proposal-generation']	['computer-vision']	[ 9.71956551e-01 4.49844718e-01 -5.34653962e-01 -2.83123463e-01 -1.29477823e+00 -4.01851922e-01 3.06013525e-01 5.84615103e-04 -2.19805226e-01 5.85760534e-01 5.46971560e-01 4.31914255e-02 3.97069544e-01 -4.70979124e-01 -9.67502654e-01 -4.52613205e-01 -1.65531710e-01 2.99903363e-01 7.57134020e-01 -5.33947237e-02 3.91415298e-01 -4.41403911e-02 -1.26567936e+00 7.65720904e-01 5.93607247e-01 1.11604297e+00 5.71653724e-01 1.09454370e+00 1.89621329e-01 1.14176261e+00 -4.86919999e-01 -1.99670553e-01 1.39806509e-01 -1.17435932e+00 -1.02824652e+00 1.26282245e-01 2.34511942e-01 -4.58576918e-01 -6.50929272e-01 9.48447049e-01 5.55261016e-01 3.32425088e-01 5.61136425e-01 -8.47311676e-01 -2.13496909e-01 7.72441208e-01 -6.05119646e-01 4.52578992e-01 6.43916726e-01 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0c1ea6bb-fb56-4520-bb7a-97a383ffd2ca	weather-event-severity-prediction-using-buoy	1911.09001	null	https://arxiv.org/abs/1911.09001v1	https://arxiv.org/pdf/1911.09001v1.pdf	Weather event severity prediction using buoy data and machine learning	In this paper, we predict severity of extreme weather events (tropical storms, hurricanes, etc.) using buoy data time series variables such as wind speed and air temperature. The prediction/forecasting method is based on various forecasting and machine learning models. The following steps are used. Data sources for the buoys and weather events are identified, aggregated and merged. For missing data imputation, we use Kalman filters as well as splines for multivariate time series. Then, statistical tests are run to ascertain increasing trends in weather event severity. Next, we use machine learning to predict/forecast event severity using buoy variables, and report good accuracies for the models built.	['Vikas Ramachandra']	2019-11-17	null	null	null	null	['severity-prediction']	['computer-vision']	[-3.90748382e-01 -4.91622686e-01 -4.43352684e-02 -6.08487487e-01 -4.95944262e-01 -4.55460727e-01 5.46575665e-01 3.12283576e-01 -2.37178832e-01 1.29696953e+00 6.89720333e-01 -5.16356468e-01 -2.31274650e-01 -1.00227571e+00 -1.50278270e-01 -7.99545765e-01 -7.18208373e-01 1.84241518e-01 -2.84713507e-01 -2.78886676e-01 3.81417215e-01 5.05777240e-01 -1.34305978e+00 -7.09553808e-02 9.84926701e-01 6.42516613e-01 -1.85564816e-01 1.08448052e+00 -2.63538569e-01 8.78864884e-01 -6.32343292e-01 2.02518046e-01 2.35045135e-01 -1.33046985e-01 -1.35506406e-01 -6.50230169e-01 -1.23256035e-01 -3.90651286e-01 1.74927682e-01 4.48041886e-01 4.44488734e-01 3.14887881e-01 7.81743646e-01 -1.16569102e+00 -1.46511078e-01 4.24486935e-01 -5.68658650e-01 4.93594587e-01 4.54670668e-01 2.44108066e-01 3.92864853e-01 -7.55651355e-01 9.96185839e-02 8.57962012e-01 1.13108909e+00 4.24603559e-02 -1.04644322e+00 -9.01702762e-01 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a4ab005e-e22e-4855-a92b-8f41eaae62db	continual-density-ratio-estimation-cdre-a-new	null	null	https://openreview.net/forum?id=HJemQJBKDr	https://openreview.net/pdf?id=HJemQJBKDr	Continual Density Ratio Estimation (CDRE): A new method for evaluating generative models in continual learning	We propose a new method Continual Density Ratio Estimation (CDRE), which can estimate density ratios between a target distribution of real samples and a distribution of samples generated by a model while the model is changing over time and the data of the target distribution is not available after a certain time point. This method perfectly fits the setting of continual learning, in which one model is supposed to learn different tasks sequentially and the most crucial restriction is that model has none or very limited access to the data of all learned tasks. Through CDRE, we can evaluate generative models in continual learning using f-divergences. To the best of our knowledge, there is no existing method that can evaluate generative models under the setting of continual learning without storing real samples from the target distribution.	['Peter Flach', 'Tom Diethe', 'Song Liu', 'Yu Chen']	2019-09-25	null	null	null	null	['density-ratio-estimation']	['methodology']	[-1.70645118e-01 -1.88125297e-01 -1.85533971e-01 -3.00566792e-01 -7.06687808e-01 -5.30461967e-01 9.40687239e-01 5.34602441e-02 -6.33671224e-01 1.09456372e+00 -5.07579565e-01 -2.23843127e-01 -2.08683476e-01 -9.78824496e-01 -1.15269637e+00 -6.58182204e-01 1.94101725e-02 1.11624956e+00 2.54555434e-01 2.68913448e-01 -1.18174974e-03 1.71670094e-01 -1.55660701e+00 -2.31811590e-02 8.11082959e-01 8.58715475e-01 5.38271010e-01 6.62732303e-01 -3.38067412e-01 6.86725557e-01 -9.16070640e-01 -2.89056122e-01 1.08907372e-01 -3.72082442e-01 -5.35675585e-01 8.32428634e-02 2.01286405e-01 -3.11393350e-01 -2.56123245e-01 1.22630644e+00 2.82879919e-01 1.47648886e-01 1.14748383e+00 -1.64004385e+00 -7.85377264e-01 4.18736637e-01 -6.70292139e-01 1.80102348e-01 1.47232413e-01 -1.24113783e-02 3.59984964e-01 -8.26502144e-01 2.76208401e-01 1.15062773e+00 4.49123412e-01 4.89518374e-01 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38decfb6-0fa3-46e0-83b9-98137321e918	best-k-search-algorithm-for-neural-text	2211.11924	null	https://arxiv.org/abs/2211.11924v1	https://arxiv.org/pdf/2211.11924v1.pdf	Best-$k$ Search Algorithm for Neural Text Generation	Modern natural language generation paradigms require a good decoding strategy to obtain quality sequences out of the model. Beam search yields high-quality but low diversity outputs; stochastic approaches suffer from high variance and sometimes low quality, but the outputs tend to be more natural and creative. In this work, we propose a deterministic search algorithm balancing both quality and diversity. We first investigate the vanilla best-first search (BFS) algorithm and then propose the Best-$k$ Search algorithm. Inspired by BFS, we greedily expand the top $k$ nodes, instead of only the first node, to boost efficiency and diversity. Upweighting recently discovered nodes accompanied by heap pruning ensures the completeness of the search procedure. Experiments on four NLG tasks, including question generation, commonsense generation, text summarization, and translation, show that best-$k$ search yields more diverse and natural outputs compared to strong baselines, while our approach maintains high text quality. The proposed algorithm is parameter-free, lightweight, efficient, and easy to use.	['Yingbo Zhou', 'Silvio Savarese', 'Caiming Xiong', 'Jiacheng Xu']	2022-11-22	null	null	null	null	['question-generation']	['natural-language-processing']	[ 3.72633636e-01 2.35784277e-01 -2.64063269e-01 -2.11131409e-01 -1.14239192e+00 -6.34169400e-01 6.85586870e-01 1.90351367e-01 -3.92912716e-01 1.16708624e+00 5.95829666e-01 -2.30286658e-01 -4.19049561e-02 -9.58829999e-01 -7.06967354e-01 -6.46179557e-01 3.90612274e-01 5.76570392e-01 1.67137831e-01 -2.85897911e-01 6.38359070e-01 -3.40933353e-02 -1.50376523e+00 2.73213774e-01 1.39238966e+00 5.95002055e-01 5.13113260e-01 7.00088561e-01 -4.80626881e-01 5.55687606e-01 -7.68002987e-01 -6.35573328e-01 -3.25798653e-02 -9.57722068e-01 -1.05013430e+00 -3.56777877e-01 3.04262638e-01 -1.00094959e-01 7.97556862e-02 9.68697309e-01 8.37474227e-01 1.94637328e-01 5.65563381e-01 -1.01350629e+00 -6.99662507e-01 1.22535658e+00 -4.12351936e-01 1.59117758e-01 6.15463674e-01 2.83876359e-01 1.42071903e+00 -7.76290834e-01 9.00780857e-01 1.19497633e+00 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82d45837-303e-42ed-ace6-b3c463c1f78c	summarizing-source-code-using-a-neural	null	null	https://aclanthology.org/P16-1195	https://aclanthology.org/P16-1195.pdf	Summarizing Source Code using a Neural Attention Model	null	['Ioannis Konstas', 'Luke Zettlemoyer', 'Alvin Cheung', 'Srinivasan Iyer']	2016-08-01	null	null	null	acl-2016-8	['code-summarization']	['computer-code']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 -2.09758401e-01 9.22211111e-01 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0a6c94fc-69f4-4c70-8a43-d2900024d27f	explaining-link-predictions-in-knowledge	2212.02651	null	https://arxiv.org/abs/2212.02651v1	https://arxiv.org/pdf/2212.02651v1.pdf	Explaining Link Predictions in Knowledge Graph Embedding Models with Influential Examples	We study the problem of explaining link predictions in the Knowledge Graph Embedding (KGE) models. We propose an example-based approach that exploits the latent space representation of nodes and edges in a knowledge graph to explain predictions. We evaluated the importance of identified triples by observing progressing degradation of model performance upon influential triples removal. Our experiments demonstrate that this approach to generate explanations outperforms baselines on KGE models for two publicly available datasets.	['Luca Costabello', 'Adrianna Janik']	2022-12-05	null	null	null	null	['knowledge-graph-embedding']	['graphs']	[ 1.37918845e-01 1.41231298e+00 -1.03752625e+00 -2.83707410e-01 -2.15753645e-01 -1.07409045e-01 9.82200980e-01 3.47502202e-01 5.05531669e-01 1.10235703e+00 8.86894405e-01 -6.27608418e-01 -5.14180124e-01 -1.21553266e+00 -1.18550181e+00 1.27821192e-01 -3.99516612e-01 7.74813414e-01 2.31798083e-01 -3.33472043e-01 1.95658922e-01 2.77370457e-02 -1.10187304e+00 5.78101158e-01 8.69530857e-01 1.37462676e-01 -6.78486288e-01 6.49311423e-01 -3.25815469e-01 1.01131582e+00 -1.75652504e-01 -9.58347142e-01 -5.89901432e-02 -9.33477432e-02 -8.33085120e-01 -1.98432058e-01 5.16287744e-01 -1.47872488e-03 -9.05076802e-01 5.06600559e-01 -3.31926979e-02 -1.65124834e-01 1.01080465e+00 -1.90014505e+00 -1.45161521e+00 1.57186019e+00 -2.57892251e-01 3.45801443e-01 5.48830152e-01 -1.40247166e-01 1.77199447e+00 -1.01583862e+00 1.10566616e+00 1.32628107e+00 9.71012890e-01 4.48104262e-01 -1.63294077e+00 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dabd969a-289d-45cc-9329-55792106fb1a	local-information-assisted-attention-free	2201.03217	null	https://arxiv.org/abs/2201.03217v2	https://arxiv.org/pdf/2201.03217v2.pdf	Local Information Assisted Attention-free Decoder for Audio Captioning	Automated audio captioning aims to describe audio data with captions using natural language. Existing methods often employ an encoder-decoder structure, where the attention-based decoder (e.g., Transformer decoder) is widely used and achieves state-of-the-art performance. Although this method effectively captures global information within audio data via the self-attention mechanism, it may ignore the event with short time duration, due to its limitation in capturing local information in an audio signal, leading to inaccurate prediction of captions. To address this issue, we propose a method using the pretrained audio neural networks (PANNs) as the encoder and local information assisted attention-free Transformer (LocalAFT) as the decoder. The novelty of our method is in the proposal of the LocalAFT decoder, which allows local information within an audio signal to be captured while retaining the global information. This enables the events of different duration, including short duration, to be captured for more precise caption generation. Experiments show that our method outperforms the state-of-the-art methods in Task 6 of the DCASE 2021 Challenge with the standard attention-based decoder for caption generation.	['Haiyan Lan', 'Wenwu Wang', 'Qiaoxi Zhu', 'Jian Guan', 'Feiyang Xiao']	2022-01-10	null	null	null	null	['audio-captioning']	['audio']	[ 3.68471682e-01 1.48715362e-01 1.09453902e-01 -1.69391394e-01 -1.46089482e+00 -3.62511456e-01 4.52301472e-01 -6.55500814e-02 1.21989595e-02 8.95759463e-01 8.54873657e-01 1.74991652e-01 2.63656616e-01 -5.80135167e-01 -1.02233148e+00 -4.91904050e-01 2.03296110e-01 4.45391536e-01 2.79416829e-01 -1.85614586e-01 1.26640303e-02 -1.55197158e-01 -1.56956244e+00 6.87439084e-01 6.79693341e-01 1.17601728e+00 5.47697663e-01 6.27035677e-01 -3.07928562e-01 9.77875531e-01 -7.91721106e-01 -3.52701515e-01 -1.02717593e-01 -7.70884037e-01 -7.99839318e-01 -1.68537736e-01 2.61421204e-01 -4.08921868e-01 -4.73306656e-01 8.06387544e-01 8.65448534e-01 3.35792825e-02 3.72485995e-01 -1.30572462e+00 -7.96588719e-01 1.01202250e+00 -2.84086645e-01 2.16636792e-01 6.90299392e-01 2.42479034e-02 1.36706519e+00 -7.68943369e-01 2.97242641e-01 1.21830249e+00 5.26955903e-01 6.07507348e-01 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-1.36035931e+00 -3.99904341e-01 3.47708821e-01 7.77876616e-01 -4.74693924e-01 -4.25440133e-01 4.25669074e-01]	[15.25768756866455, 4.888449192047119]
9b37069e-cd83-4869-ace3-f0149f7edc11	transfer-learning-based-road-damage-detection	2008.13101	null	https://arxiv.org/abs/2008.13101v1	https://arxiv.org/pdf/2008.13101v1.pdf	Transfer Learning-based Road Damage Detection for Multiple Countries	Many municipalities and road authorities seek to implement automated evaluation of road damage. However, they often lack technology, know-how, and funds to afford state-of-the-art equipment for data collection and analysis of road damages. Although some countries, like Japan, have developed less expensive and readily available Smartphone-based methods for automatic road condition monitoring, other countries still struggle to find efficient solutions. This work makes the following contributions in this context. Firstly, it assesses the usability of the Japanese model for other countries. Secondly, it proposes a large-scale heterogeneous road damage dataset comprising 26620 images collected from multiple countries using smartphones. Thirdly, we propose generalized models capable of detecting and classifying road damages in more than one country. Lastly, we provide recommendations for readers, local agencies, and municipalities of other countries when one other country publishes its data and model for automatic road damage detection and classification. Our dataset is available at (https://github.com/sekilab/RoadDamageDetector/).	['Sanjay Kumar Ghosh', 'Yoshihide Sekimoto', 'Takehiro Kashiyama', 'Alexander Mraz', 'Hiroya Maeda', 'Deeksha Arya', 'Durga Toshniwal']	2020-08-30	null	null	null	null	['road-damage-detection']	['computer-vision']	[-2.23314300e-01 -1.35749783e-02 -3.84640902e-01 -3.20522673e-02 -1.10066879e+00 -3.03132236e-01 2.55513996e-01 1.84850633e-01 -3.64419669e-01 7.54881442e-01 4.52077359e-01 -7.08967030e-01 -1.04498766e-01 -1.30701041e+00 -2.57261604e-01 -5.40273130e-01 3.80199283e-01 -3.65781784e-02 3.15994054e-01 -1.01740927e-01 3.28140765e-01 6.11191511e-01 -1.63943553e+00 -2.89400309e-01 1.45249307e+00 4.34289664e-01 5.19869089e-01 5.33079982e-01 4.18040335e-01 4.28928077e-01 -2.74056345e-01 -3.10183823e-01 4.84280735e-02 2.14979738e-01 -9.20849025e-01 -1.92797378e-01 3.49265665e-01 -1.03279817e+00 -5.77924550e-01 7.80857325e-01 6.60670400e-01 -2.74507284e-01 7.67429292e-01 -1.06824553e+00 -5.56053102e-01 -1.14010595e-01 -3.32256347e-01 3.64183754e-01 4.33634579e-01 3.11351895e-01 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bcc43c3c-fceb-4371-b0ba-848e2a9b33de	mixspeech-cross-modality-self-learning-with	2303.05309	null	https://arxiv.org/abs/2303.05309v1	https://arxiv.org/pdf/2303.05309v1.pdf	MixSpeech: Cross-Modality Self-Learning with Audio-Visual Stream Mixup for Visual Speech Translation and Recognition	Multi-media communications facilitate global interaction among people. However, despite researchers exploring cross-lingual translation techniques such as machine translation and audio speech translation to overcome language barriers, there is still a shortage of cross-lingual studies on visual speech. This lack of research is mainly due to the absence of datasets containing visual speech and translated text pairs. In this paper, we present \textbf{AVMuST-TED}, the first dataset for \textbf{A}udio-\textbf{V}isual \textbf{Mu}ltilingual \textbf{S}peech \textbf{T}ranslation, derived from \textbf{TED} talks. Nonetheless, visual speech is not as distinguishable as audio speech, making it difficult to develop a mapping from source speech phonemes to the target language text. To address this issue, we propose MixSpeech, a cross-modality self-learning framework that utilizes audio speech to regularize the training of visual speech tasks. To further minimize the cross-modality gap and its impact on knowledge transfer, we suggest adopting mixed speech, which is created by interpolating audio and visual streams, along with a curriculum learning strategy to adjust the mixing ratio as needed. MixSpeech enhances speech translation in noisy environments, improving BLEU scores for four languages on AVMuST-TED by +1.4 to +4.2. Moreover, it achieves state-of-the-art performance in lip reading on CMLR (11.1\%), LRS2 (25.5\%), and LRS3 (28.0\%).	['Zhou Zhao', 'Aoxiong Yin', 'Ye Wang', 'Huangdai Liu', 'Zehan Wang', 'Wang Lin', 'Rongjie Huang', 'Tao Jin', 'Linjun Li', 'Xize Cheng']	2023-03-09	null	null	null	null	['self-learning']	['natural-language-processing']	[ 4.97092456e-02 -7.90845137e-03 -4.34368283e-01 -1.26358019e-02 -1.46607244e+00 -6.33095801e-01 5.69485366e-01 -1.31080106e-01 -2.94919193e-01 7.19731212e-01 5.16251326e-01 -6.11179352e-01 4.45817143e-01 -3.80689800e-01 -9.15568769e-01 -4.07971233e-01 6.78459644e-01 3.81610066e-01 -9.71977934e-02 -2.29167029e-01 -2.15899915e-01 -1.61498617e-02 -1.54705703e+00 7.73356140e-01 1.03355348e+00 7.62639463e-01 5.96985817e-01 6.36913598e-01 -5.06762981e-01 2.45355427e-01 -7.10845828e-01 -5.96031010e-01 -6.50050119e-02 -7.44039655e-01 -7.05980420e-01 -1.02368243e-01 6.33960843e-01 -2.50660479e-01 -2.78585970e-01 9.33115482e-01 1.00020385e+00 -5.02714776e-02 7.43978202e-01 -1.25768125e+00 -9.96583462e-01 6.59877419e-01 -5.40339887e-01 2.60004569e-02 6.84748769e-01 2.10019618e-01 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1273cc2e-bd5f-49e4-8f79-9cce5c84a96a	local-supports-global-deep-camera	1908.04391	null	https://arxiv.org/abs/1908.04391v1	https://arxiv.org/pdf/1908.04391v1.pdf	Local Supports Global: Deep Camera Relocalization with Sequence Enhancement	We propose to leverage the local information in image sequences to support global camera relocalization. In contrast to previous methods that regress global poses from single images, we exploit the spatial-temporal consistency in sequential images to alleviate uncertainty due to visual ambiguities by incorporating a visual odometry (VO) component. Specifically, we introduce two effective steps called content-augmented pose estimation and motion-based refinement. The content-augmentation step focuses on alleviating the uncertainty of pose estimation by augmenting the observation based on the co-visibility in local maps built by the VO stream. Besides, the motion-based refinement is formulated as a pose graph, where the camera poses are further optimized by adopting relative poses provided by the VO component as additional motion constraints. Thus, the global consistency can be guaranteed. Experiments on the public indoor 7-Scenes and outdoor Oxford RobotCar benchmark datasets demonstrate that benefited from local information inherent in the sequence, our approach outperforms state-of-the-art methods, especially in some challenging cases, e.g., insufficient texture, highly repetitive textures, similar appearances, and over-exposure.	['Zike Yan', 'Junqiu Wang', 'Hongbin Zha', 'Fei Xue', 'Qiuyuan Wang', 'Xin Wang']	2019-08-06	local-supports-global-deep-camera-1	http://openaccess.thecvf.com/content_ICCV_2019/html/Xue_Local_Supports_Global_Deep_Camera_Relocalization_With_Sequence_Enhancement_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Xue_Local_Supports_Global_Deep_Camera_Relocalization_With_Sequence_Enhancement_ICCV_2019_paper.pdf	iccv-2019-10	['camera-relocalization']	['computer-vision']	[ 2.20775321e-01 2.14404240e-02 -8.42771605e-02 -2.96786249e-01 -5.92572212e-01 -5.63941538e-01 5.44308662e-01 9.81979351e-03 -4.94330317e-01 6.93381727e-01 1.32647470e-01 1.94520026e-01 -7.23828897e-02 -6.36509240e-01 -9.72157240e-01 -7.12498665e-01 3.65479231e-01 2.99847692e-01 4.41686571e-01 -2.71055400e-01 2.41352737e-01 3.03034455e-01 -1.38605118e+00 -4.09756660e-01 1.04246509e+00 1.04750168e+00 7.28045404e-01 3.63366753e-01 2.06303477e-01 8.56862366e-01 -6.92632562e-03 7.26986378e-02 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77746e9a-2887-4a88-9170-1fef6ce4a2e0	ladder-variational-autoencoders	1602.02282	null	http://arxiv.org/abs/1602.02282v3	http://arxiv.org/pdf/1602.02282v3.pdf	Ladder Variational Autoencoders	Variational Autoencoders are powerful models for unsupervised learning. However deep models with several layers of dependent stochastic variables are difficult to train which limits the improvements obtained using these highly expressive models. We propose a new inference model, the Ladder Variational Autoencoder, that recursively corrects the generative distribution by a data dependent approximate likelihood in a process resembling the recently proposed Ladder Network. We show that this model provides state of the art predictive log-likelihood and tighter log-likelihood lower bound compared to the purely bottom-up inference in layered Variational Autoencoders and other generative models. We provide a detailed analysis of the learned hierarchical latent representation and show that our new inference model is qualitatively different and utilizes a deeper more distributed hierarchy of latent variables. Finally, we observe that batch normalization and deterministic warm-up (gradually turning on the KL-term) are crucial for training variational models with many stochastic layers.	['Søren Kaae Sønderby', 'Lars Maaløe', 'Casper Kaae Sønderby', 'Tapani Raiko', 'Ole Winther']	2016-02-06	ladder-variational-autoencoders-1	http://papers.nips.cc/paper/6275-ladder-variational-autoencoders	http://papers.nips.cc/paper/6275-ladder-variational-autoencoders.pdf	neurips-2016-12	['unsupervised-mnist']	['methodology']	[-2.76226997e-01 4.72262233e-01 -6.63290322e-02 -4.44496363e-01 -8.06257188e-01 -4.50775415e-01 1.00143397e+00 -3.46288979e-01 -7.39252567e-02 8.87271345e-01 4.79301035e-01 -1.74013808e-01 -2.38123491e-01 -8.98915291e-01 -1.02370286e+00 -1.01099229e+00 1.64120063e-01 1.11470783e+00 9.96262878e-02 7.57393390e-02 -2.24313349e-01 2.71189451e-01 -1.44073701e+00 3.47936563e-02 5.06618083e-01 6.30661070e-01 1.50845394e-01 9.25649881e-01 -5.27699590e-02 9.89147782e-01 -3.44306469e-01 -6.19130194e-01 -1.80791095e-01 -5.04486918e-01 -5.75571537e-01 -6.12749942e-02 2.38158792e-01 -7.29658663e-01 -2.84113050e-01 8.44614565e-01 2.37077668e-01 4.28595811e-01 1.29142904e+00 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7b9c7352-e23f-438f-8e25-85d215bd6af2	model-agnostic-explainability-for-visual	2103.00370	null	https://arxiv.org/abs/2103.00370v3	https://arxiv.org/pdf/2103.00370v3.pdf	Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning	Visual search, recommendation, and contrastive similarity learning power technologies that impact billions of users worldwide. Modern model architectures can be complex and difficult to interpret, and there are several competing techniques one can use to explain a search engine's behavior. We show that the theory of fair credit assignment provides a $\textit{unique}$ axiomatic solution that generalizes several existing recommendation- and metric-explainability techniques in the literature. Using this formalism, we show when existing approaches violate "fairness" and derive methods that sidestep these shortcomings and naturally handle counterfactual information. More specifically, we show existing approaches implicitly approximate second-order Shapley-Taylor indices and extend CAM, GradCAM, LIME, SHAP, SBSM, and other methods to search engines. These extensions can extract pairwise correspondences between images from trained $\textit{opaque-box}$ models. We also introduce a fast kernel-based method for estimating Shapley-Taylor indices that require orders of magnitude fewer function evaluations to converge. Finally, we show that these game-theoretic measures yield more consistent explanations for image similarity architectures.	['William T. Freeman', 'Stephanie Fu', 'Lei Zhang', 'Scott Lundberg', 'Mark Hamilton']	2021-02-28	axiomatic-explanations-for-visual-search	https://openreview.net/forum?id=TqNsv1TuCX9	https://openreview.net/pdf?id=TqNsv1TuCX9	iclr-2022-4	['image-similarity-search']	['computer-vision']	[-6.86913356e-02 1.72756478e-01 -5.10793805e-01 -4.62187052e-01 -5.86070418e-01 -8.32366645e-01 7.30551243e-01 -1.80399269e-01 -3.38338315e-01 4.61572737e-01 7.14100385e-03 -8.64540279e-01 -6.30503178e-01 -3.32169384e-01 -6.14459872e-01 -1.79865211e-01 6.79693148e-02 5.52505016e-01 -8.97838473e-02 -2.17780992e-01 8.31134140e-01 3.21831614e-01 -1.77676058e+00 3.22296590e-01 8.46610844e-01 1.04291272e+00 9.40938219e-02 9.10269439e-01 -2.05375314e-01 1.13313150e+00 -1.95823550e-01 -1.17724526e+00 7.14529693e-01 -5.87181866e-01 -9.77844775e-01 -4.61828291e-01 9.95006680e-01 -5.53893745e-01 -5.56160450e-01 1.08578360e+00 1.28408343e-01 2.25638404e-01 9.38171506e-01 -1.76749885e+00 -1.63980818e+00 5.03594697e-01 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62a2d27b-1538-4e11-86a2-038492de4116	so-different-yet-so-alike-constrained-1	2205.04093	null	https://arxiv.org/abs/2205.04093v1	https://arxiv.org/pdf/2205.04093v1.pdf	So Different Yet So Alike! Constrained Unsupervised Text Style Transfer	Automatic transfer of text between domains has become popular in recent times. One of its aims is to preserve the semantic content of text being translated from source to target domain. However, it does not explicitly maintain other attributes between the source and translated text, for e.g., text length and descriptiveness. Maintaining constraints in transfer has several downstream applications, including data augmentation and de-biasing. We introduce a method for such constrained unsupervised text style transfer by introducing two complementary losses to the generative adversarial network (GAN) family of models. Unlike the competing losses used in GANs, we introduce cooperative losses where the discriminator and the generator cooperate and reduce the same loss. The first is a contrastive loss and the second is a classification loss, aiming to regularize the latent space further and bring similar sentences across domains closer together. We demonstrate that such training retains lexical, syntactic, and domain-specific constraints between domains for multiple benchmark datasets, including ones where more than one attribute change. We show that the complementary cooperative losses improve text quality, according to both automated and human evaluation measures.	['Soujanya Poria', 'Roger Zimmermann', 'Min-Yen Kan', 'Devamanyu Hazarika', 'Abhinav Ramesh Kashyap']	2022-05-09	null	https://aclanthology.org/2022.acl-long.32	https://aclanthology.org/2022.acl-long.32.pdf	acl-2022-5	['text-style-transfoer']	['natural-language-processing']	[ 6.00776494e-01 3.35024893e-01 -2.09652483e-01 -5.66115737e-01 -8.75284374e-01 -9.51888502e-01 7.83743799e-01 1.93804353e-01 -4.49491501e-01 1.17343807e+00 3.15793782e-01 2.81796139e-03 2.98989773e-01 -8.33538353e-01 -8.27539742e-01 -7.48307824e-01 5.87605298e-01 6.69314206e-01 2.23075002e-02 -3.64488691e-01 -1.41150489e-01 2.32880950e-01 -8.27486277e-01 3.19372147e-01 1.22713900e+00 8.17644000e-01 1.49296433e-01 1.36750445e-01 -2.67246157e-01 6.53578758e-01 -7.05408573e-01 -9.09562171e-01 2.65472710e-01 -8.60026956e-01 -7.66629040e-01 6.55281395e-02 2.44799420e-01 -3.26896757e-02 -3.89266193e-01 1.10314119e+00 5.13726413e-01 -1.05671398e-02 8.84096742e-01 -1.40524721e+00 -9.79156911e-01 6.79685831e-01 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220a74de-adf9-4f4f-87ee-e63620fae665	semantic-slam-with-autonomous-object-level	2011.10625	null	https://arxiv.org/abs/2011.10625v1	https://arxiv.org/pdf/2011.10625v1.pdf	Semantic SLAM with Autonomous Object-Level Data Association	It is often desirable to capture and map semantic information of an environment during simultaneous localization and mapping (SLAM). Such semantic information can enable a robot to better distinguish places with similar low-level geometric and visual features and perform high-level tasks that use semantic information about objects to be manipulated and environments to be navigated. While semantic SLAM has gained increasing attention, there is little research on semanticlevel data association based on semantic objects, i.e., object-level data association. In this paper, we propose a novel object-level data association algorithm based on bag of words algorithm, formulated as a maximum weighted bipartite matching problem. With object-level data association solved, we develop a quadratic-programming-based semantic object initialization scheme using dual quadric and introduce additional constraints to improve the success rate of object initialization. The integrated semantic-level SLAM system can achieve high-accuracy object-level data association and real-time semantic mapping as demonstrated in the experiments. The online semantic map building and semantic-level localization capabilities facilitate semantic-level mapping and task planning in a priori unknown environment.	['Jing Xiao', 'Jie Fu', 'Kartik Patath', 'Zhentian Qian']	2020-11-20	null	null	null	null	['semantic-slam']	['computer-vision']	[ 8.27027038e-02 -2.37714916e-01 -2.61342019e-01 -7.45575666e-01 -6.92419469e-01 -3.33618522e-01 3.98137867e-01 5.68039834e-01 -5.95162868e-01 3.80080462e-01 -1.68485522e-01 2.86604203e-02 -5.97456634e-01 -9.10273373e-01 -7.31006980e-01 -3.15088421e-01 -1.24032222e-01 1.11557579e+00 4.21087056e-01 -2.80403733e-01 5.77072561e-01 7.29150832e-01 -1.82352209e+00 -2.66746730e-01 1.06466949e+00 7.96521842e-01 1.19963133e+00 1.70873538e-01 -3.73113245e-01 1.92714795e-01 -1.41869187e-01 2.59064347e-01 4.39630121e-01 -7.58907720e-02 -8.46335769e-01 1.27277836e-01 2.33375728e-01 1.57648757e-01 3.02135828e-03 1.37470770e+00 2.28197739e-01 6.50988042e-01 3.20335031e-01 -1.68381214e+00 -3.73373151e-01 -3.93267497e-02 -4.82462466e-01 -1.38536498e-01 3.57699305e-01 -1.88728124e-01 8.55309606e-01 -8.43466938e-01 3.91605526e-01 1.53814805e+00 3.04500878e-01 -1.80504583e-02 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-6.86785996e-01 3.75230879e-01 1.30232602e-01 -1.32179642e+00 -4.45421562e-02 -2.66071737e-01]	[7.287022113800049, -2.2199277877807617]
e7955d27-b830-4d5d-9018-1a7447305db9	pointcontrast-unsupervised-pre-training-for	2007.10985	null	https://arxiv.org/abs/2007.10985v3	https://arxiv.org/pdf/2007.10985v3.pdf	PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding	Arguably one of the top success stories of deep learning is transfer learning. The finding that pre-training a network on a rich source set (eg., ImageNet) can help boost performance once fine-tuned on a usually much smaller target set, has been instrumental to many applications in language and vision. Yet, very little is known about its usefulness in 3D point cloud understanding. We see this as an opportunity considering the effort required for annotating data in 3D. In this work, we aim at facilitating research on 3D representation learning. Different from previous works, we focus on high-level scene understanding tasks. To this end, we select a suite of diverse datasets and tasks to measure the effect of unsupervised pre-training on a large source set of 3D scenes. Our findings are extremely encouraging: using a unified triplet of architecture, source dataset, and contrastive loss for pre-training, we achieve improvement over recent best results in segmentation and detection across 6 different benchmarks for indoor and outdoor, real and synthetic datasets -- demonstrating that the learned representation can generalize across domains. Furthermore, the improvement was similar to supervised pre-training, suggesting that future efforts should favor scaling data collection over more detailed annotation. We hope these findings will encourage more research on unsupervised pretext task design for 3D deep learning.	['Or Litany', 'Jiatao Gu', 'Saining Xie', 'Leonidas J. Guibas', 'Demi Guo', 'Charles R. Qi']	2020-07-21	null	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/893_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480579.pdf	eccv-2020-8	['point-cloud-pre-training']	['computer-vision']	[ 2.33393565e-01 6.57057613e-02 -1.38835341e-01 -7.84800351e-01 -9.18534696e-01 -8.17023575e-01 7.88486958e-01 7.01613501e-02 -5.82409859e-01 3.57294679e-01 1.80626094e-01 -5.59609771e-01 1.55113742e-01 -6.50237143e-01 -9.91433501e-01 -2.97664285e-01 -1.31518513e-01 6.24075055e-01 2.08491698e-01 -1.84802815e-01 2.10129678e-01 8.05440366e-01 -1.29136372e+00 1.61030844e-01 5.66213906e-01 7.58210003e-01 2.75049210e-01 4.54168290e-01 -9.39207003e-02 5.39989054e-01 -5.48539698e-01 -1.57455131e-01 5.15860677e-01 -9.47479755e-02 -1.04557049e+00 2.82314539e-01 7.81533897e-01 -3.59869480e-01 -3.05114016e-02 6.88229918e-01 6.80578887e-01 1.83769628e-01 8.58520210e-01 -9.29145634e-01 -6.77208245e-01 1.02766134e-01 -6.13387406e-01 2.95538723e-01 1.28157765e-01 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dc83cadf-61bd-4a85-acb6-21eced201820	bbam-bounding-box-attribution-map-for-weakly	2103.08907	null	https://arxiv.org/abs/2103.08907v1	https://arxiv.org/pdf/2103.08907v1.pdf	BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation	Weakly supervised segmentation methods using bounding box annotations focus on obtaining a pixel-level mask from each box containing an object. Existing methods typically depend on a class-agnostic mask generator, which operates on the low-level information intrinsic to an image. In this work, we utilize higher-level information from the behavior of a trained object detector, by seeking the smallest areas of the image from which the object detector produces almost the same result as it does from the whole image. These areas constitute a bounding-box attribution map (BBAM), which identifies the target object in its bounding box and thus serves as pseudo ground-truth for weakly supervised semantic and instance segmentation. This approach significantly outperforms recent comparable techniques on both the PASCAL VOC and MS COCO benchmarks in weakly supervised semantic and instance segmentation. In addition, we provide a detailed analysis of our method, offering deeper insight into the behavior of the BBAM.	['Sungroh Yoon', 'Chaehun Shin', 'Jihun Yi', 'Jungbeom Lee']	2021-03-16	null	http://openaccess.thecvf.com//content/CVPR2021/html/Lee_BBAM_Bounding_Box_Attribution_Map_for_Weakly_Supervised_Semantic_and_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Lee_BBAM_Bounding_Box_Attribution_Map_for_Weakly_Supervised_Semantic_and_CVPR_2021_paper.pdf	cvpr-2021-1	['box-supervised-instance-segmentation', 'weakly-supervised-instance-segmentation']	['computer-vision', 'computer-vision']	[ 5.85278034e-01 5.47005177e-01 -4.05313998e-01 -5.19614398e-01 -1.00109649e+00 -8.53614330e-01 5.05694449e-01 3.01847547e-01 -3.77906293e-01 4.82580364e-01 -2.29322925e-01 5.78238852e-02 5.02697647e-01 -6.53826416e-01 -1.11888194e+00 -7.67111301e-01 3.26723844e-01 5.49741328e-01 8.08145761e-01 1.40831873e-01 1.09134629e-01 3.71078372e-01 -1.27661610e+00 6.70626462e-01 6.03830040e-01 1.09692681e+00 2.53197134e-01 4.77248967e-01 -2.38640592e-01 6.54720426e-01 -8.67847383e-01 -3.17311823e-01 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cbe1d97e-8104-45a0-b5f0-d3a27b8a9fe3	uniter-learning-universal-image-text	null	null	https://openreview.net/forum?id=S1eL4kBYwr	https://openreview.net/pdf?id=S1eL4kBYwr	UNITER: Learning UNiversal Image-TExt Representations	Joint image-text embedding is the bedrock for most Vision-and-Language (V+L) tasks, where multimodality inputs are jointly processed for visual and textual understanding. In this paper, we introduce UNITER, a UNiversal Image-TExt Representation, learned through large-scale pre-training over four image-text datasets (COCO, Visual Genome, Conceptual Captions, and SBU Captions), which can power heterogeneous downstream V+L tasks with joint multimodal embeddings. We design three pre-training tasks: Masked Language Modeling (MLM), Image-Text Matching (ITM), and Masked Region Modeling (MRM, with three variants). Different from concurrent work on multimodal pre-training that apply joint random masking to both modalities, we use Conditioned Masking on pre-training tasks (i.e., masked language/region modeling is conditioned on full observation of image/text). Comprehensive analysis shows that conditioned masking yields better performance than unconditioned masking. We also conduct a thorough ablation study to find an optimal combination of pre-training tasks for UNITER. Extensive experiments show that UNITER achieves new state of the art across six V+L tasks over nine datasets, including Visual Question Answering, Image-Text Retrieval, Referring Expression Comprehension, Visual Commonsense Reasoning, Visual Entailment, and NLVR2.	['Jingjing Liu', 'Yu Cheng', 'Zhe Gan', 'Faisal Ahmed', 'Ahmed El Kholy', 'Licheng Yu', 'Linjie Li', 'Yen-Chun Chen']	2019-09-25	null	null	null	null	['visual-commonsense-reasoning', 'visual-entailment']	['reasoning', 'reasoning']	[ 5.52576423e-01 2.58062214e-01 -4.25845057e-01 -4.13401812e-01 -9.45715725e-01 -7.40973234e-01 9.96084452e-01 5.54591119e-02 -5.40591180e-01 2.11190701e-01 5.16140759e-01 -8.40698421e-01 5.17758548e-01 -3.19557756e-01 -1.23389435e+00 -2.10523278e-01 4.89603490e-01 3.06737453e-01 -3.01390022e-01 -1.14283323e-01 1.73426986e-01 1.52493984e-01 -1.26561308e+00 1.10674691e+00 6.12989604e-01 1.11369145e+00 3.85573626e-01 9.26431537e-01 -3.77495766e-01 1.32518518e+00 -3.19584101e-01 -8.06221843e-01 -1.32183343e-01 -5.03212452e-01 -8.94892991e-01 1.23474628e-01 9.39917922e-01 -5.08086801e-01 -6.50449097e-01 8.25293064e-01 3.14375550e-01 -8.88916329e-02 9.92910147e-01 -1.56044424e+00 -1.66301060e+00 5.88096380e-01 -6.78054154e-01 6.77396953e-02 5.34842968e-01 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c3897179-6727-49dd-a0fe-925af8153d94	wind-park-power-prediction-attention-based	2201.03229	null	https://arxiv.org/abs/2201.03229v2	https://arxiv.org/pdf/2201.03229v2.pdf	Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses	With the increased penetration of wind energy into the power grid, it has become increasingly important to be able to predict the expected power production for larger wind farms. Deep learning (DL) models can learn complex patterns in the data and have found wide success in predicting wake losses and expected power production. This paper proposes a modular framework for attention-based graph neural networks (GNN), where attention can be applied to any desired component of a graph block. The results show that the model significantly outperforms a multilayer perceptron (MLP) and a bidirectional LSTM (BLSTM) model, while delivering performance on-par with a vanilla GNN model. Moreover, we argue that the proposed graph attention architecture can easily adapt to different applications by offering flexibility into the desired attention operations to be used, which might depend on the specific application. Through analysis of the attention weights, it was showed that employing attention-based GNNs can provide insights into what the models learn. In particular, the attention networks seemed to realise turbine dependencies that aligned with some physical intuition about wake losses.	['Paal Engelstad', 'Roy Stenbro', 'Narada Dilp Warakagoda', 'Lars Ødegaard Bentsen']	2022-01-10	null	null	null	null	['physical-intuition']	['reasoning']	[-2.73078769e-01 3.31900418e-01 1.61155432e-01 -1.13688715e-01 5.44644237e-01 -3.87560129e-01 4.14121389e-01 2.87413180e-01 1.48380876e-01 5.04738390e-01 1.08657748e-01 -8.83754313e-01 -5.07769108e-01 -1.10103953e+00 -3.77204686e-01 -7.64420271e-01 -6.77579403e-01 1.42602697e-01 -1.99357718e-01 -5.95848382e-01 -9.99623723e-03 8.57027054e-01 -1.26776183e+00 -1.07392855e-01 5.86088300e-01 8.80684197e-01 5.45732021e-01 1.02193975e+00 8.19056481e-02 1.31015909e+00 -7.07514107e-01 -1.71739861e-01 -1.03211544e-01 -4.56399083e-01 -7.37861335e-01 -4.55779701e-01 -5.29814661e-02 8.45871773e-03 -1.81875616e-01 8.63960803e-01 4.95522171e-01 3.39362562e-01 3.91563445e-01 -1.22424757e+00 -9.20462847e-01 7.37293959e-01 -1.34789303e-01 7.58514106e-01 -2.20990717e-01 1.16360269e-01 1.35613775e+00 -5.26321828e-01 2.16298312e-01 9.08097565e-01 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6c5106df-2f11-4761-a8c7-78ae4ac47bb4	adaptively-topological-tensor-network-for	2305.00716	null	https://arxiv.org/abs/2305.00716v1	https://arxiv.org/pdf/2305.00716v1.pdf	Adaptively Topological Tensor Network for Multi-view Subspace Clustering	Multi-view subspace clustering methods have employed learned self-representation tensors from different tensor decompositions to exploit low rank information. However, the data structures embedded with self-representation tensors may vary in different multi-view datasets. Therefore, a pre-defined tensor decomposition may not fully exploit low rank information for a certain dataset, resulting in sub-optimal multi-view clustering performance. To alleviate the aforementioned limitations, we propose the adaptively topological tensor network (ATTN) by determining the edge ranks from the structural information of the self-representation tensor, and it can give a better tensor representation with the data-driven strategy. Specifically, in multi-view tensor clustering, we analyze the higher-order correlations among different modes of a self-representation tensor, and prune the links of the weakly correlated ones from a fully connected tensor network. Therefore, the newly obtained tensor networks can efficiently explore the essential clustering information with self-representation with different tensor structures for various datasets. A greedy adaptive rank-increasing strategy is further applied to improve the capture capacity of low rank structure. We apply ATTN on multi-view subspace clustering and utilize the alternating direction method of multipliers to solve it. Experimental results show that multi-view subspace clustering based on ATTN outperforms the counterparts on six multi-view datasets.	['Ce Zhu', 'Zhen Long', 'Weiting Ou', 'Yingcong Lu', 'Yipeng Liu']	2023-05-01	null	null	null	null	['multi-view-subspace-clustering', 'tensor-networks']	['computer-vision', 'methodology']	[-5.20890355e-01 -5.59178889e-01 -2.70960748e-01 1.42929226e-01 -2.17532471e-01 -8.29903722e-01 1.05339564e-01 -4.93392229e-01 8.98996890e-02 -2.61351140e-03 6.70144081e-01 5.60538657e-03 -8.57168555e-01 -5.28988063e-01 -1.51565701e-01 -1.09841573e+00 -2.69502610e-01 4.81075227e-01 2.30034105e-02 -2.30833367e-01 2.57450402e-01 1.81864992e-01 -1.18374312e+00 6.05355263e-01 9.20596659e-01 5.72175205e-01 1.19623795e-01 2.88332164e-01 -2.28309959e-01 4.15283382e-01 6.66842237e-02 6.79282546e-02 6.79982305e-02 -2.20534071e-01 -7.59442449e-01 5.69159627e-01 1.21614583e-01 2.29334868e-02 -3.19276959e-01 1.13407946e+00 2.53048539e-01 9.05544609e-02 4.01182741e-01 -1.30840528e+00 -3.45780194e-01 6.66068971e-01 -1.01961792e+00 2.36559272e-01 2.63725281e-01 -4.28694561e-02 1.27067184e+00 -1.19653046e+00 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c5cd2480-ef56-4b76-81cb-f2afd5997c92	parallel-corpus-for-japanese-spoken-to	null	null	https://aclanthology.org/2020.lrec-1.779	https://aclanthology.org/2020.lrec-1.779.pdf	Parallel Corpus for Japanese Spoken-to-Written Style Conversion	With the increase of automatic speech recognition (ASR) applications, spoken-to-written style conversion that transforms spoken-style text into written-style text is becoming an important technology to increase the readability of ASR transcriptions. To establish such conversion technology, a parallel corpus of spoken-style text and written-style text is beneficial because it can be utilized for building end-to-end neural sequence transformation models. Spoken-to-written style conversion involves multiple conversion problems including punctuation restoration, disfluency detection, and simplification. However, most existing corpora tend to be made for just one of these conversion problems. In addition, in Japanese, we have to consider not only general spoken-to-written style conversion problems but also Japanese-specific ones, such as language style unification (e.g., polite, frank, and direct styles) and omitted postpositional particle expressions restoration. Therefore, we created a new Japanese parallel corpus of spoken-style text and written-style text that can simultaneously handle general problems and Japanese-specific ones. To make this corpus, we prepared four types of spoken-style text and utilized a crowdsourcing service for manually converting them into written-style text. This paper describes the building setup of this corpus and reports the baseline results of spoken-to-written style conversion using the latest neural sequence transformation models.	['Mana Ihori', 'Ryo Masumura', 'Akihiko Takashima']	2020-05-01	null	null	null	lrec-2020-5	['punctuation-restoration']	['natural-language-processing']	[ 0.35037115 -0.14209086 0.27439785 -0.61243296 -0.8230863 -0.620573 0.48582184 -0.45979816 -0.57148737 0.8594566 0.38605642 -0.49991798 0.60948217 -0.49567205 -0.31802934 -0.49733898 0.93876255 0.76273054 0.22720502 -0.74847424 -0.03188023 0.21850362 -0.9767748 0.40201765 1.0864766 0.40611702 0.7455586 0.7071344 -0.46586576 0.45534915 -0.9802808 -0.35445803 0.09534761 -0.8600799 -0.83322704 0.09925161 0.016112 -0.18100213 -0.25907168 1.1557902 0.5322798 0.325695 0.57933384 -0.8445405 -1.0279241 0.74977714 -0.09366695 0.0233725 0.4890583 0.04863261 0.6917349 -0.95044243 0.6321353 1.3823199 0.18366186 1.1071026 -1.1550869 -0.3421345 0.05976877 -0.08318564 -1.3989488 -0.73313737 0.660756 -0.19609246 1.228677 0.55702627 0.38816398 1.1755129 -0.1102713 0.8180619 1.1988082 -0.8022235 0.09654735 0.38669395 0.2690837 0.18798557 -0.31747675 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62cced47-b6de-4ad6-a60d-ec8e7f4b23ce	hauser-towards-holistic-and-automatic	2306.07554	null	https://arxiv.org/abs/2306.07554v1	https://arxiv.org/pdf/2306.07554v1.pdf	HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation	Similes play an imperative role in creative writing such as story and dialogue generation. Proper evaluation metrics are like a beacon guiding the research of simile generation (SG). However, it remains under-explored as to what criteria should be considered, how to quantify each criterion into metrics, and whether the metrics are effective for comprehensive, efficient, and reliable SG evaluation. To address the issues, we establish HAUSER, a holistic and automatic evaluation system for the SG task, which consists of five criteria from three perspectives and automatic metrics for each criterion. Through extensive experiments, we verify that our metrics are significantly more correlated with human ratings from each perspective compared with prior automatic metrics.	['Yunwen Chen', 'Yanghua Xiao', 'Yuncheng Huang', 'Jiaqing Liang', 'Yikai Zhang', 'Qianyu He']	2023-06-13	null	null	null	null	['dialogue-generation', 'dialogue-generation']	['natural-language-processing', 'speech']	[-2.99898237e-02 -4.36754785e-02 -1.89834028e-01 -1.64896578e-01 -5.92200696e-01 -6.59409881e-01 1.00021255e+00 -1.17284417e-01 -1.12483919e-01 7.32624114e-01 7.99807131e-01 1.81449771e-01 -1.49553329e-01 -5.95443189e-01 5.54720759e-02 -2.38751188e-01 5.26710868e-01 3.61313879e-01 3.77670266e-02 -6.16051972e-01 6.02200270e-01 2.93177404e-02 -1.36183095e+00 3.96130681e-01 1.30416763e+00 7.79044628e-01 6.98936284e-02 3.98248792e-01 -3.09848964e-01 1.14490306e+00 -1.16509533e+00 -6.78924143e-01 -3.11432064e-01 -1.10825181e+00 -1.16615975e+00 1.71652734e-01 -1.53443083e-01 -1.29248068e-01 1.35755450e-01 9.55996811e-01 5.77765286e-01 -8.44250396e-02 8.64856243e-01 -1.07151663e+00 -1.07310688e+00 1.05608499e+00 1.63430408e-01 -1.46199623e-02 8.73630285e-01 7.88498223e-02 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1.15696895e+00 -6.95942864e-02 3.22401226e-01 8.93831253e-01 -4.05211926e-01 -6.78529561e-01 -1.44336998e+00 -5.58796704e-01 4.50844407e-01 -3.78539205e-01 -5.74781656e-01 -4.72085178e-01 -7.65566230e-02]	[11.638143539428711, 9.020976066589355]
05e29030-ef2d-482f-b47d-a9a26028c563	semantic-instance-segmentation-via-deep	1703.10277	null	http://arxiv.org/abs/1703.10277v1	http://arxiv.org/pdf/1703.10277v1.pdf	Semantic Instance Segmentation via Deep Metric Learning	We propose a new method for semantic instance segmentation, by first computing how likely two pixels are to belong to the same object, and then by grouping similar pixels together. Our similarity metric is based on a deep, fully convolutional embedding model. Our grouping method is based on selecting all points that are sufficiently similar to a set of "seed points", chosen from a deep, fully convolutional scoring model. We show competitive results on the Pascal VOC instance segmentation benchmark.	['Sergio Guadarrama', 'Vivek Rathod', 'Peng Wang', 'Kevin P. Murphy', 'Hyun Oh Song', 'Zbigniew Wojna', 'Alireza Fathi']	2017-03-30	null	null	null	null	['object-proposal-generation']	['computer-vision']	[ 3.42112511e-01 2.19340488e-01 -1.01091936e-01 -7.85340190e-01 -1.01340437e+00 -5.68788946e-01 3.83131057e-01 4.39681113e-01 -7.28875816e-01 2.57052869e-01 -7.98290689e-03 3.06670368e-01 5.97326383e-02 -9.07025576e-01 -8.88161659e-01 -4.47627276e-01 5.71905822e-03 7.88163185e-01 7.20645487e-01 1.34933352e-01 3.76456320e-01 5.22610545e-01 -1.41748822e+00 4.26639408e-01 5.62339067e-01 9.81590986e-01 2.79009849e-01 6.28130913e-01 -2.24440232e-01 2.35290200e-01 -7.06258178e-01 -3.94448072e-01 2.90988952e-01 -2.90835291e-01 -1.35929751e+00 1.27649814e-01 8.29872847e-01 -2.52644628e-01 2.09440496e-02 1.17610025e+00 2.36160949e-01 3.55814904e-01 8.13459456e-01 -8.87743831e-01 -4.81465280e-01 4.38311398e-01 -4.52517658e-01 6.95228130e-02 2.79428065e-01 -1.33274615e-01 1.58154035e+00 -7.44327247e-01 8.84150505e-01 1.15837014e+00 7.62628734e-01 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b4e17473-3934-4668-b962-2ec03df0d188	empirical-evaluation-of-gated-recurrent	1412.3555	null	http://arxiv.org/abs/1412.3555v1	http://arxiv.org/pdf/1412.3555v1.pdf	Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling	In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU). We evaluate these recurrent units on the tasks of polyphonic music modeling and speech signal modeling. Our experiments revealed that these advanced recurrent units are indeed better than more traditional recurrent units such as tanh units. Also, we found GRU to be comparable to LSTM.	['Kyunghyun Cho', 'Junyoung Chung', 'Yoshua Bengio', 'Caglar Gulcehre']	2014-12-11	null	null	null	null	['music-modeling']	['music']	[ 1.39781728e-01 1.24629416e-01 -1.27552211e-01 -7.47998990e-03 -3.14624697e-01 -7.26069212e-02 5.57376444e-01 -4.90649909e-01 -4.31992710e-01 7.48238325e-01 6.53254330e-01 -5.64819932e-01 4.66264009e-01 -7.32667208e-01 -6.36413813e-01 -6.54380083e-01 -1.23882554e-01 -3.25061560e-01 1.47084817e-01 -3.38693053e-01 3.13358635e-01 4.07515198e-01 -1.25934196e+00 5.57876468e-01 2.58020550e-01 6.38094664e-01 3.84317338e-01 8.92069519e-01 -1.65367350e-01 1.16523743e+00 -6.53371394e-01 2.33772784e-01 -3.91830206e-01 -6.29317999e-01 -5.64874470e-01 -5.61021924e-01 -1.25779510e-01 1.47022188e-01 -6.17461383e-01 6.32938325e-01 7.90839374e-01 6.82851434e-01 3.92906934e-01 -3.82783681e-01 -7.86462367e-01 1.40559304e+00 -2.07150578e-01 4.13907975e-01 3.25355023e-01 -2.67124265e-01 1.18272722e+00 -1.11483943e+00 5.82128704e-01 1.24680722e+00 8.56152177e-01 7.40714490e-01 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0e84a979-969a-4b31-aa20-67ffefd6f94e	search-in-the-chain-towards-the-accurate	2304.14732	null	https://arxiv.org/abs/2304.14732v5	https://arxiv.org/pdf/2304.14732v5.pdf	Search-in-the-Chain: Towards Accurate, Credible and Traceable Large Language Models for Knowledge-intensive Tasks	Making the contents generated by Large Language Model (LLM) such as ChatGPT, accurate, credible and traceable is crucial, especially in complex knowledge-intensive tasks that require multi-step reasoning and each of which needs knowledge to solve. Introducing Information Retrieval (IR) to provide LLM with external knowledge is good potential to solve this problem. However, where and how to introduce IR into LLM is a big challenge. Previous work has the disadvantage that the wrong knowledge retrieved by IR misleads the LLM or breaks the reasoning chain of LLM. In this paper, we propose a novel framework called Search-in-the-Chain (SearChain) for the interaction between LLM and IR to solve the challenges. First, LLM generates the global reasoning chain called Chain-of-Query (CoQ) where each node consists of an IR-oriented query and the answer to the query. Second, IR verifies the answer of each node of CoQ, it corrects the answer that is not consistent with the retrieved information when IR gives high confidence, which improves the credibility. Third, LLM can mark its missing knowledge in CoQ and IR can provide this knowledge to LLM. These three operations improve the accuracy of LLM for complex knowledge-intensive tasks in terms of reasoning ability and knowledge. Finally, SearChain generates the reasoning process and marks references to supporting documents for each reasoning step, which improves traceability. SearChain transforms the topology of reasoning from chain to tree, which can modify the reasoning direction. Experiment shows that SearChain outperforms baselines on complex knowledge-intensive tasks including multi-hop question-answering, slot filling, fact checking, and long-form question-answering.	['Tat-Seng Chua', 'Xueqi Cheng', 'HuaWei Shen', 'Liang Pang', 'Shicheng Xu']	2023-04-28	null	null	null	null	['multi-hop-question-answering', 'long-form-question-answering', 'slot-filling']	['knowledge-base', 'natural-language-processing', 'natural-language-processing']	[-1.41343370e-01 6.61519706e-01 -4.07221258e-01 -9.58232209e-02 -1.25957990e+00 -8.23749006e-01 3.62437069e-01 3.71209472e-01 -4.77085352e-01 7.66464412e-01 2.19410405e-01 -7.02180207e-01 -5.23380458e-01 -1.11186683e+00 -8.79522085e-01 1.24772064e-01 4.41292733e-01 1.04764533e+00 1.04536688e+00 -5.38389325e-01 3.65649968e-01 -6.91069365e-02 -1.01703322e+00 8.40249836e-01 1.11619222e+00 8.75406861e-01 1.61965787e-01 3.43303442e-01 -9.77989674e-01 1.49091852e+00 -7.76511073e-01 -7.28752792e-01 -3.68264504e-02 -1.13172762e-01 -1.81829536e+00 -6.07018769e-01 2.02918902e-01 -2.20528051e-01 1.58235207e-02 1.11068618e+00 1.28526926e-01 1.55451223e-01 3.35929006e-01 -1.41568875e+00 -8.16305459e-01 1.11794472e+00 -3.94227564e-01 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f72c1970-8352-4784-b3cb-f67c1522efb4	automatic-microscopic-cell-counting-by-use-of-1	1903.01084	null	http://arxiv.org/abs/1903.01084v3	http://arxiv.org/pdf/1903.01084v3.pdf	Automatic microscopic cell counting by use of deeply-supervised density regression model	Accurately counting cells in microscopic images is important for medical diagnoses and biological studies, but manual cell counting is very tedious, time-consuming, and prone to subjective errors, and automatic counting can be less accurate than desired. To improve the accuracy of automatic cell counting, we propose here a novel method that employs deeply-supervised density regression. A fully convolutional neural network (FCNN) serves as the primary FCNN for density map regression. Innovatively, a set of auxiliary FCNNs are employed to provide additional supervision for learning the intermediate layers of the primary CNN to improve network performance. In addition, the primary CNN is designed as a concatenating framework to integrate multi-scale features through shortcut connections in the network, which improves the granularity of the features extracted from the intermediate CNN layers and further supports the final density map estimation.	['Lilianna Solnica-Krezel', 'Kyaw Thu Minn', 'Shenghua He', 'Mark Anastasio', 'Hua Li']	2019-03-04	null	null	null	null	['automatic-cell-counting']	['miscellaneous']	[-1.06864884e-01 -1.41122013e-01 -9.18132663e-02 -3.58144581e-01 -2.82013476e-01 -1.56212062e-01 3.30669820e-01 3.17592472e-01 -8.11273932e-01 9.48686898e-01 -1.80088460e-01 -2.53956199e-01 3.50156188e-01 -1.14530671e+00 -3.92989486e-01 -8.95856738e-01 2.97698170e-01 3.09611529e-01 3.85023624e-01 2.13492572e-01 5.85066266e-02 6.61806405e-01 -1.16396749e+00 -7.78581947e-02 1.04348874e+00 1.17314851e+00 2.92986870e-01 4.64950562e-01 -5.01441300e-01 1.03562510e+00 -6.14327312e-01 -1.76854163e-01 -3.02899987e-01 -2.98053414e-01 -5.47021687e-01 -2.22694334e-02 -2.96544671e-01 -4.02916133e-01 -2.99281269e-01 1.05958056e+00 3.21014762e-01 -2.15578109e-01 7.57059991e-01 -9.69539344e-01 -4.54568684e-01 3.57611507e-01 -6.77402973e-01 3.63168269e-01 -2.30193302e-01 3.24997485e-01 5.86242795e-01 -6.39878869e-01 1.49008527e-01 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5cfa24af-0054-4367-93ef-dd0a0d1439db	cparr-category-based-proposal-analysis-for	2004.08028	null	https://arxiv.org/abs/2004.08028v1	https://arxiv.org/pdf/2004.08028v1.pdf	CPARR: Category-based Proposal Analysis for Referring Relationships	The task of referring relationships is to localize subject and object entities in an image satisfying a relationship query, which is given in the form of \texttt{<subject, predicate, object>}. This requires simultaneous localization of the subject and object entities in a specified relationship. We introduce a simple yet effective proposal-based method for referring relationships. Different from the existing methods such as SSAS, our method can generate a high-resolution result while reducing its complexity and ambiguity. Our method is composed of two modules: a category-based proposal generation module to select the proposals related to the entities and a predicate analysis module to score the compatibility of pairs of selected proposals. We show state-of-the-art performance on the referring relationship task on two public datasets: Visual Relationship Detection and Visual Genome.	['Jiyang Gao', 'Chuanzi He', 'Ram Nevatia', 'Haidong Zhu', 'Kan Chen']	2020-04-17	null	null	null	null	['visual-relationship-detection']	['computer-vision']	[ 1.88346654e-01 2.03532517e-01 -1.00821197e-01 -6.58236444e-01 -8.00547004e-01 -6.58601284e-01 5.88829339e-01 2.77717590e-01 -1.23132259e-01 3.69621187e-01 8.69780034e-02 -1.47717610e-01 -4.03695889e-02 -9.26709950e-01 -6.58370793e-01 -1.52273551e-01 2.09262982e-01 6.44797623e-01 1.03253686e+00 -1.21345297e-01 8.48088115e-02 6.62225604e-01 -1.76111448e+00 5.21823049e-01 6.79776490e-01 1.07553840e+00 3.30592394e-01 2.74718344e-01 -3.13866735e-01 5.36994219e-01 -5.56670368e-01 -7.35417664e-01 1.40888132e-02 -3.85342151e-01 -1.08564436e+00 1.70118496e-01 6.60764277e-01 1.05987057e-01 4.72311452e-02 1.04200125e+00 4.09103662e-01 -2.94452459e-02 6.03351712e-01 -1.36125410e+00 -6.03399932e-01 4.40567851e-01 -7.00376749e-01 3.28303814e-01 8.38181496e-01 -3.03758234e-01 1.43443263e+00 -1.29369283e+00 1.16157210e+00 1.50150025e+00 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fb716c4c-b7a8-45c7-b124-890a754c23a5	learning-from-suspected-target-bootstrapping	2003.01109	null	https://arxiv.org/abs/2003.01109v1	https://arxiv.org/pdf/2003.01109v1.pdf	Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography	Deep learning object detection algorithm has been widely used in medical image analysis. Currently all the object detection tasks are based on the data annotated with object classes and their bounding boxes. On the other hand, medical images such as mammography usually contain normal regions or objects that are similar to the lesion region, and may be misclassified in the testing stage if they are not taken care of. In this paper, we address such problem by introducing a novel top likelihood loss together with a new sampling procedure to select and train the suspected target regions, as well as proposing a similarity loss to further identify suspected targets from targets. Mean average precision (mAP) according to the predicted targets and specificity, sensitivity, accuracy, AUC values according to classification of patients are adopted for performance comparisons. We firstly test our proposed method on a private dense mammogram dataset. Results show that our proposed method greatly reduce the false positive rate and the specificity is increased by 0.25 on detecting mass type cancer. It is worth mention that dense breast typically has a higher risk for developing breast cancers and also are harder for cancer detection in diagnosis, and our method outperforms a reported result from performance of radiologists. Our method is also validated on the public Digital Database for Screening Mammography (DDSM) dataset, brings significant improvement on mass type cancer detection and outperforms the most state-of-the-art work.	['Cheng Zhu', 'Yi Zhao', 'Peifang Liu', 'Chunlong Luo', 'Li Xiao', 'Junjun Liu']	2020-03-01	null	null	null	null	['breast-cancer-detection', 'breast-cancer-detection']	['knowledge-base', 'medical']	[ 4.80171770e-01 4.80892003e-01 -3.79868269e-01 -5.75172663e-01 -8.31411123e-01 6.92366734e-02 2.76291668e-01 6.72076166e-01 -6.59324288e-01 4.51424897e-01 -1.79132700e-01 -3.02203059e-01 -2.40435123e-01 -1.00285006e+00 -8.37109089e-01 -9.02302504e-01 -2.70612091e-01 6.69256568e-01 7.84738541e-01 3.50492805e-01 -3.18348557e-02 3.66980791e-01 -1.26407194e+00 5.81885159e-01 6.47353113e-01 1.35963058e+00 5.43349683e-01 4.06552434e-01 1.43172175e-01 6.49503171e-01 -3.76172751e-01 -5.13902068e-01 2.83023924e-01 -3.16391289e-01 -6.69596732e-01 1.41755939e-01 1.95039868e-01 -5.69472611e-01 -1.32182956e-01 1.18378294e+00 5.61122537e-01 -3.77991647e-01 9.53643143e-01 -7.06900477e-01 -2.36189052e-01 5.88654995e-01 -9.62771893e-01 3.92700642e-01 -3.11472565e-01 -2.15398923e-01 4.80681360e-01 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f9fb1100-b66a-4a7f-aa24-637c6cc88c37	efficient-and-differentiable-conformal-1	2202.11091	null	https://arxiv.org/abs/2202.11091v2	https://arxiv.org/pdf/2202.11091v2.pdf	Efficient and Differentiable Conformal Prediction with General Function Classes	Quantifying the data uncertainty in learning tasks is often done by learning a prediction interval or prediction set of the label given the input. Two commonly desired properties for learned prediction sets are \emph{valid coverage} and \emph{good efficiency} (such as low length or low cardinality). Conformal prediction is a powerful technique for learning prediction sets with valid coverage, yet by default its conformalization step only learns a single parameter, and does not optimize the efficiency over more expressive function classes. In this paper, we propose a generalization of conformal prediction to multiple learnable parameters, by considering the constrained empirical risk minimization (ERM) problem of finding the most efficient prediction set subject to valid empirical coverage. This meta-algorithm generalizes existing conformal prediction algorithms, and we show that it achieves approximate valid population coverage and near-optimal efficiency within class, whenever the function class in the conformalization step is low-capacity in a certain sense. Next, this ERM problem is challenging to optimize as it involves a non-differentiable coverage constraint. We develop a gradient-based algorithm for it by approximating the original constrained ERM using differentiable surrogate losses and Lagrangians. Experiments show that our algorithm is able to learn valid prediction sets and improve the efficiency significantly over existing approaches in several applications such as prediction intervals with improved length, minimum-volume prediction sets for multi-output regression, and label prediction sets for image classification.	['Caiming Xiong', 'Yingbo Zhou', 'Huan Wang', 'Song Mei', 'Yu Bai']	2022-02-22	efficient-and-differentiable-conformal	https://openreview.net/forum?id=Ht85_jyihxp	https://openreview.net/pdf?id=Ht85_jyihxp	iclr-2022-4	['prediction-intervals']	['miscellaneous']	[ 6.34558380e-01 7.59256721e-01 -8.08084726e-01 -7.17247605e-01 -1.60529602e+00 -4.79086339e-01 2.89124418e-02 5.38981855e-01 -1.39817297e-01 1.02307296e+00 -4.83786762e-02 -1.38809592e-01 -8.64973128e-01 -9.44544077e-01 -1.12031722e+00 -8.08778286e-01 -1.87320709e-01 7.86128044e-01 4.17194441e-02 3.83793831e-01 8.57491717e-02 1.87070116e-01 -1.44629133e+00 2.25665092e-01 1.15795457e+00 1.33235967e+00 4.40316312e-02 2.48617381e-01 3.08981657e-01 6.93998516e-01 -8.93104002e-02 -5.30459225e-01 1.79533616e-01 -2.76524484e-01 -7.52489388e-01 -3.42442185e-01 2.30789244e-01 -3.08037978e-02 4.10524249e-01 9.28078413e-01 3.02234083e-01 1.32157747e-02 1.16399968e+00 -1.39118242e+00 -3.65440369e-01 9.02222037e-01 -4.11956519e-01 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f150835d-3f23-403a-9649-89359ceb7e02	bilingual-lexicon-induction-by-learning-to	null	null	https://aclanthology.org/E17-1102	https://aclanthology.org/E17-1102.pdf	Bilingual Lexicon Induction by Learning to Combine Word-Level and Character-Level Representations	We study the problem of bilingual lexicon induction (BLI) in a setting where some translation resources are available, but unknown translations are sought for certain, possibly domain-specific terminology. We frame BLI as a classification problem for which we design a neural network based classification architecture composed of recurrent long short-term memory and deep feed forward networks. The results show that word- and character-level representations each improve state-of-the-art results for BLI, and the best results are obtained by exploiting the synergy between these word- and character-level representations in the classification model.	['Marie-Francine Moens', "Ivan Vuli{\\'c}", 'Geert Heyman']	2017-04-01	null	null	null	eacl-2017-4	['cross-lingual-entity-linking']	['natural-language-processing']	[ 1.91907465e-01 -2.56661683e-01 -1.06391871e+00 -3.30433488e-01 -1.04530191e+00 -6.04356349e-01 7.21712708e-01 -5.78860417e-02 -5.49491167e-01 9.11907792e-01 4.35913980e-01 -8.05121481e-01 1.72061875e-01 -6.11476302e-01 -7.93856382e-01 -2.20271885e-01 3.60540077e-02 1.05738223e+00 -4.97023404e-01 -5.38724124e-01 -6.21032864e-02 4.44387496e-01 -9.93193448e-01 6.47942424e-01 9.21501875e-01 6.11401856e-01 -1.01789132e-01 2.01608866e-01 -4.08094019e-01 8.79685998e-01 -4.18327510e-01 -3.31012905e-01 1.08252451e-01 -5.51231086e-01 -1.17624080e+00 -2.55992532e-01 1.70150876e-01 2.59172857e-01 -3.92435193e-01 1.11650741e+00 4.03628290e-01 -1.46388754e-01 6.66286945e-01 -5.54978013e-01 -9.78727639e-01 1.11060858e+00 -3.99736792e-01 5.00197768e-01 3.77188742e-01 -6.84992000e-02 1.35533750e+00 -1.20045614e+00 9.55108404e-01 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6.02801383e-01 -5.62277257e-01 -4.00856793e-01 -1.29536808e+00 -2.73802817e-01 2.19605505e-01 3.83868426e-01 -4.17531043e-01 7.98214152e-02 5.81829660e-02]	[11.27636432647705, 10.03506851196289]
3f51a365-91d3-4d8a-8a71-127b76a95f46	grad2task-improved-few-shot-text-1	2201.11576	null	https://arxiv.org/abs/2201.11576v1	https://arxiv.org/pdf/2201.11576v1.pdf	Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation	Large pretrained language models (LMs) like BERT have improved performance in many disparate natural language processing (NLP) tasks. However, fine tuning such models requires a large number of training examples for each target task. Simultaneously, many realistic NLP problems are "few shot", without a sufficiently large training set. In this work, we propose a novel conditional neural process-based approach for few-shot text classification that learns to transfer from other diverse tasks with rich annotation. Our key idea is to represent each task using gradient information from a base model and to train an adaptation network that modulates a text classifier conditioned on the task representation. While previous task-aware few-shot learners represent tasks by input encoding, our novel task representation is more powerful, as the gradient captures input-output relationships of a task. Experimental results show that our approach outperforms traditional fine-tuning, sequential transfer learning, and state-of-the-art meta learning approaches on a collection of diverse few-shot tasks. We further conducted analysis and ablations to justify our design choices.	['Michael Brudno', 'Frank Rudzicz', 'Kuan-Chieh Wang', 'Jixuan Wang']	2022-01-27	grad2task-improved-few-shot-text	http://proceedings.neurips.cc/paper/2021/hash/33a854e247155d590883b93bca53848a-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/33a854e247155d590883b93bca53848a-Paper.pdf	neurips-2021-12	['few-shot-text-classification']	['natural-language-processing']	[ 5.59416890e-01 -4.18613814e-02 -2.42211387e-01 -5.21797121e-01 -9.78650272e-01 -1.67195156e-01 9.56149578e-01 2.96562940e-01 -7.82769322e-01 6.68867290e-01 5.25411248e-01 1.28869906e-01 -8.09633434e-02 -6.56173587e-01 -5.34423530e-01 -5.31510174e-01 3.80870640e-01 8.26504946e-01 2.77082175e-01 -3.08739364e-01 4.15288061e-01 -3.31039578e-01 -1.36734009e+00 6.47175074e-01 8.28874409e-01 8.21512640e-01 5.67685544e-01 7.21503735e-01 -5.94819725e-01 1.05535245e+00 -6.05358064e-01 -2.27175057e-01 -1.23860404e-01 -5.73110640e-01 -8.48350048e-01 -3.68993104e-01 2.03973517e-01 3.79975326e-02 -9.52825695e-02 7.81974852e-01 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4472b8fd-f2fe-4731-a957-b08eea4d94a7	surgical-phase-recognition-in-laparoscopic	2206.07198	null	https://arxiv.org/abs/2206.07198v1	https://arxiv.org/pdf/2206.07198v1.pdf	Surgical Phase Recognition in Laparoscopic Cholecystectomy	Automatic recognition of surgical phases in surgical videos is a fundamental task in surgical workflow analysis. In this report, we propose a Transformer-based method that utilizes calibrated confidence scores for a 2-stage inference pipeline, which dynamically switches between a baseline model and a separately trained transition model depending on the calibrated confidence level. Our method outperforms the baseline model on the Cholec80 dataset, and can be applied to a variety of action segmentation methods.	['Himanshu Gupta', 'Haibin Ling', 'I. V. Ramakrishnan', 'Prateek Prasanna', 'Vinayak Shenoy', 'Yunfan Li']	2022-06-14	null	null	null	null	['surgical-phase-recognition', 'action-segmentation']	['computer-vision', 'computer-vision']	[ 5.55201054e-01 2.53681570e-01 -7.85300434e-01 -4.07797486e-01 -1.18945682e+00 -6.33876741e-01 3.76474619e-01 1.99241817e-01 -6.36663139e-01 3.68649364e-01 2.83388346e-01 -6.88139975e-01 -1.18983828e-01 -2.19120249e-01 -4.42419022e-01 -7.06481159e-01 1.66570231e-01 6.59558117e-01 5.85870802e-01 2.65679926e-01 3.39268386e-01 3.64629358e-01 -8.03977072e-01 6.51474893e-01 7.43625224e-01 9.59052622e-01 2.61610985e-01 8.29431832e-01 1.40908256e-01 7.65632868e-01 -4.00983661e-01 -1.75966144e-01 2.03810349e-01 -3.81555617e-01 -8.96901011e-01 -1.19943835e-01 2.27246955e-01 -1.75844252e-01 -1.59198776e-01 1.09947836e+00 2.84073502e-01 -1.75325915e-01 5.97637832e-01 -6.90974832e-01 4.75686044e-01 8.20049524e-01 -3.06252509e-01 5.18268287e-01 7.62901232e-02 1.04981519e-01 5.98614037e-01 -5.69559097e-01 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06e9ed4c-f1d3-4242-87f1-7cd9b69ed034	r2d2-reliable-and-repeatable-detectors-and	1906.06195	null	https://arxiv.org/abs/1906.06195v2	https://arxiv.org/pdf/1906.06195v2.pdf	R2D2: Repeatable and Reliable Detector and Descriptor	Interest point detection and local feature description are fundamental steps in many computer vision applications. Classical methods for these tasks are based on a detect-then-describe paradigm where separate handcrafted methods are used to first identify repeatable keypoints and then represent them with a local descriptor. Neural networks trained with metric learning losses have recently caught up with these techniques, focusing on learning repeatable saliency maps for keypoint detection and learning descriptors at the detected keypoint locations. In this work, we argue that salient regions are not necessarily discriminative, and therefore can harm the performance of the description. Furthermore, we claim that descriptors should be learned only in regions for which matching can be performed with high confidence. We thus propose to jointly learn keypoint detection and description together with a predictor of the local descriptor discriminativeness. This allows us to avoid ambiguous areas and leads to reliable keypoint detections and descriptions. Our detection-and-description approach, trained with self-supervision, can simultaneously output sparse, repeatable and reliable keypoints that outperforms state-of-the-art detectors and descriptors on the HPatches dataset. It also establishes a record on the recently released Aachen Day-Night localization dataset.	['Gabriela Csurka', 'César De Souza', 'Philippe Weinzaepfel', 'Jerome Revaud', 'Yohann Cabon', 'Noe Pion', 'Martin Humenberger']	2019-06-14	null	null	null	null	['interest-point-detection']	['computer-vision']	[-4.76415046e-02 -8.75697881e-02 -4.45463806e-01 -2.11075246e-01 -1.14175200e+00 -5.78591466e-01 9.29558158e-01 6.88277960e-01 -5.98240912e-01 5.03402114e-01 -9.96749103e-02 3.38844448e-01 -3.10419410e-01 -4.58978176e-01 -8.61568689e-01 -6.98559344e-01 -2.84188122e-01 3.74910682e-01 6.09006047e-01 -8.20354074e-02 5.56212544e-01 8.66771758e-01 -1.76605070e+00 5.05162496e-03 5.55475235e-01 1.23000014e+00 5.54819405e-01 5.93719840e-01 2.13751838e-01 6.87549531e-01 -4.56240594e-01 -1.20816596e-01 2.96194375e-01 -2.96937823e-01 -4.59940612e-01 -2.25240529e-01 8.72959495e-01 -2.18125790e-01 -3.29352468e-01 9.80661869e-01 4.46336746e-01 1.99464485e-01 6.66657150e-01 -1.30609334e+00 -5.34036934e-01 2.22849607e-01 -5.63902497e-01 2.96451837e-01 3.07839811e-01 -1.65657267e-01 1.41383779e+00 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85050ecd-cf5e-460e-8009-32c3766308a0	cmrnet-camera-to-lidar-map-registration	1906.10109	null	https://arxiv.org/abs/1906.10109v3	https://arxiv.org/pdf/1906.10109v3.pdf	CMRNet: Camera to LiDAR-Map Registration	In this paper we present CMRNet, a realtime approach based on a Convolutional Neural Network to localize an RGB image of a scene in a map built from LiDAR data. Our network is not trained in the working area, i.e. CMRNet does not learn the map. Instead it learns to match an image to the map. We validate our approach on the KITTI dataset, processing each frame independently without any tracking procedure. CMRNet achieves 0.27m and 1.07deg median localization accuracy on the sequence 00 of the odometry dataset, starting from a rough pose estimate displaced up to 3.5m and 17deg. To the best of our knowledge this is the first CNN-based approach that learns to match images from a monocular camera to a given, preexisting 3D LiDAR-map.	['Augusto Luis Ballardini', 'Daniele Cattaneo', 'Domenico Giorgio Sorrenti', 'Wolfram Burgard', 'Simone Fontana', 'Matteo Vaghi']	2019-06-24	null	null	null	null	['camera-localization']	['computer-vision']	[-7.99922347e-02 1.28911227e-01 2.74852157e-01 -5.73023796e-01 -4.76793975e-01 -6.86716139e-01 3.39760512e-01 -9.11781341e-02 -9.10552859e-01 5.88225663e-01 -4.76976812e-01 -6.23740852e-02 8.74688253e-02 -8.10085416e-01 -1.31298339e+00 -1.07400499e-01 -1.41418278e-01 9.21198010e-01 4.18772727e-01 -1.30830884e-01 1.59270093e-01 8.88754129e-01 -1.30850160e+00 -2.52221912e-01 3.57434958e-01 1.22228515e+00 4.43698406e-01 7.17734396e-01 2.54478127e-01 5.45177996e-01 -3.41505498e-01 -4.50423695e-02 7.09640622e-01 8.08141232e-02 -5.79572916e-01 -2.81643867e-02 1.09012377e+00 -6.13370001e-01 -5.64824760e-01 7.83713579e-01 5.06461740e-01 -2.47174010e-01 1.86102927e-01 -1.14379680e+00 -2.11763561e-01 1.46531865e-01 -3.70049417e-01 3.57959382e-02 3.29262465e-01 1.76557615e-01 3.40673089e-01 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9941406a-40c3-46fa-810b-0c9842ca6523	adaptive-edge-attention-for-graph-matching	null	null	https://www.ijcai.org/proceedings/2021/134	https://www.ijcai.org/proceedings/2021/0134.pdf	Adaptive Edge Attention for Graph Matching with Outliers	Graph matching aims at establishing correspondence between node sets of given graphs while keeping the consistency between their edge sets. However, outliers in practical scenarios and equivalent learning of edge representations in deep learning methods are still challenging. To address these issues, we present an Edge Attention-adaptive Graph Matching (EAGM) network and a novel description of edge features. EAGM transforms the matching relation between two graphs into a node and edge classification problem over their assignment graph. To explore the potential of edges, EAGM learns edge attention on the assignment graph to 1) reveal the impact of each edge on graph matching, as well as 2) adjust the learning of edge representations adaptively. To alleviate issues caused by the outliers, we describe an edge by aggregating the semantic information over the space spanned by the edge. Such rich information provides clear distinctions between different edges (e.g., inlier-inlier edges vs. inlier-outlier edges), which further distinguishes outliers in the view of their associated edges. Extensive experiments demonstrate that EAGM achieves promising matching quality compared with state-of-thearts, on cases both with and without outliers. Our source code along with the experiments is available at https://github.com/bestwei/EAGM.	['Zhi Tang', 'Xiaoqing Lyu', 'Chenrui Zhang', 'Haibin Ling', 'Jingwei Qu']	2021-08-19	null	null	null	international-joint-conference-on-artificial-4	['graph-matching']	['graphs']	[-2.49427572e-01 3.89517725e-01 -7.69850612e-02 -3.17383379e-01 -5.05912840e-01 -3.08136791e-01 2.68255293e-01 3.61061662e-01 1.20939814e-01 1.57292798e-01 3.29379737e-01 8.55539814e-02 -2.24327415e-01 -8.14533532e-01 -8.24982882e-01 -4.68090087e-01 -2.47540712e-01 4.38544720e-01 -1.69040769e-01 -4.23675543e-03 1.60151243e-01 5.30488610e-01 -1.14381886e+00 6.03020899e-02 9.18672204e-01 8.80798221e-01 -3.51301916e-02 3.49344999e-01 -1.86575785e-01 2.86563903e-01 -3.00149560e-01 -5.29491484e-01 6.82343006e-01 -3.07055056e-01 -4.12733793e-01 4.23688710e-01 8.79868865e-01 -1.98272765e-01 -9.66018677e-01 1.45651388e+00 4.66957808e-01 2.30325058e-01 3.97056282e-01 -1.85427642e+00 -1.02526641e+00 6.11720741e-01 -8.29766750e-01 2.50191867e-01 4.05128598e-01 2.84954846e-01 1.45413768e+00 -1.04152775e+00 4.83700484e-01 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6ec96fe9-ebb3-4c35-a4a4-8f48694dd735	one-model-is-all-you-need-multi-task-learning	2203.00077	null	https://arxiv.org/abs/2203.00077v2	https://arxiv.org/pdf/2203.00077v2.pdf	One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classification	The recent surge in performance for image analysis of digitised pathology slides can largely be attributed to the advances in deep learning. Deep models can be used to initially localise various structures in the tissue and hence facilitate the extraction of interpretable features for biomarker discovery. However, these models are typically trained for a single task and therefore scale poorly as we wish to adapt the model for an increasing number of different tasks. Also, supervised deep learning models are very data hungry and therefore rely on large amounts of training data to perform well. In this paper, we present a multi-task learning approach for segmentation and classification of nuclei, glands, lumina and different tissue regions that leverages data from multiple independent data sources. While ensuring that our tasks are aligned by the same tissue type and resolution, we enable meaningful simultaneous prediction with a single network. As a result of feature sharing, we also show that the learned representation can be used to improve the performance of additional tasks via transfer learning, including nuclear classification and signet ring cell detection. As part of this work, we train our developed Cerberus model on a huge amount of data, consisting of over 600K objects for segmentation and 440K patches for classification. We use our approach to process 599 colorectal whole-slide images from TCGA, where we localise 377 million, 900K and 2.1 million nuclei, glands and lumina, respectively and make the results available to the community for downstream analysis.	['Nasir Rajpoot', 'David Snead', 'Fayyaz Minhas', 'Shan E Ahmed Raza', 'Mostafa Jahanifar', 'Quoc Dang Vu', 'Simon Graham']	2022-02-28	null	null	null	null	['explainable-models', 'cell-detection']	['computer-vision', 'computer-vision']	[ 4.58704680e-01 2.25580901e-01 -2.96331018e-01 -3.43314409e-01 -1.45263386e+00 -8.23185623e-01 4.61906374e-01 5.48444033e-01 -4.84714687e-01 6.37708187e-01 1.45932436e-01 -4.29828256e-01 -9.09294188e-02 -5.57334423e-01 -7.26770163e-01 -1.08261025e+00 -2.85851583e-02 5.72480023e-01 3.29982847e-01 1.39721977e-02 8.24675243e-03 5.96502721e-01 -9.65812206e-01 6.49483502e-01 4.43317741e-01 8.54233325e-01 1.03647307e-01 1.00578666e+00 4.91666943e-02 5.35163462e-01 -3.08893561e-01 -2.10464939e-01 6.55254573e-02 -1.27603084e-01 -1.00945938e+00 -4.25326675e-02 4.18762356e-01 -1.12114981e-01 3.49957496e-02 8.63386393e-01 6.27132058e-01 -4.45877075e-01 8.23700309e-01 -7.44900167e-01 1.32495075e-01 3.71836901e-01 -6.52835250e-01 3.86623412e-01 -2.58288234e-01 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7f291d51-e556-4848-aa84-3e0e70d56993	a-point-cloud-generative-model-based-on	null	null	https://openreview.net/forum?id=O1GEH9X8848	https://openreview.net/pdf?id=O1GEH9X8848	A Point Cloud Generative Model Based on Nonequilibrium Thermodynamics	We present a probabilistic model for point cloud generation, which is critical for various 3D vision tasks such as shape completion, upsampling, synthesis and data augmentation. Inspired by the diffusion process in non-equilibrium thermodynamics, we view points in point clouds as particles in a thermodynamic system in contact with a heat bath, which diffuse from the original distribution to a noise distribution. Point cloud generation thus amounts to learning the reverse diffusion process that transforms the noise distribution to the distribution of a desired shape. Specifically, we propose to model the reverse diffusion process for point clouds as a Markov chain conditioned on certain shape latent. We derive the variational bound in closed form for training and provide implementations of the model. Experimental results demonstrate that our model achieves the state-of-the-art performance in point cloud generation and auto-encoding.	['Wei Hu', 'Shitong Luo']	2021-01-01	null	null	null	null	['point-cloud-generation']	['computer-vision']	[ 3.41444492e-01 1.46766871e-01 2.17793390e-01 -9.43043455e-02 -7.58251429e-01 -5.52887857e-01 9.77940083e-01 -1.52593762e-01 -2.31322553e-02 4.08717930e-01 -1.41389549e-01 -1.77291900e-01 2.74358004e-01 -1.05502760e+00 -1.02560389e+00 -9.48493421e-01 3.42905551e-01 1.09321070e+00 -1.24604702e-01 2.29254976e-01 1.67923629e-01 9.97567713e-01 -1.52368689e+00 -1.53576404e-01 7.83834696e-01 6.99709594e-01 4.56059426e-01 8.44675183e-01 -4.18308079e-01 1.23564579e-01 -2.14319408e-01 -3.13883424e-01 2.77578622e-01 -2.01043546e-01 -4.86490399e-01 3.70886892e-01 2.98069924e-01 -1.60672069e-01 -4.47685599e-01 1.23759186e+00 2.89129943e-01 1.04548663e-01 1.42989540e+00 -1.04116571e+00 -1.07247221e+00 7.13621378e-02 -6.46137893e-01 -3.21234554e-01 -4.60175537e-02 1.86632514e-01 7.07948983e-01 -1.25628042e+00 7.96487033e-01 1.24505389e+00 5.54519117e-01 1.00452483e+00 -1.65378928e+00 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642e00d3-7699-479d-ac87-863af0e24fb5	current-status-and-performance-analysis-of	2104.14272	null	https://arxiv.org/abs/2104.14272v2	https://arxiv.org/pdf/2104.14272v2.pdf	Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networks	The first phase of table recognition is to detect the tabular area in a document. Subsequently, the tabular structures are recognized in the second phase in order to extract information from the respective cells. Table detection and structural recognition are pivotal problems in the domain of table understanding. However, table analysis is a perplexing task due to the colossal amount of diversity and asymmetry in tables. Therefore, it is an active area of research in document image analysis. Recent advances in the computing capabilities of graphical processing units have enabled deep neural networks to outperform traditional state-of-the-art machine learning methods. Table understanding has substantially benefited from the recent breakthroughs in deep neural networks. However, there has not been a consolidated description of the deep learning methods for table detection and table structure recognition. This review paper provides a thorough analysis of the modern methodologies that utilize deep neural networks. This work provided a thorough understanding of the current state-of-the-art and related challenges of table understanding in document images. Furthermore, the leading datasets and their intricacies have been elaborated along with the quantitative results. Moreover, a brief overview is given regarding the promising directions that can serve as a guide to further improve table analysis in document images.	['Muhammad Zeshan Afzal', 'Muhammad Ahtsham Afzal', 'Muhammad Adnan Afzal', 'Didier Stricker', 'Marcus Liwicki', 'Khurram Azeem Hashmi']	2021-04-29	null	null	null	null	['table-recognition', 'table-detection']	['computer-vision', 'miscellaneous']	[ 3.41291130e-01 -3.52381051e-01 -3.76887858e-01 -2.50075787e-01 -5.81268132e-01 -7.37917006e-01 4.66582894e-01 5.99373937e-01 1.79901272e-01 5.86935043e-01 1.24886334e-02 -2.16791347e-01 -1.83629580e-02 -1.06957459e+00 -6.16397262e-01 -6.53723359e-01 -4.55728266e-03 5.21616161e-01 -3.32010567e-01 -4.26932544e-01 5.89569449e-01 7.59280503e-01 -1.51362979e+00 1.17878270e+00 8.27300966e-01 1.48088062e+00 -9.62476507e-02 8.69288981e-01 -8.63962114e-01 1.25676441e+00 -9.08348203e-01 -6.26564980e-01 -1.52336523e-01 -5.07684112e-01 -6.06923282e-01 4.36973482e-01 6.17630780e-01 -4.15429294e-01 -7.09090173e-01 9.99578238e-01 3.03377628e-01 -8.62082914e-02 6.82189763e-01 -1.04150391e+00 -7.75307000e-01 8.46654832e-01 -5.38297832e-01 3.43813449e-01 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b6aaf7b2-4260-4456-8aea-a0a073d19789	collaborative-noisy-label-cleaner-learning	2303.14768	null	https://arxiv.org/abs/2303.14768v1	https://arxiv.org/pdf/2303.14768v1.pdf	Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in Movies	Movie highlights stand out of the screenplay for efficient browsing and play a crucial role on social media platforms. Based on existing efforts, this work has two observations: (1) For different annotators, labeling highlight has uncertainty, which leads to inaccurate and time-consuming annotations. (2) Besides previous supervised or unsupervised settings, some existing video corpora can be useful, e.g., trailers, but they are often noisy and incomplete to cover the full highlights. In this work, we study a more practical and promising setting, i.e., reformulating highlight detection as "learning with noisy labels". This setting does not require time-consuming manual annotations and can fully utilize existing abundant video corpora. First, based on movie trailers, we leverage scene segmentation to obtain complete shots, which are regarded as noisy labels. Then, we propose a Collaborative noisy Label Cleaner (CLC) framework to learn from noisy highlight moments. CLC consists of two modules: augmented cross-propagation (ACP) and multi-modality cleaning (MMC). The former aims to exploit the closely related audio-visual signals and fuse them to learn unified multi-modal representations. The latter aims to achieve cleaner highlight labels by observing the changes in losses among different modalities. To verify the effectiveness of CLC, we further collect a large-scale highlight dataset named MovieLights. Comprehensive experiments on MovieLights and YouTube Highlights datasets demonstrate the effectiveness of our approach. Code has been made available at: https://github.com/TencentYoutuResearch/HighlightDetection-CLC	['Bo Ren', 'Hanjun Li', 'Keyu Chen', 'Haoqian Wu', 'Ruizhi Qiao', 'Xiujun Shu', 'Bei Gan']	2023-03-26	null	http://openaccess.thecvf.com//content/CVPR2023/html/Gan_Collaborative_Noisy_Label_Cleaner_Learning_Scene-Aware_Trailers_for_Multi-Modal_Highlight_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Gan_Collaborative_Noisy_Label_Cleaner_Learning_Scene-Aware_Trailers_for_Multi-Modal_Highlight_CVPR_2023_paper.pdf	cvpr-2023-1	['scene-segmentation', 'highlight-detection', 'learning-with-noisy-labels', 'learning-with-noisy-labels']	['computer-vision', 'computer-vision', 'computer-vision', 'natural-language-processing']	[ 1.20201394e-01 -3.96012753e-01 -2.50001401e-01 -2.62883127e-01 -1.15779686e+00 -6.66958749e-01 3.08544338e-01 6.90246224e-02 -3.25214535e-01 4.21128869e-01 2.70455867e-01 1.49051309e-01 8.90587941e-02 -3.12665910e-01 -7.42987692e-01 -7.47699440e-01 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74e9517d-e5b7-401d-be8e-a098b97f500e	what-do-questions-exactly-ask-mfae-duplicate	null	null	https://epubs.siam.org/doi/10.1137/1.9781611976236.26	https://epubs.siam.org/doi/pdf/10.1137/1.9781611976236.26	What Do Questions Exactly Ask? MFAE: Duplicate Question Identification with Multi-Fusion Asking Emphasis	Duplicate Question Identification (DQI) improves the processing efficiency and accuracy of large-scale community question answering and automatic QA system. The purpose of DQI task is to identify whether the paired questions are semantically equivalent. However, how to distinguish the synonyms or homonyms in paired questions is still challenging. Most previous works focus on the word-level or phrase-level semantic differences. We firstly propose to explore the asking emphasis of a question as a key factor in DQI. Asking emphasis bridges semantic equivalence between two questions. In this paper, we propose an attention model with multi-fusion asking emphasis (MFAE) for DQI. At first, BERT is used to obtain the dynamic pre-trained word embeddings. Then we get inter- and intra-asking emphasis by summing inter-attention and self-attention, respectively; the idea is that, the more a word interacts with others, the more important the word is. Finally, we use eight-way combinations to generate multi-fusion asking emphasis and multi-fusion word representation. Experimental results demonstrate that our model achieves state-of-the-art performance on both Quora Question Pairs and CQADupStack data. In addition, our model can also improve the results for natural language inference task on SNLI and MultiNLI datasets. The code is available at https://github.com/rzhangpku/MFAE.	['Tong Mo', 'Weiping Li', 'Bo Wu', 'Qifei Zhou', 'Rong Zhang']	2020-05-07	null	null	null	siam-international-conference-on-data-mining	['community-question-answering', 'community-question-answering', 'paraphrase-identification']	['miscellaneous', 'natural-language-processing', 'natural-language-processing']	[ 5.71442917e-02 -2.74757117e-01 1.87322512e-01 -5.27115464e-01 -1.05920517e+00 -6.29450321e-01 4.93938029e-01 1.54072136e-01 -6.73978686e-01 4.74830389e-01 5.89345634e-01 -3.25464398e-01 -1.87332481e-01 -9.11632240e-01 -4.35492247e-01 -3.65960807e-01 7.23950148e-01 5.09693325e-01 2.34276026e-01 -5.49530566e-01 2.44218156e-01 -1.84152022e-01 -1.32559717e+00 3.95681471e-01 1.31620419e+00 8.42366993e-01 3.19211096e-01 5.64697504e-01 -6.57246351e-01 8.69326949e-01 -7.80352473e-01 -7.03055501e-01 -1.48281887e-01 -6.23341560e-01 -1.26575160e+00 -3.92634809e-01 5.97799182e-01 -2.64850020e-01 -4.55647349e-01 1.11117768e+00 6.09539270e-01 2.97940344e-01 5.05596995e-01 -1.18745947e+00 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c44896a4-f195-4c6c-ad76-b89c42f049ba	energy-inspired-self-supervised-pretraining	2302.01384	null	https://arxiv.org/abs/2302.01384v1	https://arxiv.org/pdf/2302.01384v1.pdf	Energy-Inspired Self-Supervised Pretraining for Vision Models	Motivated by the fact that forward and backward passes of a deep network naturally form symmetric mappings between input and output representations, we introduce a simple yet effective self-supervised vision model pretraining framework inspired by energy-based models (EBMs). In the proposed framework, we model energy estimation and data restoration as the forward and backward passes of a single network without any auxiliary components, e.g., an extra decoder. For the forward pass, we fit a network to an energy function that assigns low energy scores to samples that belong to an unlabeled dataset, and high energy otherwise. For the backward pass, we restore data from corrupted versions iteratively using gradient-based optimization along the direction of energy minimization. In this way, we naturally fold the encoder-decoder architecture widely used in masked image modeling into the forward and backward passes of a single vision model. Thus, our framework now accepts a wide range of pretext tasks with different data corruption methods, and permits models to be pretrained from masked image modeling, patch sorting, and image restoration, including super-resolution, denoising, and colorization. We support our findings with extensive experiments, and show the proposed method delivers comparable and even better performance with remarkably fewer epochs of training compared to the state-of-the-art self-supervised vision model pretraining methods. Our findings shed light on further exploring self-supervised vision model pretraining and pretext tasks beyond masked image modeling.	['Qiang Qiu', 'Zicheng Liu', 'Jiang Wang', 'Ze Wang']	2023-02-02	null	null	null	null	['colorization']	['computer-vision']	[ 5.14204741e-01 2.42299020e-01 2.04750020e-02 -5.30950665e-01 -6.25791371e-01 -1.08467191e-01 6.17854357e-01 -2.15873063e-01 -4.77271497e-01 4.74871099e-01 2.52213687e-01 -3.98283377e-02 2.75080651e-01 -8.40037286e-01 -1.20558953e+00 -8.15266728e-01 5.35043061e-01 1.16954140e-01 8.90370086e-02 3.66538018e-03 1.59506619e-01 3.73598635e-01 -1.77467585e+00 4.32168841e-01 1.09477293e+00 1.01911402e+00 3.93256873e-01 4.53060836e-01 -1.08663678e-01 1.00388861e+00 -1.22282147e-01 -4.41750050e-01 2.02892661e-01 -7.59104431e-01 -6.52228892e-01 4.49761510e-01 6.19587421e-01 -4.51236099e-01 -4.05931890e-01 1.32011473e+00 2.93615550e-01 3.33143979e-01 6.04841948e-01 -7.75124192e-01 -1.47258317e+00 4.92285043e-01 -7.11435199e-01 4.80142161e-02 -1.40482366e-01 2.16460347e-01 6.27052963e-01 -1.23928618e+00 4.46406692e-01 1.13309991e+00 5.16984940e-01 8.60210121e-01 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ac9a5b3f-8f24-4aaf-a0c2-97b0e9418957	progressive-sampling-based-bayesian	1812.02855	null	http://arxiv.org/abs/1812.02855v1	http://arxiv.org/pdf/1812.02855v1.pdf	Progressive Sampling-Based Bayesian Optimization for Efficient and Automatic Machine Learning Model Selection	Purpose: Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. Methods: To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. Results: We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. Conclusions: This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.	['Gang Luo', 'Xueqiang Zeng']	2018-12-06	null	null	null	null	['automatic-machine-learning-model-selection', 'miscellaneous']	['methodology', 'miscellaneous']	[ 3.73418331e-01 -2.90605873e-01 -4.72158998e-01 -4.27816838e-01 -1.31693101e+00 -4.16917115e-01 1.11920044e-01 5.59584498e-01 -5.85715115e-01 8.16042364e-01 1.13128042e-02 -5.09219229e-01 -4.30524707e-01 -6.63941622e-01 -2.01996759e-01 -9.03907895e-01 8.61590728e-02 1.25221801e+00 -5.30623384e-02 2.72385955e-01 4.58576888e-01 4.92449999e-01 -1.19433987e+00 3.31986457e-01 8.19301784e-01 6.23532534e-01 1.63362280e-01 7.58499026e-01 4.83759232e-02 3.78356367e-01 -5.60068846e-01 -3.11661940e-02 2.37462953e-01 -6.06398702e-01 -8.10602307e-01 -3.00698400e-01 1.48412928e-01 8.88161957e-02 4.86090004e-01 8.44151020e-01 1.13634789e+00 2.73680314e-03 6.58520043e-01 -9.23054814e-01 4.12037194e-01 6.30844533e-01 -3.27643484e-01 2.34194428e-01 3.49188386e-03 3.05176288e-01 7.31618404e-01 -5.93095779e-01 6.54668093e-01 9.01425660e-01 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992d92d5-addd-4f99-a29a-793cade80dd5	selective-manipulation-of-disentangled	2208.12632	null	https://arxiv.org/abs/2208.12632v1	https://arxiv.org/pdf/2208.12632v1.pdf	Selective manipulation of disentangled representations for privacy-aware facial image processing	Camera sensors are increasingly being combined with machine learning to perform various tasks such as intelligent surveillance. Due to its computational complexity, most of these machine learning algorithms are offloaded to the cloud for processing. However, users are increasingly concerned about privacy issues such as function creep and malicious usage by third-party cloud providers. To alleviate this, we propose an edge-based filtering stage that removes privacy-sensitive attributes before the sensor data are transmitted to the cloud. We use state-of-the-art image manipulation techniques that leverage disentangled representations to achieve privacy filtering. We define opt-in and opt-out filter operations and evaluate their effectiveness for filtering private attributes from face images. Additionally, we examine the effect of naturally occurring correlations and residual information on filtering. We find the results promising and believe this elicits further research on how image manipulation can be used for privacy preservation.	['Pieter Simoens', 'Sam Leroux', 'Wei-Cheng Wang', 'Sander De Coninck']	2022-08-26	null	null	null	null	['image-manipulation']	['computer-vision']	[ 5.91730952e-01 9.45996940e-02 -2.73818374e-01 -6.16767049e-01 -4.68467325e-01 -9.29594636e-01 3.04894716e-01 2.21342400e-01 -5.22883892e-01 3.07182044e-01 1.93567097e-01 -3.60969961e-01 -6.31243503e-03 -6.75117373e-01 -6.19653821e-01 -6.66311741e-01 -9.07636881e-02 -2.18260422e-01 -3.06280196e-01 2.70044357e-01 4.85490821e-02 7.13352263e-01 -1.47174144e+00 2.55079627e-01 5.17380357e-01 1.06910193e+00 -6.83120668e-01 4.52206463e-01 5.48453033e-01 4.86151874e-01 -4.17499185e-01 -1.11343467e+00 8.37711155e-01 -4.65737144e-03 -1.91696808e-01 9.91661623e-02 5.34420192e-01 -7.33681381e-01 -5.29785693e-01 1.33094895e+00 3.34016472e-01 -2.22931117e-01 7.74734169e-02 -1.75081468e+00 -6.68684602e-01 1.69510230e-01 -7.67025471e-01 -5.24077676e-02 3.51302117e-01 2.62355477e-01 7.67345011e-01 -4.24876958e-01 5.31600535e-01 8.83388221e-01 4.82293695e-01 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b5f32005-a69a-44aa-bf37-30bbcbef8a44	sim-to-real-via-sim-to-seg-end-to-end-off	2210.14721	null	https://arxiv.org/abs/2210.14721v1	https://arxiv.org/pdf/2210.14721v1.pdf	Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Data	Autonomous driving is complex, requiring sophisticated 3D scene understanding, localization, mapping, and control. Rather than explicitly modelling and fusing each of these components, we instead consider an end-to-end approach via reinforcement learning (RL). However, collecting exploration driving data in the real world is impractical and dangerous. While training in simulation and deploying visual sim-to-real techniques has worked well for robot manipulation, deploying beyond controlled workspace viewpoints remains a challenge. In this paper, we address this challenge by presenting Sim2Seg, a re-imagining of RCAN that crosses the visual reality gap for off-road autonomous driving, without using any real-world data. This is done by learning to translate randomized simulation images into simulated segmentation and depth maps, subsequently enabling real-world images to also be translated. This allows us to train an end-to-end RL policy in simulation, and directly deploy in the real-world. Our approach, which can be trained in 48 hours on 1 GPU, can perform equally as well as a classical perception and control stack that took thousands of engineering hours over several months to build. We hope this work motivates future end-to-end autonomous driving research.	['Stephen James', 'Pieter Abbeel', 'Ali Agha-mohammadi', 'Rohan Thakker', 'Jeffrey Edlund', 'Sunggoo Jung', 'Amber Xie', 'John So']	2022-10-25	null	null	null	null	['robot-manipulation']	['robots']	[ 1.00369751e-01 3.91557813e-01 3.93190771e-01 -4.02852863e-01 -6.85424089e-01 -8.83444548e-01 6.92623377e-01 -6.58041909e-02 -7.04904735e-01 6.58223867e-01 -4.84405071e-01 -9.07764375e-01 3.02417099e-01 -8.52678776e-01 -1.18054724e+00 -3.43060195e-01 -3.71681899e-01 9.06442761e-01 5.07940590e-01 -6.17002726e-01 2.86257297e-01 5.35461307e-01 -2.05049539e+00 -2.68294781e-01 7.69026697e-01 6.57673776e-01 6.41222715e-01 1.27924442e+00 1.82508573e-01 7.96367586e-01 -3.31985295e-01 3.27593982e-01 5.19650221e-01 -1.13234065e-01 -4.84415323e-01 4.33369689e-02 4.42217022e-01 -5.06724954e-01 -2.67105907e-01 6.01450622e-01 5.93229830e-01 2.18061581e-01 1.57024130e-01 -1.57983422e+00 1.08457156e-01 -1.09670594e-01 -3.32594365e-01 -1.99257180e-01 4.04518306e-01 9.78587687e-01 2.19379202e-01 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c21e2d5e-d304-464e-9906-e1c6305f44c7	clipscore-a-reference-free-evaluation-metric	2104.08718	null	https://arxiv.org/abs/2104.08718v3	https://arxiv.org/pdf/2104.08718v3.pdf	CLIPScore: A Reference-free Evaluation Metric for Image Captioning	Image captioning has conventionally relied on reference-based automatic evaluations, where machine captions are compared against captions written by humans. This is in contrast to the reference-free manner in which humans assess caption quality. In this paper, we report the surprising empirical finding that CLIP (Radford et al., 2021), a cross-modal model pretrained on 400M image+caption pairs from the web, can be used for robust automatic evaluation of image captioning without the need for references. Experiments spanning several corpora demonstrate that our new reference-free metric, CLIPScore, achieves the highest correlation with human judgements, outperforming existing reference-based metrics like CIDEr and SPICE. Information gain experiments demonstrate that CLIPScore, with its tight focus on image-text compatibility, is complementary to existing reference-based metrics that emphasize text-text similarities. Thus, we also present a reference-augmented version, RefCLIPScore, which achieves even higher correlation. Beyond literal description tasks, several case studies reveal domains where CLIPScore performs well (clip-art images, alt-text rating), but also where it is relatively weaker in comparison to reference-based metrics, e.g., news captions that require richer contextual knowledge.	['Yejin Choi', 'Ronan Le Bras', 'Maxwell Forbes', 'Ari Holtzman', 'Jack Hessel']	2021-04-18	null	https://aclanthology.org/2021.emnlp-main.595	https://aclanthology.org/2021.emnlp-main.595.pdf	emnlp-2021-11	['human-judgment-correlation', 'human-judgment-classification']	['reasoning', 'reasoning']	[ 5.80591977e-01 -9.10231471e-02 -3.12521130e-01 -4.27884489e-01 -1.61893356e+00 -9.75919545e-01 1.13492882e+00 3.30483049e-01 -5.84789991e-01 6.77356839e-01 6.82481945e-01 -1.23370215e-01 2.98780389e-02 -1.18504718e-01 -7.42439926e-01 -2.22299397e-01 2.72973627e-01 4.06764418e-01 4.93058227e-02 -3.59977514e-01 4.59634930e-01 6.11509867e-02 -1.61859572e+00 5.96310019e-01 6.77119553e-01 1.13876140e+00 1.99792758e-01 5.50391436e-01 -1.29239693e-01 8.20707083e-01 -5.24121344e-01 -1.09888721e+00 5.66665120e-02 -7.02553332e-01 -8.99714410e-01 5.08663654e-02 1.24470031e+00 -2.18166456e-01 -1.09709360e-01 1.10089159e+00 4.29761648e-01 -1.78174689e-01 6.17748201e-01 -1.31549478e+00 -1.34961033e+00 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3958e9f4-e9f2-4c27-b367-cffce310192b	an-analysis-of-the-effect-of-emotional-speech	null	null	https://aclanthology.org/W18-5044	https://aclanthology.org/W18-5044.pdf	An Analysis of the Effect of Emotional Speech Synthesis on Non-Task-Oriented Dialogue System	This paper explores the effect of emotional speech synthesis on a spoken dialogue system when the dialogue is non-task-oriented. Although the use of emotional speech responses have been shown to be effective in a limited domain, e.g., scenario-based and counseling dialogue, the effect is still not clear in the non-task-oriented dialogue such as voice chatting. For this purpose, we constructed a simple dialogue system with example- and rule-based dialogue management. In the system, two types of emotion labeling with emotion estimation are adopted, i.e., system-driven and user-cooperative emotion labeling. We conducted a dialogue experiment where subjects evaluate the subjective quality of the system and the dialogue from the multiple aspects such as richness of the dialogue and impression of the agent. We then analyze and discuss the results and show the advantage of using appropriate emotions for the expressive speech responses in the non-task-oriented system.	['Mai Yamanaka', 'Akinori Ito', 'Taketo Kase', 'Takashi Nose', 'Yuya Chiba']	2018-07-01	null	null	null	ws-2018-7	['emotional-speech-synthesis']	['speech']	[-4.45809066e-01 6.29866540e-01 3.89860809e-01 -8.34713697e-01 -1.99780524e-01 -4.75419313e-01 6.66710615e-01 -1.41455829e-01 -3.07291240e-01 9.51145649e-01 5.11273146e-01 -7.52109587e-02 2.05537234e-03 -3.87124449e-01 2.57519543e-01 -3.97113651e-01 2.83446819e-01 4.41253185e-01 1.01887479e-01 -7.52901793e-01 3.69198650e-01 3.97321731e-01 -1.48881471e+00 4.37654048e-01 9.38409388e-01 6.45887256e-01 2.24833325e-01 8.55301976e-01 -7.64319479e-01 1.07402670e+00 -1.19842005e+00 -9.28686634e-02 -2.20158637e-01 -9.15328026e-01 -9.32906806e-01 5.35673916e-01 -6.44518375e-01 -4.05360281e-01 -1.44665744e-02 8.13448131e-01 8.81082177e-01 2.74145484e-01 4.78764057e-01 -1.46604979e+00 -1.76588483e-02 4.15446997e-01 1.59837857e-01 -7.47802183e-02 8.69577050e-01 1.28083691e-01 3.14573050e-01 -4.01449174e-01 7.06551194e-01 1.62177181e+00 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e68e2d3b-a2e4-4404-b122-bf10101b3b71	gollic-learning-global-context-beyond-patches	2210.03301	null	https://arxiv.org/abs/2210.03301v1	https://arxiv.org/pdf/2210.03301v1.pdf	GOLLIC: Learning Global Context beyond Patches for Lossless High-Resolution Image Compression	Neural-network-based approaches recently emerged in the field of data compression and have already led to significant progress in image compression, especially in achieving a higher compression ratio. In the lossless image compression scenario, however, existing methods often struggle to learn a probability model of full-size high-resolution images due to the limitation of the computation source. The current strategy is to crop high-resolution images into multiple non-overlapping patches and process them independently. This strategy ignores long-term dependencies beyond patches, thus limiting modeling performance. To address this problem, we propose a hierarchical latent variable model with a global context to capture the long-term dependencies of high-resolution images. Besides the latent variable unique to each patch, we introduce shared latent variables between patches to construct the global context. The shared latent variables are extracted by a self-supervised clustering module inside the model's encoder. This clustering module assigns each patch the confidence that it belongs to any cluster. Later, shared latent variables are learned according to latent variables of patches and their confidence, which reflects the similarity of patches in the same cluster and benefits the global context modeling. Experimental results show that our global context model improves compression ratio compared to the engineered codecs and deep learning models on three benchmark high-resolution image datasets, DIV2K, CLIC.pro, and CLIC.mobile.	['Jie Sun', 'Yang Xiang', 'Zhaoyi Sun', 'Liang Qin', 'Yuan Lan']	2022-10-07	null	null	null	null	['data-compression']	['time-series']	[ 4.66891706e-01 -1.44629255e-01 -4.32319522e-01 -3.08255404e-01 -7.72864997e-01 2.19392315e-01 7.06834868e-02 1.40317142e-01 -2.05390602e-01 6.07315361e-01 2.71670550e-01 2.14350641e-01 -3.14902216e-01 -1.12942207e+00 -6.79059625e-01 -8.96079659e-01 -5.62262125e-02 2.72991985e-01 7.72778364e-03 4.98588920e-01 1.19478054e-01 1.34366274e-01 -1.67856181e+00 5.12145996e-01 8.81103873e-01 1.05795634e+00 9.30471539e-01 4.35464263e-01 -5.86403497e-02 9.13610280e-01 -3.41080934e-01 -9.10840854e-02 3.30548473e-02 -3.54526997e-01 -7.18017697e-01 2.33345568e-01 1.54705375e-01 -4.00590569e-01 -5.53056836e-01 1.10456109e+00 2.82847077e-01 -1.01154946e-01 4.75146562e-01 -8.98817778e-01 -7.43889213e-01 6.13855481e-01 -8.92569125e-01 7.28510469e-02 -2.18587801e-01 -2.65981138e-01 1.16460216e+00 -5.47064185e-01 6.27476633e-01 1.11141121e+00 4.49222147e-01 2.59877920e-01 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18cde0ec-3c33-4e28-b21e-e691c99c2043	multimodal-dataset-from-harsh-sub-terranean	2304.14520	null	https://arxiv.org/abs/2304.14520v2	https://arxiv.org/pdf/2304.14520v2.pdf	Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration	Algorithms for autonomous navigation in environments without Global Navigation Satellite System (GNSS) coverage mainly rely on onboard perception systems. These systems commonly incorporate sensors like cameras and Light Detection and Rangings (LiDARs), the performance of which may degrade in the presence of aerosol particles. Thus, there is a need of fusing acquired data from these sensors with data from Radio Detection and Rangings (RADARs) which can penetrate through such particles. Overall, this will improve the performance of localization and collision avoidance algorithms under such environmental conditions. This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles. A detailed description of the onboard sensors and the environment, where the dataset is collected are presented to enable full evaluation of acquired data. Furthermore, the dataset contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format to facilitate the evaluation of navigation, and localization algorithms in such environments. In contrast to the existing datasets, the focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data. Therefore, to validate the dataset, a preliminary comparison of odometry from onboard LiDARs is presented.	['George Nikolakopoulos', 'Anton Koval', 'Vignesh Kottayam Viswanathan', 'Nikolaos Stathoulopoulos', 'Niklas Dahlquist', 'Alexander Kyuroson']	2023-04-27	null	null	null	null	['autonomous-navigation']	['computer-vision']	[ 2.77830541e-01 -6.36450768e-01 4.71307635e-01 -3.77988726e-01 -2.21300855e-01 -8.32316339e-01 4.29133117e-01 2.80831367e-01 -8.44920218e-01 1.04049551e+00 -5.34717739e-01 -2.00550139e-01 -4.43769753e-01 -1.26750350e+00 -5.07262707e-01 -8.21142495e-01 -2.16279969e-01 6.01162672e-01 5.53261399e-01 -6.00366354e-01 1.05661191e-01 9.39813256e-01 -2.12196636e+00 -3.76105279e-01 9.21964169e-01 7.88780689e-01 8.63124073e-01 9.03838456e-01 -1.63121074e-01 -1.42011940e-01 -5.93165457e-01 5.21421194e-01 4.87245828e-01 7.04115555e-02 5.39831072e-02 -2.16166124e-01 5.97993672e-01 -2.11939394e-01 2.99339890e-02 1.01535201e+00 7.89556742e-01 2.67896324e-01 4.95191306e-01 -1.16648531e+00 1.52870476e-01 -7.81956464e-02 -2.26945832e-01 2.88788110e-01 4.47706193e-01 1.61000177e-01 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9684b5b9-bad1-457d-bd27-c73288a73c6f	learning-the-precise-feature-for-cluster	2106.06159	null	https://arxiv.org/abs/2106.06159v1	https://arxiv.org/pdf/2106.06159v1.pdf	Learning the Precise Feature for Cluster Assignment	Clustering is one of the fundamental tasks in computer vision and pattern recognition. Recently, deep clustering methods (algorithms based on deep learning) have attracted wide attention with their impressive performance. Most of these algorithms combine deep unsupervised representation learning and standard clustering together. However, the separation of representation learning and clustering will lead to suboptimal solutions because the two-stage strategy prevents representation learning from adapting to subsequent tasks (e.g., clustering according to specific cues). To overcome this issue, efforts have been made in the dynamic adaption of representation and cluster assignment, whereas current state-of-the-art methods suffer from heuristically constructed objectives with representation and cluster assignment alternatively optimized. To further standardize the clustering problem, we audaciously formulate the objective of clustering as finding a precise feature as the cue for cluster assignment. Based on this, we propose a general-purpose deep clustering framework which radically integrates representation learning and clustering into a single pipeline for the first time. The proposed framework exploits the powerful ability of recently developed generative models for learning intrinsic features, and imposes an entropy minimization on the distribution of the cluster assignment by a dedicated variational algorithm. Experimental results show that the performance of the proposed method is superior, or at least comparable to, the state-of-the-art methods on the handwritten digit recognition, fashion recognition, face recognition and object recognition benchmark datasets.	['Junyu Dong', 'Feng Gao', 'Huiyu Zhou', 'Xinghui Dong', 'Yanhai Gan']	2021-06-11	null	null	null	null	['handwritten-digit-recognition']	['computer-vision']	[ 1.34662271e-01 -3.64881605e-01 -2.11361088e-02 -4.39783901e-01 -6.82416916e-01 -3.73493463e-01 8.11947823e-01 3.74103487e-02 -3.38490367e-01 1.45460770e-01 -4.91381884e-02 2.40767553e-01 -4.97306257e-01 -5.03147721e-01 -3.67401719e-01 -1.26044297e+00 2.03538269e-01 8.24465990e-01 -1.30735844e-01 2.45842427e-01 4.03086841e-01 4.55532074e-01 -1.92701364e+00 2.27023467e-01 1.02849233e+00 9.23146188e-01 4.08232361e-01 1.86337113e-01 -2.50616938e-01 4.95373607e-01 -4.05071527e-01 -1.96917787e-01 7.25695770e-03 -5.77991009e-01 -4.93343860e-01 6.62974596e-01 -5.29489219e-02 3.34909528e-01 -3.12727123e-01 1.15210354e+00 4.73368496e-01 5.11301458e-01 1.00832820e+00 -1.02593374e+00 -8.51468086e-01 5.85382223e-01 -8.04169714e-01 -8.72785449e-02 -1.33117393e-01 -1.86489057e-02 8.39464903e-01 -8.95711720e-01 4.84604925e-01 1.09857190e+00 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00cf00e2-4548-44d6-9199-b674e6324429	decipherment-complexity-in-11-substitution	null	null	https://aclanthology.org/P13-1060	https://aclanthology.org/P13-1060.pdf	Decipherment Complexity in 1:1 Substitution Ciphers	null	['Hermann Ney', 'Malte Nuhn']	2013-08-01	null	null	null	acl-2013-8	['decipherment']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 -2.09758401e-01 9.22211111e-01 5.37373662e-01 4.49454933e-02 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30430e4e-52a4-4a9f-88aa-8d56af44dc5f	representation-compression-and-generalization	null	null	https://openreview.net/forum?id=SkeL6sCqK7	https://openreview.net/pdf?id=SkeL6sCqK7	REPRESENTATION COMPRESSION AND GENERALIZATION IN DEEP NEURAL NETWORKS	Understanding the groundbreaking performance of Deep Neural Networks is one of the greatest challenges to the scientific community today. In this work, we introduce an information theoretic viewpoint on the behavior of deep networks optimization processes and their generalization abilities. By studying the Information Plane, the plane of the mutual information between the input variable and the desired label, for each hidden layer. Specifically, we show that the training of the network is characterized by a rapid increase in the mutual information (MI) between the layers and the target label, followed by a longer decrease in the MI between the layers and the input variable. Further, we explicitly show that these two fundamental information-theoretic quantities correspond to the generalization error of the network, as a result of introducing a new generalization bound that is exponential in the representation compression. The analysis focuses on typical patterns of large-scale problems. For this purpose, we introduce a novel analytic bound on the mutual information between consecutive layers in the network. An important consequence of our analysis is a super-linear boost in training time with the number of non-degenerate hidden layers, demonstrating the computational benefit of the hidden layers.	['Ravid Shwartz-Ziv', 'Naftali Tishby', 'Amichai Painsky']	2019-05-01	null	null	null	iclr-2019-5	['information-plane']	['methodology']	[ 2.34979644e-01 4.07235861e-01 1.13222815e-01 -2.36076593e-01 -1.10053360e-01 -4.34296638e-01 3.64764303e-01 2.93148249e-01 -6.59425557e-01 5.06065726e-01 -3.19686644e-02 -1.04924865e-01 -5.83536744e-01 -7.02661455e-01 -7.21206248e-01 -1.15542722e+00 -3.16262841e-01 3.76380205e-01 -1.66534573e-01 -6.03798814e-02 2.13176355e-01 6.63137972e-01 -1.44095361e+00 -6.74235672e-02 5.52557707e-01 1.47774863e+00 -3.54070142e-02 6.68427706e-01 3.32292356e-02 3.29977244e-01 -4.61598277e-01 -4.03412014e-01 4.32609320e-01 -5.11020124e-01 -9.49508190e-01 4.10274079e-04 5.83077110e-02 2.08446905e-01 -3.53314161e-01 1.31985784e+00 3.81714374e-01 2.38523841e-01 7.95655668e-01 -1.14391410e+00 -2.60635376e-01 7.31208980e-01 -3.52875233e-01 3.38045806e-01 -4.55353200e-01 -1.67358354e-01 1.28496742e+00 -4.21796620e-01 5.14812946e-01 8.11851144e-01 5.65288365e-01 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f1ca3986-ec3b-44a4-8058-7f6cbcc2a0d6	accelerating-diffusion-models-via-pre	2210.17408	null	https://arxiv.org/abs/2210.17408v1	https://arxiv.org/pdf/2210.17408v1.pdf	Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation	Based on the Denoising Diffusion Probabilistic Model (DDPM), medical image segmentation can be described as a conditional image generation task, which allows to compute pixel-wise uncertainty maps of the segmentation and allows an implicit ensemble of segmentations to boost the segmentation performance. However, DDPM requires many iterative denoising steps to generate segmentations from Gaussian noise, resulting in extremely inefficient inference. To mitigate the issue, we propose a principled acceleration strategy, called pre-segmentation diffusion sampling DDPM (PD-DDPM), which is specially used for medical image segmentation. The key idea is to obtain pre-segmentation results based on a separately trained segmentation network, and construct noise predictions (non-Gaussian distribution) according to the forward diffusion rule. We can then start with noisy predictions and use fewer reverse steps to generate segmentation results. Experiments show that PD-DDPM yields better segmentation results over representative baseline methods even if the number of reverse steps is significantly reduced. Moreover, PD-DDPM is orthogonal to existing advanced segmentation models, which can be combined to further improve the segmentation performance.	['Ting Ma', 'Yang Xiang', 'Shang Lu', 'Chenfei Ye', 'Yanwu Yang', 'Xutao Guo']	2022-10-27	null	null	null	null	['conditional-image-generation']	['computer-vision']	[ 5.67296267e-01 4.81301278e-01 1.66616306e-01 -2.74877906e-01 -9.20478702e-01 -2.41449729e-01 5.02212703e-01 -8.19336772e-02 -4.83448565e-01 4.26670820e-01 -3.22457217e-02 -3.74953538e-01 1.82713866e-01 -1.05743384e+00 -5.48570752e-01 -1.03339112e+00 4.02947396e-01 5.15773118e-01 7.30444014e-01 2.46774957e-01 1.10267513e-01 4.13330346e-01 -9.84477699e-01 4.54919003e-02 1.41810608e+00 7.85210907e-01 5.63608706e-01 5.82191706e-01 -3.53854626e-01 4.27784979e-01 -4.21113223e-01 -3.97801220e-01 1.93882063e-01 -5.95388412e-01 -8.40233982e-01 4.27556425e-01 -2.75239617e-01 -3.70116681e-01 1.03204288e-01 1.43885565e+00 4.01520967e-01 2.17129901e-01 9.45799947e-01 -6.76187396e-01 -4.67530131e-01 7.26397991e-01 -8.60565722e-01 -2.77450621e-01 -7.30708987e-02 1.29216388e-01 5.45741200e-01 -6.26856208e-01 6.35863185e-01 1.27868044e+00 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7138dd64-5405-41b5-9fe2-c508f5b39a4f	learning-self-modulating-attention-in	2204.06517	null	https://arxiv.org/abs/2204.06517v1	https://arxiv.org/pdf/2204.06517v1.pdf	Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation	User interests are usually dynamic in the real world, which poses both theoretical and practical challenges for learning accurate preferences from rich behavior data. Among existing user behavior modeling solutions, attention networks are widely adopted for its effectiveness and relative simplicity. Despite being extensively studied, existing attentions still suffer from two limitations: i) conventional attentions mainly take into account the spatial correlation between user behaviors, regardless the distance between those behaviors in the continuous time space; and ii) these attentions mostly provide a dense and undistinguished distribution over all past behaviors then attentively encode them into the output latent representations. This is however not suitable in practical scenarios where a user's future actions are relevant to a small subset of her/his historical behaviors. In this paper, we propose a novel attention network, named self-modulating attention, that models the complex and non-linearly evolving dynamic user preferences. We empirically demonstrate the effectiveness of our method on top-N sequential recommendation tasks, and the results on three large-scale real-world datasets show that our model can achieve state-of-the-art performance.	['Xiaokang Yang', 'Jianping Yu', 'Daiyue Xue', 'Junchi Yan', 'Nianzu Yang', 'Haoyu Geng', 'Chao Chen']	2022-03-30	null	null	null	null	['dynamic-link-prediction']	['graphs']	[-6.71436265e-02 -7.23646998e-01 -5.84631145e-01 -5.29082179e-01 -1.93440720e-01 -1.85061350e-01 3.27887982e-01 -3.86041552e-02 -3.39164317e-01 4.61262107e-01 7.18419313e-01 -1.70922577e-01 -4.44902927e-01 -5.94016731e-01 -4.49786395e-01 -5.59392869e-01 -2.99970418e-01 5.41317403e-01 -1.73446182e-02 -3.31812412e-01 1.50979340e-01 1.28068730e-01 -1.56272686e+00 1.83276478e-02 1.11968756e+00 1.10384250e+00 2.17020497e-01 4.59424615e-01 6.29873760e-03 9.46853518e-01 -2.16158658e-01 -2.43960351e-01 -8.25568363e-02 -2.07633525e-01 -5.66937149e-01 -9.67269298e-03 6.48821592e-02 -6.56077623e-01 -7.68172801e-01 8.91580999e-01 1.88675776e-01 6.93424582e-01 6.41177356e-01 -1.19221961e+00 -1.48878407e+00 6.01186395e-01 -5.64107656e-01 5.36756694e-01 2.22929910e-01 -8.60966891e-02 1.18508589e+00 -4.80071306e-01 1.89420558e-03 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-8.48768473e-01 -8.41212511e-01 -1.07824969e+00 -6.70827270e-01 8.49995434e-01 5.31701028e-01 -8.12367082e-01 -2.55773515e-01 -1.47654027e-01]	[10.085731506347656, 5.539830684661865]
8e892ff2-e26c-42de-bc79-5056c3682944	a-multi-objective-deep-reinforcement-learning	1803.02965	null	https://arxiv.org/abs/1803.02965v3	https://arxiv.org/pdf/1803.02965v3.pdf	A Multi-Objective Deep Reinforcement Learning Framework	This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks. We develop a high-performance MODRL framework that supports both single-policy and multi-policy strategies, as well as both linear and non-linear approaches to action selection. The experimental results on two benchmark problems (two-objective deep sea treasure environment and three-objective Mountain Car problem) indicate that the proposed framework is able to find the Pareto-optimal solutions effectively. The proposed framework is generic and highly modularized, which allows the integration of different deep reinforcement learning algorithms in different complex problem domains. This therefore overcomes many disadvantages involved with standard multi-objective reinforcement learning methods in the current literature. The proposed framework acts as a testbed platform that accelerates the development of MODRL for solving increasingly complicated multi-objective problems.	['Saeid Nahavandi', 'Peter Vamplew', 'Ngoc Duy Nguyen', 'Chee Peng Lim', 'Thanh Thi Nguyen', 'Richard Dazeley']	2018-03-08	null	null	null	null	['multi-objective-reinforcement-learning']	['methodology']	[-5.83463132e-01 -4.45407689e-01 -2.60904521e-01 -1.51643530e-01 -7.07133353e-01 -3.94876122e-01 3.33350599e-01 1.61500230e-01 -9.61698413e-01 1.47804236e+00 -2.51835525e-01 -1.60541072e-01 -7.06488669e-01 -1.19695652e+00 -5.03908336e-01 -9.89161015e-01 -4.75324064e-01 7.73784637e-01 1.33303180e-01 -8.36825788e-01 5.49761415e-01 4.51227218e-01 -1.79080808e+00 -2.50526607e-01 9.53626931e-01 9.24308240e-01 3.65724564e-01 6.95408583e-01 -1.08061627e-01 5.49542069e-01 -8.14701855e-01 -2.81176600e-03 3.07809770e-01 -2.00397730e-01 -7.40632355e-01 -2.32073963e-01 -2.35377818e-01 -3.14846963e-01 2.57525921e-01 7.23446429e-01 8.56529593e-01 6.10271573e-01 3.01154941e-01 -1.47954190e+00 -3.04110408e-01 4.58630949e-01 -5.32657385e-01 3.76122683e-01 1.79883346e-01 4.40194547e-01 1.31885850e+00 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9.27190900e-01 -7.52340615e-01 -9.46080387e-01 -7.59402871e-01 -6.70090079e-01 -1.70721337e-01 3.23384225e-01 -1.10452020e+00 5.56127774e-03 -9.87057447e-01]	[4.003037929534912, 2.1129939556121826]
351db756-c3f0-4d9e-a30d-b2e57173401d	large-scale-multi-view-subspace-clustering-in	1911.09290	null	https://arxiv.org/abs/1911.09290v1	https://arxiv.org/pdf/1911.09290v1.pdf	Large-scale Multi-view Subspace Clustering in Linear Time	A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years. Researchers manage to boost clustering accuracy from different points of view. However, many state-of-the-art MVSC algorithms, typically have a quadratic or even cubic complexity, are inefficient and inherently difficult to apply at large scales. In the era of big data, the computational issue becomes critical. To fill this gap, we propose a large-scale MVSC (LMVSC) algorithm with linear order complexity. Inspired by the idea of anchor graph, we first learn a smaller graph for each view. Then, a novel approach is designed to integrate those graphs so that we can implement spectral clustering on a smaller graph. Interestingly, it turns out that our model also applies to single-view scenario. Extensive experiments on various large-scale benchmark data sets validate the effectiveness and efficiency of our approach with respect to state-of-the-art clustering methods.	['Zhitong Zhao', 'Zenglin Xu', 'Zhao Kang', 'Meng Han', 'Wangtao Zhou', 'Junming Shao']	2019-11-21	null	null	null	null	['multi-view-subspace-clustering']	['computer-vision']	[-1.48633882e-01 -2.00208843e-01 -8.93761218e-02 -9.85134244e-02 -7.64680505e-01 -7.34263957e-01 3.66443396e-01 9.33774635e-02 8.88537392e-02 2.83830553e-01 1.22378543e-01 -8.68847035e-03 -3.92878592e-01 -5.92960775e-01 -5.37339687e-01 -8.83853078e-01 -3.82690951e-02 4.68496978e-01 5.06261170e-01 3.44013236e-02 1.33068830e-01 1.65557370e-01 -1.35686696e+00 1.64365023e-01 8.69101524e-01 6.26721621e-01 1.94535270e-01 3.11611950e-01 4.46073711e-02 3.65427494e-01 -7.59620070e-02 -3.70869964e-01 3.73526901e-01 -3.90543401e-01 -7.04068065e-01 5.01689315e-01 3.83031607e-01 1.76326200e-01 -1.64495140e-01 1.09663033e+00 5.52724481e-01 1.97228994e-02 3.27814698e-01 -1.32761979e+00 -4.34291124e-01 4.97106671e-01 -8.96137476e-01 -5.17180488e-02 3.65645498e-01 -1.99673712e-01 1.03094840e+00 -9.66187537e-01 7.96264350e-01 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fb463d3e-7641-4462-ba21-6710513f9e1f	feature-informed-latent-space-regularization	2203.09132	null	https://arxiv.org/abs/2203.09132v2	https://arxiv.org/pdf/2203.09132v2.pdf	Feature-informed Latent Space Regularization for Music Source Separation	The integration of additional side information to improve music source separation has been investigated numerous times, e.g., by adding features to the input or by adding learning targets in a multi-task learning scenario. These approaches, however, require additional annotations such as musical scores, instrument labels, etc. in training and possibly during inference. The available datasets for source separation do not usually provide these additional annotations. In this work, we explore transfer learning strategies to incorporate VGGish features with a state-of-the-art source separation model; VGGish features are known to be a very condensed representation of audio content and have been successfully used in many MIR tasks. We introduce three approaches to incorporate the features, including two latent space regularization methods and one naive concatenation method. Experimental results show that our proposed approaches improve several evaluation metrics for music source separation.	['Alexander Lerch', 'Yun-Ning Hung']	2022-03-17	null	null	null	null	['music-source-separation']	['music']	[ 5.75417519e-01 -2.32268289e-01 -2.33387664e-01 -2.61714756e-01 -1.45959973e+00 -7.20895171e-01 4.92734939e-01 1.60815328e-01 -2.35135615e-01 6.87567055e-01 4.49502021e-01 2.81669736e-01 -5.57935655e-01 -3.53214890e-01 -5.88996410e-01 -9.21597421e-01 7.60631487e-02 2.08248630e-01 4.29536738e-02 -4.70022261e-02 2.15456188e-01 -4.46753949e-03 -1.73121750e+00 5.01396418e-01 9.77903843e-01 8.78913522e-01 1.59626514e-01 6.57535791e-01 -1.69680506e-01 6.05270863e-01 -5.92179120e-01 -3.12163293e-01 3.11749160e-01 -7.14970231e-01 -6.27414942e-01 -1.29791543e-01 3.67296010e-01 1.24716736e-01 2.22872883e-01 9.51936066e-01 8.71112287e-01 2.75981426e-01 7.09487736e-01 -1.27086556e+00 -1.93664283e-01 1.25843370e+00 -6.11032546e-01 6.06685039e-03 4.33156699e-01 -4.62086082e-01 1.18385398e+00 -9.18777406e-01 1.44813478e-01 1.00679529e+00 7.22321749e-01 8.48274529e-02 -1.25255692e+00 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9671c80c-b47f-43b7-8928-f97f659ee546	cigar-cross-modality-graph-reasoning-for	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Liu_CIGAR_Cross-Modality_Graph_Reasoning_for_Domain_Adaptive_Object_Detection_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_CIGAR_Cross-Modality_Graph_Reasoning_for_Domain_Adaptive_Object_Detection_CVPR_2023_paper.pdf	CIGAR: Cross-Modality Graph Reasoning for Domain Adaptive Object Detection	Unsupervised domain adaptive object detection (UDA-OD) aims to learn a detector by generalizing knowledge from a labeled source domain to an unlabeled target domain. Though the existing graph-based methods for UDA-OD perform well in some cases, they cannot learn a proper node set for the graph. In addition, these methods build the graph solely based on the visual features and do not consider the linguistic knowledge carried by the semantic prototypes, e.g., dataset labels. To overcome these problems, we propose a cross-modality graph reasoning adaptation (CIGAR) method to take advantage of both visual and linguistic knowledge. Specifically, our method performs cross-modality graph reasoning between the linguistic modality graph and visual modality graphs to enhance their representations. We also propose a discriminative feature selector to find the most discriminative features and take them as the nodes of the visual graph for both efficiency and effectiveness. In addition, we employ the linguistic graph matching loss to regulate the update of linguistic graphs and maintain their semantic representation during the training process. Comprehensive experiments validate the effectiveness of our proposed CIGAR.	['Yong Xu', 'YaoWei Wang', 'Chao Huang', 'Jinghua Wang', 'Yabo Liu']	2023-01-01	null	null	null	cvpr-2023-1	['graph-matching']	['graphs']	[-8.50614682e-02 7.59535329e-03 -4.31287348e-01 -5.12071073e-01 -3.21407765e-01 -4.69617546e-01 5.25306165e-01 2.79197961e-01 -1.91025019e-01 1.94559127e-01 7.02298656e-02 -1.04042431e-02 -1.89928681e-01 -9.38102365e-01 -5.18245101e-01 -6.56836808e-01 4.56755966e-01 3.67753506e-01 5.87195039e-01 -5.86426482e-02 -9.59562603e-04 3.32058072e-01 -1.27361643e+00 1.62520736e-01 1.02710629e+00 9.36764300e-01 2.91445851e-01 -9.49976891e-02 -4.48262066e-01 8.20593178e-01 -2.25330219e-01 -3.35339725e-01 1.52803317e-01 -6.25268102e-01 -4.94475454e-01 5.39379358e-01 3.05659622e-01 -8.60580653e-02 -5.47457039e-01 1.44034529e+00 4.06255901e-01 4.40537035e-01 6.51682436e-01 -1.62113261e+00 -1.02570474e+00 6.09697521e-01 -6.63287163e-01 1.18488349e-01 2.64738321e-01 2.57029459e-02 1.01907301e+00 -1.06924701e+00 8.16637158e-01 1.51689100e+00 4.08506840e-01 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f5361a28-27a2-472b-85d3-9149c0ff3eac	arkittrack-a-new-diverse-dataset-for-tracking	2303.13885	null	https://arxiv.org/abs/2303.13885v1	https://arxiv.org/pdf/2303.13885v1.pdf	ARKitTrack: A New Diverse Dataset for Tracking Using Mobile RGB-D Data	Compared with traditional RGB-only visual tracking, few datasets have been constructed for RGB-D tracking. In this paper, we propose ARKitTrack, a new RGB-D tracking dataset for both static and dynamic scenes captured by consumer-grade LiDAR scanners equipped on Apple's iPhone and iPad. ARKitTrack contains 300 RGB-D sequences, 455 targets, and 229.7K video frames in total. Along with the bounding box annotations and frame-level attributes, we also annotate this dataset with 123.9K pixel-level target masks. Besides, the camera intrinsic and camera pose of each frame are provided for future developments. To demonstrate the potential usefulness of this dataset, we further present a unified baseline for both box-level and pixel-level tracking, which integrates RGB features with bird's-eye-view representations to better explore cross-modality 3D geometry. In-depth empirical analysis has verified that the ARKitTrack dataset can significantly facilitate RGB-D tracking and that the proposed baseline method compares favorably against the state of the arts. The code and dataset is available at https://arkittrack.github.io.	['Huchuan Lu', 'Lijun Wang', 'Junsong Chen', 'Haojie Zhao']	2023-03-24	null	http://openaccess.thecvf.com//content/CVPR2023/html/Zhao_ARKitTrack_A_New_Diverse_Dataset_for_Tracking_Using_Mobile_RGB-D_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Zhao_ARKitTrack_A_New_Diverse_Dataset_for_Tracking_Using_Mobile_RGB-D_CVPR_2023_paper.pdf	cvpr-2023-1	['visual-tracking']	['computer-vision']	[-1.11431651e-01 -4.74375337e-01 -2.74948686e-01 -2.78169394e-01 -7.85912752e-01 -9.25997734e-01 4.72004592e-01 -4.15140778e-01 -3.06973130e-01 2.55425751e-01 4.57447506e-02 -2.27843359e-01 2.96302348e-01 -3.88724506e-01 -6.27702236e-01 -4.25589770e-01 1.55957624e-01 -1.60640702e-01 4.79536831e-01 9.60115790e-02 3.73683386e-02 9.50105429e-01 -1.78493285e+00 -5.66996187e-02 1.62116125e-01 1.66551220e+00 -3.74118797e-02 7.58870125e-01 1.04372002e-01 4.76212054e-01 -2.49597609e-01 -3.29587132e-01 7.92752147e-01 6.79331133e-05 -1.40574798e-01 1.56585127e-01 1.11734951e+00 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5669cba8-62d2-4636-a9cf-e77325ac9fdf	wspalign-word-alignment-pre-training-via	2306.05644	null	https://arxiv.org/abs/2306.05644v1	https://arxiv.org/pdf/2306.05644v1.pdf	WSPAlign: Word Alignment Pre-training via Large-Scale Weakly Supervised Span Prediction	Most existing word alignment methods rely on manual alignment datasets or parallel corpora, which limits their usefulness. Here, to mitigate the dependence on manual data, we broaden the source of supervision by relaxing the requirement for correct, fully-aligned, and parallel sentences. Specifically, we make noisy, partially aligned, and non-parallel paragraphs. We then use such a large-scale weakly-supervised dataset for word alignment pre-training via span prediction. Extensive experiments with various settings empirically demonstrate that our approach, which is named WSPAlign, is an effective and scalable way to pre-train word aligners without manual data. When fine-tuned on standard benchmarks, WSPAlign has set a new state-of-the-art by improving upon the best-supervised baseline by 3.3~6.1 points in F1 and 1.5~6.1 points in AER. Furthermore, WSPAlign also achieves competitive performance compared with the corresponding baselines in few-shot, zero-shot and cross-lingual tests, which demonstrates that WSPAlign is potentially more practical for low-resource languages than existing methods.	['Yoshimasa Tsuruoka', 'Masaaki Nagata', 'Qiyu Wu']	2023-06-09	null	null	null	null	['word-alignment']	['natural-language-processing']	[ 1.72930375e-01 -2.77692080e-01 -5.35746932e-01 -4.83188808e-01 -1.48265553e+00 -6.71453595e-01 4.42648083e-01 -4.61989045e-02 -6.87545240e-01 8.73561025e-01 5.18815935e-01 -4.31460351e-01 3.23604524e-01 -4.99316752e-01 -6.05232596e-01 -5.40169418e-01 3.93695354e-01 6.97998822e-01 2.27424085e-01 -7.73225188e-01 3.43783796e-01 1.74184386e-02 -1.05520928e+00 1.18267626e-01 1.20418608e+00 1.48162171e-01 2.89367735e-01 4.43678081e-01 -3.34274173e-01 -4.23546471e-02 -5.31128883e-01 -7.57374167e-01 4.07007813e-01 -3.92673045e-01 -9.42353308e-01 -1.85252920e-01 6.65579915e-01 -7.61528686e-02 9.21778753e-02 9.91736650e-01 7.66368270e-01 -9.26889712e-04 3.23132575e-01 -8.49821508e-01 -6.58752024e-01 9.66148257e-01 -8.22349846e-01 3.59644830e-01 5.32729447e-01 2.63006985e-01 1.55173266e+00 -9.47130919e-01 4.92509693e-01 9.60228801e-01 9.43815947e-01 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7a44f954-6dc0-41ae-a0df-04384cb2eef2	learning-granularity-unified-representations	2207.07802	null	https://arxiv.org/abs/2207.07802v1	https://arxiv.org/pdf/2207.07802v1.pdf	Learning Granularity-Unified Representations for Text-to-Image Person Re-identification	Text-to-image person re-identification (ReID) aims to search for pedestrian images of an interested identity via textual descriptions. It is challenging due to both rich intra-modal variations and significant inter-modal gaps. Existing works usually ignore the difference in feature granularity between the two modalities, i.e., the visual features are usually fine-grained while textual features are coarse, which is mainly responsible for the large inter-modal gaps. In this paper, we propose an end-to-end framework based on transformers to learn granularity-unified representations for both modalities, denoted as LGUR. LGUR framework contains two modules: a Dictionary-based Granularity Alignment (DGA) module and a Prototype-based Granularity Unification (PGU) module. In DGA, in order to align the granularities of two modalities, we introduce a Multi-modality Shared Dictionary (MSD) to reconstruct both visual and textual features. Besides, DGA has two important factors, i.e., the cross-modality guidance and the foreground-centric reconstruction, to facilitate the optimization of MSD. In PGU, we adopt a set of shared and learnable prototypes as the queries to extract diverse and semantically aligned features for both modalities in the granularity-unified feature space, which further promotes the ReID performance. Comprehensive experiments show that our LGUR consistently outperforms state-of-the-arts by large margins on both CUHK-PEDES and ICFG-PEDES datasets. Code will be released at https://github.com/ZhiyinShao-H/LGUR.	['Changxing Ding', 'Jian Wang', 'Zhifeng Lin', 'Meng Fang', 'Xinyu Zhang', 'Zhiyin Shao']	2022-07-16	null	null	null	null	['nlp-based-person-retrival']	['computer-vision']	[-2.96109617e-01 -5.66399872e-01 -2.61647731e-01 -4.91202980e-01 -9.39484298e-01 -4.15612847e-01 7.86539435e-01 -1.23112641e-01 -2.96173602e-01 4.75551575e-01 7.00215697e-01 2.55115598e-01 8.66449103e-02 -7.26695359e-01 -5.96462488e-01 -7.55864918e-01 5.60322702e-01 2.86611229e-01 1.34636417e-01 -9.39276665e-02 -1.36178389e-01 1.27125069e-01 -1.74754965e+00 4.40525651e-01 8.33390713e-01 8.98484230e-01 1.98527560e-01 7.15298131e-02 -1.71477363e-01 4.24765557e-01 -2.94901580e-02 -8.05994332e-01 3.34990412e-01 -3.17595184e-01 -5.47289908e-01 3.10480654e-01 6.79451108e-01 -4.11210895e-01 -8.00752997e-01 1.22180760e+00 7.33059764e-01 1.73718408e-01 5.52941859e-01 -1.41923678e+00 -8.14688563e-01 3.21631908e-01 -8.79575193e-01 9.97713357e-02 3.15767825e-01 3.86432469e-01 9.24297512e-01 -1.00488770e+00 5.31060457e-01 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db5f4a7d-3a55-4be8-ae5c-fc62c71371a7	wave-san-wavelet-based-style-augmentation	2203.07656	null	https://arxiv.org/abs/2203.07656v1	https://arxiv.org/pdf/2203.07656v1.pdf	Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning	Previous few-shot learning (FSL) works mostly are limited to natural images of general concepts and categories. These works assume very high visual similarity between the source and target classes. In contrast, the recently proposed cross-domain few-shot learning (CD-FSL) aims at transferring knowledge from general nature images of many labeled examples to novel domain-specific target categories of only a few labeled examples. The key challenge of CD-FSL lies in the huge data shift between source and target domains, which is typically in the form of totally different visual styles. This makes it very nontrivial to directly extend the classical FSL methods to address the CD-FSL task. To this end, this paper studies the problem of CD-FSL by spanning the style distributions of the source dataset. Particularly, wavelet transform is introduced to enable the decomposition of visual representations into low-frequency components such as shape and style and high-frequency components e.g., texture. To make our model robust to visual styles, the source images are augmented by swapping the styles of their low-frequency components with each other. We propose a novel Style Augmentation (StyleAug) module to implement this idea. Furthermore, we present a Self-Supervised Learning (SSL) module to ensure the predictions of style-augmented images are semantically similar to the unchanged ones. This avoids the potential semantic drift problem in exchanging the styles. Extensive experiments on two CD-FSL benchmarks show the effectiveness of our method. Our codes and models will be released.	['Yu-Gang Jiang', 'Jingjing Chen', 'Yanwei Fu', 'Yu Xie', 'Yuqian Fu']	2022-03-15	null	null	null	null	['cross-domain-few-shot', 'cross-domain-few-shot-learning']	['computer-vision', 'computer-vision']	[ 4.35517102e-01 -1.71941787e-01 -1.36859044e-01 -5.42250097e-01 -5.20983040e-01 -6.34754062e-01 7.34421551e-01 -5.78775033e-02 -5.67805693e-02 6.05581284e-01 -2.56677326e-02 2.54864126e-01 1.36925876e-01 -8.58512461e-01 -8.74449670e-01 -6.67928398e-01 3.20169091e-01 1.42168686e-01 4.71784920e-01 -3.52984011e-01 9.03679207e-02 1.32989287e-01 -1.65540421e+00 5.56529284e-01 8.58572245e-01 1.02400231e+00 3.69993776e-01 2.35270023e-01 -5.95635831e-01 4.68696594e-01 -5.33638775e-01 -2.56162703e-01 3.98335695e-01 -7.37398446e-01 -6.41200662e-01 5.74090719e-01 7.24674463e-01 -1.67083040e-01 -7.37675726e-02 1.38994503e+00 3.28263879e-01 3.65939081e-01 7.08755195e-01 -1.51364434e+00 -1.06393516e+00 1.75301537e-01 -7.57623971e-01 -7.73803145e-02 3.79213899e-01 1.64430842e-01 7.48761237e-01 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-9.02888894e-01 -4.96508598e-01 -8.00654411e-01 -1.03332841e+00 1.15210712e+00 3.15056801e-01 -1.01914012e+00 -3.04751635e-01 3.64756316e-01]	[10.101237297058105, 2.708676815032959]
c1a7eb0a-d776-4af1-9a7d-1422128b9d37	flavr-flow-agnostic-video-representations-for	2012.08512	null	https://arxiv.org/abs/2012.08512v3	https://arxiv.org/pdf/2012.08512v3.pdf	FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation	A majority of methods for video frame interpolation compute bidirectional optical flow between adjacent frames of a video, followed by a suitable warping algorithm to generate the output frames. However, approaches relying on optical flow often fail to model occlusions and complex non-linear motions directly from the video and introduce additional bottlenecks unsuitable for widespread deployment. We address these limitations with FLAVR, a flexible and efficient architecture that uses 3D space-time convolutions to enable end-to-end learning and inference for video frame interpolation. Our method efficiently learns to reason about non-linear motions, complex occlusions and temporal abstractions, resulting in improved performance on video interpolation, while requiring no additional inputs in the form of optical flow or depth maps. Due to its simplicity, FLAVR can deliver 3x faster inference speed compared to the current most accurate method on multi-frame interpolation without losing interpolation accuracy. In addition, we evaluate FLAVR on a wide range of challenging settings and consistently demonstrate superior qualitative and quantitative results compared with prior methods on various popular benchmarks including Vimeo-90K, UCF101, DAVIS, Adobe, and GoPro. Finally, we demonstrate that FLAVR for video frame interpolation can serve as a useful self-supervised pretext task for action recognition, optical flow estimation, and motion magnification.	['Du Tran', 'Manmohan Chandraker', 'Deepak Pathak', 'Tarun Kalluri']	2020-12-15	null	null	null	null	['motion-magnification']	['computer-vision']	[ 6.61124513e-02 -6.04218841e-01 -5.30910552e-01 -1.63727090e-01 -5.99583924e-01 -4.09997553e-01 4.99100655e-01 -3.58808488e-01 -3.02167356e-01 9.22210455e-01 4.03281271e-01 -3.68228197e-01 2.94520617e-01 -4.16170120e-01 -9.08037841e-01 -2.72104681e-01 -1.25710249e-01 -3.90110724e-02 2.00389564e-01 1.27014622e-01 1.76407740e-01 4.64294165e-01 -1.52215111e+00 6.56088352e-01 6.13447130e-01 9.92292464e-01 -1.15958646e-01 1.05308867e+00 1.39990941e-01 1.49405646e+00 -2.79097885e-01 -2.38050908e-01 3.96900475e-01 -4.01835561e-01 -9.84984875e-01 1.58821583e-01 1.12503242e+00 -1.09853375e+00 -7.82706320e-01 5.09202540e-01 3.27984333e-01 3.96046817e-01 4.10908401e-01 -1.36654246e+00 -7.55803704e-01 4.84031737e-02 -5.74183345e-01 3.69445562e-01 8.20250511e-01 6.41852319e-01 8.27430964e-01 -9.58169043e-01 8.10151994e-01 1.53183794e+00 9.60545301e-01 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62b59f57-f89b-4ac0-ac36-59dafa1df3f1	tofa-transfer-once-for-all	2303.15485	null	https://arxiv.org/abs/2303.15485v2	https://arxiv.org/pdf/2303.15485v2.pdf	Transfer-Once-For-All: AI Model Optimization for Edge	Weight-sharing neural architecture search aims to optimize a configurable neural network model (supernet) for a variety of deployment scenarios across many devices with different resource constraints. Existing approaches use evolutionary search to extract models of different sizes from a supernet trained on a very large data set, and then fine-tune the extracted models on the typically small, real-world data set of interest. The computational cost of training thus grows linearly with the number of different model deployment scenarios. Hence, we propose Transfer-Once-For-All (TOFA) for supernet-style training on small data sets with constant computational training cost over any number of edge deployment scenarios. Given a task, TOFA obtains custom neural networks, both the topology and the weights, optimized for any number of edge deployment scenarios. To overcome the challenges arising from small data, TOFA utilizes a unified semi-supervised training loss to simultaneously train all subnets within the supernet, coupled with on-the-fly architecture selection at deployment time.	['Luis Angel Bathen', 'Rhui Dih Lee', 'Laura Wynter', 'Achintya Kundu']	2023-03-27	null	null	null	null	['architecture-search']	['methodology']	[ 5.89049980e-02 -1.39780506e-01 -2.62812883e-01 -2.86394715e-01 -2.50221282e-01 -6.65115833e-01 -8.46831873e-02 -1.14723623e-01 -4.84790206e-01 5.71640015e-01 -7.48562038e-01 -5.27539551e-01 -5.70313036e-01 -8.18637609e-01 -6.91978276e-01 -5.71646452e-01 -1.43135097e-02 1.15699959e+00 1.39650971e-01 -6.12704232e-02 -7.00749010e-02 8.46516490e-01 -1.70169759e+00 -1.82046458e-01 4.97862637e-01 1.53112435e+00 5.38090587e-01 7.60165572e-01 -1.44773781e-01 1.18475690e-01 -8.26548576e-01 -2.99557775e-01 6.08500898e-01 1.68906972e-01 -4.37748492e-01 -1.25328332e-01 2.86151022e-01 2.05668986e-01 -2.74579525e-01 7.14898825e-01 8.76622677e-01 1.70659661e-01 1.96082383e-01 -1.53831959e+00 -1.46337867e-01 1.00366437e+00 -1.96309522e-01 4.54864115e-01 -8.63327980e-01 1.65299654e-01 6.80903792e-01 -4.55650032e-01 5.61770439e-01 4.25336510e-01 8.67328286e-01 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540589e6-de85-4f4a-9aee-d661f49f7101	boosting-human-object-interaction-detection	2305.12252	null	https://arxiv.org/abs/2305.12252v1	https://arxiv.org/pdf/2305.12252v1.pdf	Boosting Human-Object Interaction Detection with Text-to-Image Diffusion Model	This paper investigates the problem of the current HOI detection methods and introduces DiffHOI, a novel HOI detection scheme grounded on a pre-trained text-image diffusion model, which enhances the detector's performance via improved data diversity and HOI representation. We demonstrate that the internal representation space of a frozen text-to-image diffusion model is highly relevant to verb concepts and their corresponding context. Accordingly, we propose an adapter-style tuning method to extract the various semantic associated representation from a frozen diffusion model and CLIP model to enhance the human and object representations from the pre-trained detector, further reducing the ambiguity in interaction prediction. Moreover, to fill in the gaps of HOI datasets, we propose SynHOI, a class-balance, large-scale, and high-diversity synthetic dataset containing over 140K HOI images with fully triplet annotations. It is built using an automatic and scalable pipeline designed to scale up the generation of diverse and high-precision HOI-annotated data. SynHOI could effectively relieve the long-tail issue in existing datasets and facilitate learning interaction representations. Extensive experiments demonstrate that DiffHOI significantly outperforms the state-of-the-art in regular detection (i.e., 41.50 mAP) and zero-shot detection. Furthermore, SynHOI can improve the performance of model-agnostic and backbone-agnostic HOI detection, particularly exhibiting an outstanding 11.55% mAP improvement in rare classes.	['Ruimao Zhang', 'Lei Zhang', 'Ailing Zeng', 'Fengyu Yang', 'Bingliang Li', 'Jie Yang']	2023-05-20	null	null	null	null	['human-object-interaction-detection']	['computer-vision']	[ 5.59440196e-01 3.74904312e-02 -1.53489754e-01 -1.07011989e-01 -8.78461897e-01 -1.27437234e-01 3.29500616e-01 -3.14629883e-01 9.42379143e-03 3.16427499e-01 4.63742226e-01 3.92572016e-01 -4.81592156e-02 -6.51617527e-01 -7.20181882e-01 -6.45322204e-01 3.80830765e-01 6.52172685e-01 5.55368185e-01 -1.82234794e-01 -2.88903266e-02 5.30152768e-02 -2.03622556e+00 6.31913185e-01 9.56582367e-01 1.09837258e+00 2.87282288e-01 5.52541673e-01 4.72773165e-01 7.36286104e-01 -9.11367655e-01 -2.64943302e-01 2.36535504e-01 -4.23537731e-01 -3.73730004e-01 -1.62411220e-02 6.71162784e-01 -3.29786122e-01 -7.18980253e-01 7.00791895e-01 9.62450266e-01 -1.84389934e-01 7.36835778e-01 -1.28129220e+00 -9.81376052e-01 6.21728659e-01 -9.38435435e-01 1.77344069e-01 4.62022275e-01 6.39226854e-01 1.19604099e+00 -1.26302671e+00 1.18752670e+00 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4798370b-e3f1-47c6-a38a-75250c2413bf	a-comparative-study-of-source-finding	2211.12809	null	https://arxiv.org/abs/2211.12809v1	https://arxiv.org/pdf/2211.12809v1.pdf	A comparative study of source-finding techniques in HI emission line cubes using SoFiA, MTObjects, and supervised deep learning	The 21 cm spectral line emission of atomic neutral hydrogen (HI) is one of the primary wavelengths observed in radio astronomy. However, the signal is intrinsically faint and the HI content of galaxies depends on the cosmic environment, requiring large survey volumes and survey depth to investigate the HI Universe. As the amount of data coming from these surveys continues to increase with technological improvements, so does the need for automatic techniques for identifying and characterising HI sources while considering the tradeoff between completeness and purity. This study aimed to find the optimal pipeline for finding and masking the most sources with the best mask quality and the fewest artefacts in 3D neutral hydrogen cubes. Various existing methods were explored in an attempt to create a pipeline to optimally identify and mask the sources in 3D neutral hydrogen 21 cm spectral line data cubes. Two traditional source-finding methods were tested, SoFiA and MTObjects, as well as a new supervised deep learning approach, in which a 3D convolutional neural network architecture, known as V-Net was used. These three source-finding methods were further improved by adding a classical machine learning classifier as a post-processing step to remove false positive detections. The pipelines were tested on HI data cubes from the Westerbork Synthesis Radio Telescope with additional inserted mock galaxies. SoFiA combined with a random forest classifier provided the best results, with the V-Net-random forest combination a close second. We suspect this is due to the fact that there are many more mock sources in the training set than real sources. There is, therefore, room to improve the quality of the V-Net network with better-labelled data such that it can potentially outperform SoFiA.	['M. H. F. Wilkinson', 'E. T. Martínez', 'M. A. W. Verheijen', 'J. A. Barkai']	2022-11-23	null	null	null	null	['astronomy']	['miscellaneous']	[ 1.61305308e-01 4.67583314e-02 2.45136142e-01 2.87746023e-02 -4.52763796e-01 -4.31516677e-01 8.38842928e-01 -1.87346280e-01 -4.99423891e-01 6.45968974e-01 3.49501930e-02 -2.67201722e-01 -1.50691450e-01 -8.94047439e-01 -3.56272459e-01 -9.80725110e-01 -1.03406410e-03 8.50871623e-01 8.76561046e-01 -3.69232260e-02 2.20774725e-01 7.79701591e-01 -1.55620623e+00 2.68989176e-01 3.46399784e-01 9.20928121e-01 5.01209974e-01 7.01758683e-01 -3.06759682e-02 8.62293184e-01 -6.03755116e-01 -4.38670926e-02 7.06945419e-01 -8.58374774e-01 -6.63136661e-01 -4.01602238e-02 9.38599855e-02 1.34446606e-01 1.70238577e-02 7.09983230e-01 6.38078034e-01 -6.02626503e-02 6.33287549e-01 -1.15091753e+00 1.89205974e-01 3.47100049e-01 -4.91436183e-01 2.55844295e-01 -1.11656465e-01 4.14560884e-01 6.18606806e-01 -9.12606061e-01 5.75740278e-01 9.25879836e-01 7.57148147e-01 -2.72159483e-02 -1.00507081e+00 -6.81949735e-01 -8.25303018e-01 3.47315609e-01 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2764ba43-9ff0-4ef3-ae9b-d05f88bdc939	multiearth-2022-the-champion-solution-for-the	2206.08970	null	https://arxiv.org/abs/2206.08970v1	https://arxiv.org/pdf/2206.08970v1.pdf	MultiEarth 2022 -- The Champion Solution for the Matrix Completion Challenge via Multimodal Regression and Generation	Earth observation satellites have been continuously monitoring the earth environment for years at different locations and spectral bands with different modalities. Due to complex satellite sensing conditions (e.g., weather, cloud, atmosphere, orbit), some observations for certain modalities, bands, locations, and times may not be available. The MultiEarth Matrix Completion Challenge in CVPR 2022 [1] provides the multimodal satellite data for addressing such data sparsity challenges with the Amazon Rainforest as the region of interest. This work proposes an adaptive real-time multimodal regression and generation framework and achieves superior performance on unseen test queries in this challenge with an LPIPS of 0.2226, a PSNR of 123.0372, and an SSIM of 0.6347.	['Jui-Hsin Lai', 'Yuchuan Gou', 'Hang Zhou', 'Hongchen Liu', 'Bo Peng']	2022-06-17	null	null	null	null	['matrix-completion']	['methodology']	[ 2.23051339e-01 -7.93968379e-01 -2.50875175e-01 -2.38427326e-01 -1.19422519e+00 -6.51314735e-01 3.71973187e-01 -1.54140115e-01 -3.88709545e-01 7.85941839e-01 8.52128193e-02 -1.55528292e-01 -1.24727763e-01 -4.52401996e-01 -6.74381435e-01 -8.47122014e-01 -3.34076852e-01 1.51993170e-01 -3.07246983e-01 -1.88783422e-01 -6.55154064e-02 3.43313128e-01 -1.32466865e+00 3.15540850e-01 5.83100438e-01 1.26845551e+00 3.79298568e-01 7.72433221e-01 4.31384623e-01 3.83760035e-01 -2.14711934e-01 -2.62838930e-01 4.64065105e-01 1.00891478e-01 -2.88214505e-01 3.44974935e-01 6.74525321e-01 -4.03432369e-01 -6.79011106e-01 1.13207352e+00 4.91295516e-01 3.26574177e-01 4.18693990e-01 -1.10641253e+00 -4.97307867e-01 2.32914940e-01 -1.32651031e+00 3.80643398e-01 2.62316436e-01 -8.30396414e-02 1.30848849e+00 -1.19767725e+00 4.81161177e-01 1.13777983e+00 4.10535634e-01 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65fff80d-ea9d-4336-89f0-03b0c3f99d00	ganonymization-a-gan-based-face-anonymization	2305.02143	null	https://arxiv.org/abs/2305.02143v1	https://arxiv.org/pdf/2305.02143v1.pdf	GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressions	In recent years, the increasing availability of personal data has raised concerns regarding privacy and security. One of the critical processes to address these concerns is data anonymization, which aims to protect individual privacy and prevent the release of sensitive information. This research focuses on the importance of face anonymization. Therefore, we introduce GANonymization, a novel face anonymization framework with facial expression-preserving abilities. Our approach is based on a high-level representation of a face which is synthesized into an anonymized version based on a generative adversarial network (GAN). The effectiveness of the approach was assessed by evaluating its performance in removing identifiable facial attributes to increase the anonymity of the given individual face. Additionally, the performance of preserving facial expressions was evaluated on several affect recognition datasets and outperformed the state-of-the-art method in most categories. Finally, our approach was analyzed for its ability to remove various facial traits, such as jewelry, hair color, and multiple others. Here, it demonstrated reliable performance in removing these attributes. Our results suggest that GANonymization is a promising approach for anonymizing faces while preserving facial expressions.	['Elisabeth André', 'Peter Krawitz', 'Cristina Conati', 'Tzung-Chien Hsieh', 'Alexander Hustinx', 'Mohamed Benouis', 'Silvan Mertes', 'Fabio Hellmann']	2023-05-03	null	null	null	null	['face-anonymization']	['computer-vision']	[ 2.28964433e-01 3.46007168e-01 2.51547635e-01 -5.80416203e-01 -3.24016601e-01 -6.85689688e-01 4.56731051e-01 -8.10279101e-02 -2.83060700e-01 7.62196183e-01 3.13821971e-01 3.82299244e-01 1.19641796e-01 -8.78582239e-01 -5.21943271e-01 -7.18421876e-01 1.29775614e-01 -7.72893280e-02 -6.54684722e-01 -1.98363662e-01 3.54287364e-02 1.05940700e+00 -1.66277921e+00 2.59159178e-01 7.93463051e-01 9.54625309e-01 -9.18209791e-01 1.15289278e-01 -5.25176823e-02 3.29981774e-01 -8.59778643e-01 -1.16586173e+00 5.31148016e-01 -4.11662757e-01 -6.28850639e-01 -3.95196378e-02 5.57797372e-01 -4.83736902e-01 -2.02009887e-01 1.16638041e+00 6.63392961e-01 -1.94532424e-01 3.66494775e-01 -1.63643467e+00 -1.02289724e+00 2.96036333e-01 -5.19142866e-01 -2.39379108e-01 5.36639929e-01 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517c6f74-04de-4f53-a0b3-79b08437cc16	epileptic-seizure-risk-assessment-by-multi	2204.07034	null	https://arxiv.org/abs/2204.07034v1	https://arxiv.org/pdf/2204.07034v1.pdf	Epileptic Seizure Risk Assessment by Multi-Channel Imaging of the EEG	Refractory epileptic patients can suffer a seizure at any moment. Seizure prediction would substantially improve their lives. In this work, based on scalp EEG and its transformation into images, the likelihood of an epileptic seizure occurring at any moment is computed using an average of the softmax layer output (the likelihood) of a CNN, instead of the output of the classification layer. Results show that by analyzing the likelihood and thresholding it, prediction has higher sensitivity or a lower FPR/h. The best threshold for the likelihood was higher than 50% for 5 patients, and was lower for the remaining 36. However, more testing is needed, especially in new seizures, to better assess the real performance of this method. This work is a proof of concept with a positive outlook.	['Antonio Dourado', 'Cesar Teixeira', 'Fabio Lopes', 'Tiago Leal']	2022-04-12	null	null	null	null	['seizure-prediction']	['medical']	[ 1.28088057e-01 5.00332594e-01 1.23412587e-01 -4.44547802e-01 -4.52556640e-01 -1.45595878e-01 3.87986779e-01 2.76626348e-01 -5.36478579e-01 9.30788517e-01 1.30099328e-02 -1.74211442e-01 -1.07013389e-01 -7.29369938e-01 -3.56148064e-01 -8.53344858e-01 -5.55183589e-01 2.16156408e-01 2.42933214e-01 1.80861354e-01 1.37050375e-01 4.99306530e-01 -1.25485122e+00 5.30475974e-01 6.02361262e-01 1.42510402e+00 4.16013271e-01 1.89495102e-01 4.13719922e-01 5.28373659e-01 -1.08989000e+00 -7.70518631e-02 3.76438797e-02 -3.78617734e-01 -3.33431333e-01 -2.75486082e-01 -8.21033046e-02 -2.76367605e-01 -1.84485808e-01 1.11704373e+00 5.10948956e-01 -3.32282871e-01 4.77627039e-01 -9.71467137e-01 -1.35910124e-01 8.46942186e-01 1.69433132e-02 3.74080688e-01 4.06654358e-01 1.64607510e-01 3.71561348e-01 -4.75010395e-01 4.07915503e-01 5.38024008e-01 4.27380532e-01 3.27505112e-01 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-1.30815518e+00 -7.74551690e-01 -9.09777433e-02 1.80829436e-01 -5.16636252e-01 -6.78922951e-01 -9.13804024e-02]	[13.230745315551758, 3.5238869190216064]
07e2e36f-dc2d-4de3-8d7f-9a7a4e41734b	histopathological-image-classification-based	2210.09021	null	https://arxiv.org/abs/2210.09021v2	https://arxiv.org/pdf/2210.09021v2.pdf	Histopathological Image Classification based on Self-Supervised Vision Transformer and Weak Labels	Whole Slide Image (WSI) analysis is a powerful method to facilitate the diagnosis of cancer in tissue samples. Automating this diagnosis poses various issues, most notably caused by the immense image resolution and limited annotations. WSIs commonly exhibit resolutions of 100Kx100K pixels. Annotating cancerous areas in WSIs on the pixel level is prohibitively labor-intensive and requires a high level of expert knowledge. Multiple instance learning (MIL) alleviates the need for expensive pixel-level annotations. In MIL, learning is performed on slide-level labels, in which a pathologist provides information about whether a slide includes cancerous tissue. Here, we propose Self-ViT-MIL, a novel approach for classifying and localizing cancerous areas based on slide-level annotations, eliminating the need for pixel-wise annotated training data. Self-ViT- MIL is pre-trained in a self-supervised setting to learn rich feature representation without relying on any labels. The recent Vision Transformer (ViT) architecture builds the feature extractor of Self-ViT-MIL. For localizing cancerous regions, a MIL aggregator with global attention is utilized. To the best of our knowledge, Self-ViT- MIL is the first approach to introduce self-supervised ViTs in MIL-based WSI analysis tasks. We showcase the effectiveness of our approach on the common Camelyon16 dataset. Self-ViT-MIL surpasses existing state-of-the-art MIL-based approaches in terms of accuracy and area under the curve (AUC).	['Heinz Koeppl', 'Nadine Flinner', 'Tim Prangemeier', 'Christoph Reich', 'Oezdemir Cetin', 'Ahmet Gokberk Gul']	2022-10-17	null	null	null	null	['histopathological-image-classification', 'multiple-instance-learning']	['medical', 'methodology']	[ 5.98547876e-01 2.13026181e-01 -2.90230185e-01 -2.19139293e-01 -1.59147465e+00 -5.10248303e-01 4.62931544e-01 4.56519067e-01 -5.62375665e-01 5.05119920e-01 -1.34827271e-01 -3.13210905e-01 1.94744885e-01 -6.67986751e-01 -7.34220147e-01 -1.11420906e+00 3.04438651e-01 3.42927188e-01 4.09634531e-01 2.52484918e-01 -2.41394378e-02 3.99145961e-01 -1.05862677e+00 6.42799199e-01 7.63853550e-01 1.25875950e+00 3.64398092e-01 8.40483844e-01 -4.46982503e-01 8.68095458e-01 -3.63447309e-01 -3.02316844e-01 4.48882766e-02 -3.16835761e-01 -8.83463502e-01 1.25263974e-01 6.42136812e-01 -4.59424891e-02 -6.48610815e-02 1.04053795e+00 2.21885055e-01 -6.07940972e-01 7.47031808e-01 -9.40339684e-01 -2.21116126e-01 3.31248671e-01 -9.02273297e-01 4.10440624e-01 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9.48385715e-01 2.24232033e-01 7.82930493e-01 5.30033231e-01 -7.08434284e-01 5.97436493e-03 -7.13661373e-01 -5.50069749e-01 3.08263004e-01 6.46491587e-01 -3.33228827e-01 -2.75120348e-01 2.26075292e-01]	[15.105401039123535, -2.9384305477142334]
864aeb1c-7d03-4a37-8101-003707b7dc4c	multi-classification-of-brain-tumor-images	2206.08543	null	https://arxiv.org/abs/2206.08543v1	https://arxiv.org/pdf/2206.08543v1.pdf	Multi-Classification of Brain Tumor Images Using Transfer Learning Based Deep Neural Network	In recent advancement towards computer based diagnostics system, the classification of brain tumor images is a challenging task. This paper mainly focuses on elevating the classification accuracy of brain tumor images with transfer learning based deep neural network. The classification approach is started with the image augmentation operation including rotation, zoom, hori-zontal flip, width shift, height shift, and shear to increase the diversity in image datasets. Then the general features of the input brain tumor images are extracted based on a pre-trained transfer learning method comprised of Inception-v3. Fi-nally, the deep neural network with 4 customized layers is employed for classi-fying the brain tumors in most frequent brain tumor types as meningioma, glioma, and pituitary. The proposed model acquires an effective performance with an overall accuracy of 96.25% which is much improved than some existing multi-classification methods. Whereas, the fine-tuning of hyper-parameters and inclusion of customized DNN with the Inception-v3 model results in an im-provement of the classification accuracy.	['Md. Saiful Islam', 'Khaleda Akhter Sathi', 'Pramit Dutta']	2022-06-17	null	null	null	null	['image-augmentation']	['computer-vision']	[ 1.87059104e-01 3.29677276e-02 9.33400244e-02 -2.71377712e-01 -3.49828809e-01 -6.38697669e-02 5.34663737e-01 4.08562599e-03 -6.11902654e-01 7.05016613e-01 -1.64393596e-02 -6.41274154e-01 -2.20727205e-01 -7.38541007e-01 -2.20237702e-01 -9.95195806e-01 9.92662236e-02 4.99736577e-01 1.02420911e-01 -2.26605624e-01 2.15853706e-01 6.94017768e-01 -1.42687154e+00 4.14794862e-01 7.27173150e-01 1.03798127e+00 2.48014882e-01 6.54115617e-01 -2.54652947e-01 6.87085629e-01 -5.79623282e-01 1.86928287e-01 1.13478556e-01 4.98262197e-02 -6.84514344e-01 2.23097280e-01 -5.64227104e-02 -2.15595588e-01 -1.83018520e-01 8.97170544e-01 6.52990580e-01 -3.01844180e-01 1.04288876e+00 -1.27204883e+00 -3.04149657e-01 4.25848097e-01 -6.93308949e-01 5.13368905e-01 -2.42498174e-01 -3.64588164e-02 2.76159376e-01 -7.72006094e-01 3.80177200e-01 7.05836892e-01 5.88553667e-01 3.92294019e-01 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2.02577695e-01 2.99273878e-01 -8.24083090e-01 -3.34310204e-01 4.61194180e-02]	[14.90623950958252, -2.569298267364502]
1c4f21f8-f279-45d5-bb57-8ccad70d9a66	statistical-analysis-of-time-frequency	2209.13350	null	https://arxiv.org/abs/2209.13350v1	https://arxiv.org/pdf/2209.13350v1.pdf	Statistical Analysis of Time-Frequency Features Based On Multivariate Synchrosqueezing Transform for Hand Gesture Classification	In this study, the four joint time-frequency (TF) moments; mean, variance, skewness, and kurtosis of TF matrix obtained from Multivariate Synchrosqueezing Transform (MSST) are proposed as features for hand gesture recognition. A publicly available dataset containing surface EMG (sEMG) signals of 40 subjects performing 10 hand gestures, was used. The distinguishing power of the feature variables for the tested gestures was evaluated according to their p values obtained from the Kruskal-Wallis (KW) test. It is concluded that the mean, variance, skewness, and kurtosis of TF matrices can be candidate feature sets for the recognition of hand gestures.	['Onan Guren', 'Mehmet Akif Ozdemir', 'Deniz Hande Kisa', 'Lutfiye Saripinar']	2022-09-24	null	null	null	null	['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition']	['computer-vision', 'computer-vision', 'computer-vision']	[ 4.85111661e-02 -5.97938538e-01 -5.55395782e-01 -5.60708679e-02 -3.25452805e-01 -3.41655493e-01 3.44470561e-01 -2.56104022e-01 -5.58614314e-01 6.53388917e-01 2.92107254e-01 6.37915656e-02 -8.02719593e-01 -3.11143463e-04 4.70397156e-03 -9.23061609e-01 -9.32265103e-01 -6.15799055e-02 -2.42844075e-02 7.45723918e-02 6.68113708e-01 6.79647505e-01 -1.58261204e+00 4.30466160e-02 5.01659513e-01 1.08152533e+00 2.20698994e-02 5.37985802e-01 2.14138001e-01 1.38223730e-02 -9.39419270e-01 3.01041845e-02 2.25682542e-01 -2.49239638e-01 -5.27309895e-01 -1.29305527e-01 -1.69657260e-01 -4.53079522e-01 -1.51116058e-01 7.92417884e-01 7.53738105e-01 3.72536272e-01 9.49713767e-01 -1.28157163e+00 -3.13572675e-01 5.85068882e-01 -4.72362697e-01 2.56461918e-01 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190bf0e9-e5f4-41a5-a988-3acef021ac8d	neural-compression-based-feature-learning-for	2203.09208	null	https://arxiv.org/abs/2203.09208v2	https://arxiv.org/pdf/2203.09208v2.pdf	Neural Compression-Based Feature Learning for Video Restoration	How to efficiently utilize the temporal features is crucial, yet challenging, for video restoration. The temporal features usually contain various noisy and uncorrelated information, and they may interfere with the restoration of the current frame. This paper proposes learning noise-robust feature representations to help video restoration. We are inspired by that the neural codec is a natural denoiser. In neural codec, the noisy and uncorrelated contents which are hard to predict but cost lots of bits are more inclined to be discarded for bitrate saving. Therefore, we design a neural compression module to filter the noise and keep the most useful information in features for video restoration. To achieve robustness to noise, our compression module adopts a spatial channel-wise quantization mechanism to adaptively determine the quantization step size for each position in the latent. Experiments show that our method can significantly boost the performance on video denoising, where we obtain 0.13 dB improvement over BasicVSR++ with only 0.23x FLOPs. Meanwhile, our method also obtains SOTA results on video deraining and dehazing.	['Yan Lu', 'Dong Liu', 'Bin Li', 'Jiahao Li', 'Cong Huang']	2022-03-17	null	http://openaccess.thecvf.com//content/CVPR2022/html/Huang_Neural_Compression-Based_Feature_Learning_for_Video_Restoration_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Huang_Neural_Compression-Based_Feature_Learning_for_Video_Restoration_CVPR_2022_paper.pdf	cvpr-2022-1	['video-denoising', 'video-restoration']	['computer-vision', 'computer-vision']	[ 2.37457067e-01 -7.44569480e-01 -4.73932065e-02 -3.03364158e-01 -4.52301949e-01 -9.52648371e-02 -2.40088962e-02 -2.96476126e-01 -3.25988024e-01 6.00114405e-01 6.62699401e-01 -9.89942700e-02 -1.19197428e-01 -7.50058174e-01 -6.20545506e-01 -1.17435396e+00 -2.36382067e-01 -8.07942688e-01 3.44628036e-01 -3.31255704e-01 3.90799105e-01 2.11068898e-01 -1.66791546e+00 4.18181330e-01 9.68227863e-01 1.28381908e+00 6.69642627e-01 5.96750438e-01 -5.29933982e-02 9.65057492e-01 -7.26934254e-01 -9.42977890e-03 2.85235107e-01 -4.72303629e-01 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