paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3fc3a0bb-0e3f-4045-81e0-4f82832ed829 | kpdrop-an-approach-to-improving-absent | 2112.01476 | null | https://arxiv.org/abs/2112.01476v3 | https://arxiv.org/pdf/2112.01476v3.pdf | KPDrop: Improving Absent Keyphrase Generation | Keyphrase generation is the task of generating phrases (keyphrases) that summarize the main topics of a given document. Keyphrases can be either present or absent from the given document. While the extraction of present keyphrases has received much attention in the past, only recently a stronger focus has been placed o... | ['Jishnu Ray Chowdhury', 'Cornelia Caragea', 'Tuhin Kundu', 'Seoyeon Park'] | 2021-12-02 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 5.08632421e-01 1.17006458e-01 -4.52240705e-01 -1.88316349e-02
-1.22394693e+00 -1.00962210e+00 1.29014528e+00 6.14002049e-01
-6.46280885e-01 1.06383479e+00 6.61438704e-01 -4.13205385e-01
1.14532508e-01 -5.92289388e-01 -1.01325524e+00 -5.18093526e-01
1.80658266e-01 1.98838905e-01 1.68014124e-01 -1.73919663... | [12.305792808532715, 8.904464721679688] |
9cf1aa09-4912-49ef-a4a9-448452ead093 | how-far-can-camels-go-exploring-the-state-of | 2306.04751 | null | https://arxiv.org/abs/2306.04751v1 | https://arxiv.org/pdf/2306.04751v1.pdf | How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources | In this work we explore recent advances in instruction-tuning language models on a range of open instruction-following datasets. Despite recent claims that open models can be on par with state-of-the-art proprietary models, these claims are often accompanied by limited evaluation, making it difficult to compare models ... | ['Hannaneh Hajishirzi', 'Iz Beltagy', 'Noah A. Smith', 'Kelsey MacMillan', 'David Wadden', 'Khyathi Raghavi Chandu', 'Tushar Khot', 'Jack Hessel', 'Pradeep Dasigi', 'Hamish Ivison', 'Yizhong Wang'] | 2023-06-07 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [-4.36943144e-01 -1.89310655e-01 -6.54828966e-01 -4.50959146e-01
-1.21551096e+00 -9.05737519e-01 4.85631317e-01 2.63403952e-01
-4.27901804e-01 5.14191151e-01 5.11721551e-01 -1.09800589e+00
-2.13709697e-01 -6.14911079e-01 -9.92129624e-01 6.12681769e-02
1.75955489e-01 3.81842792e-01 2.66505539e-01 -8.33167493... | [10.592019081115723, 8.258960723876953] |
1555995f-b332-4f6a-b51a-2cf094871aa7 | measuring-numerical-common-sense-is-a-word | null | null | https://openreview.net/forum?id=B1xbTlBKwB | https://openreview.net/pdf?id=B1xbTlBKwB | Measuring Numerical Common Sense: Is A Word Embedding Approach Effective? | Numerical common sense (e.g., ``a person with a height of 2m is very tall'') is essential when deploying artificial intelligence (AI) systems in society. To predict ranges of small and large values for a given target noun and unit,
previous studies have implemented a rule-based method that processed numeric values app... | ['Tatsuya Harada', 'Chin-Yew Lin', 'Hiroaki Yamane'] | 2019-09-25 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 1.76990896e-01 3.04080158e-01 -1.03378668e-01 -3.34677666e-01
-2.90310770e-01 -4.32578743e-01 6.41377866e-01 8.96350145e-01
-8.48072112e-01 8.64215612e-01 2.67225504e-01 -2.06419632e-01
-1.27616763e-01 -1.15481067e+00 -2.80835748e-01 -2.91618913e-01
2.76268244e-01 3.65573883e-01 -3.17304432e-02 -7.51810312... | [10.454530715942383, 9.314409255981445] |
46f8eb45-d19f-405b-bab5-e0baa7f683ff | fast-and-robust-online-inference-with | 2106.03156 | null | https://arxiv.org/abs/2106.03156v3 | https://arxiv.org/pdf/2106.03156v3.pdf | Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling | We develop a new method of online inference for a vector of parameters estimated by the Polyak-Ruppert averaging procedure of stochastic gradient descent (SGD) algorithms. We leverage insights from time series regression in econometrics and construct asymptotically pivotal statistics via random scaling. Our approach is... | ['Youngki Shin', 'Myung Hwan Seo', 'Yuan Liao', 'Sokbae Lee'] | 2021-06-06 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-1.76073059e-01 -3.70822281e-01 -2.00282782e-01 -2.65421122e-01
-9.01959419e-01 -7.27442741e-01 7.59105742e-01 2.03078717e-01
-5.32161057e-01 9.69752491e-01 -1.34357437e-01 -7.24556386e-01
-3.90403658e-01 -4.95572031e-01 -6.48689330e-01 -6.26154184e-01
-4.32474434e-01 1.22643411e-01 2.59826914e-03 7.74494186... | [6.796984672546387, 4.143144130706787] |
75bef24d-6130-46d9-92f6-059069c34176 | deepvo-a-deep-learning-approach-for-monocular | 1611.06069 | null | http://arxiv.org/abs/1611.06069v1 | http://arxiv.org/pdf/1611.06069v1.pdf | DeepVO: A Deep Learning approach for Monocular Visual Odometry | Deep Learning based techniques have been adopted with precision to solve a
lot of standard computer vision problems, some of which are image
classification, object detection and segmentation. Despite the widespread
success of these approaches, they have not yet been exploited largely for
solving the standard perception... | ['Vikram Mohanty', 'Shubh Agrawal', 'Shaswat Datta', 'Arna Ghosh', 'Vishnu Dutt Sharma', 'Debashish Chakravarty'] | 2016-11-18 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [ 3.76309790e-02 -2.85246372e-02 -1.97435275e-01 -4.73460913e-01
-1.38376147e-01 -5.71160316e-01 7.44631469e-01 1.34297032e-02
-9.31873620e-01 3.29453886e-01 -4.37461168e-01 -2.65423179e-01
4.05283868e-02 -2.95522153e-01 -9.28139091e-01 -6.40374780e-01
-5.96402548e-02 8.61112058e-01 6.22057199e-01 -1.49433643... | [7.642723083496094, -2.100255012512207] |
ec89c9ac-37c6-4476-9869-7768458c7515 | moral-hazard-on-productivity-among-work-from | 2209.05684 | null | https://arxiv.org/abs/2209.05684v1 | https://arxiv.org/pdf/2209.05684v1.pdf | Moral Hazard on Productivity Among Work-From-Home Workers Amid the COVID-19 Pandemic | After the outbreak of COVID 19, firms appear to monitor Work From Home (WFH) workers more than ever out of anxiety that workers may shirk at home or implement moral hazard at home. Using the Survey of Working Arrangements and Attitudes (SWAA, Barrero et al., 2021), the evidence of WFH workers' ex post moral hazard as w... | ['Jieun Lee'] | 2022-09-13 | null | null | null | null | ['culture'] | ['speech'] | [-1.30536417e-02 8.42819750e-01 -3.48756313e-01 7.96420053e-02
1.11260694e-02 -4.76301670e-01 1.49354354e-01 -1.93476170e-01
-3.95765156e-01 8.32607925e-01 8.30503464e-01 -4.82922822e-01
-8.57010782e-01 -3.48737150e-01 -1.91189617e-01 -9.38591659e-01
7.00539470e-01 5.76785579e-02 -3.99901181e-01 -3.58950794... | [8.938135147094727, 6.240711688995361] |
895f5c24-f3f7-4843-8816-0b26a52d07dd | findings-of-the-the-ruatd-shared-task-2022-on | 2206.01583 | null | https://arxiv.org/abs/2206.01583v1 | https://arxiv.org/pdf/2206.01583v1.pdf | Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russian | We present the shared task on artificial text detection in Russian, which is organized as a part of the Dialogue Evaluation initiative, held in 2022. The shared task dataset includes texts from 14 text generators, i.e., one human writer and 13 text generative models fine-tuned for one or more of the following generatio... | ['Ekaterina Artemova', 'Elena Tutubalina', 'Ivan Smurov', 'Tatiana Shavrina', 'Anastasiya Valeeva', 'Marat Saidov', 'Alena Fenogenova', 'Daniil Chernianskii', 'Vladislav Mikhailov', 'Tatiana Shamardina'] | 2022-06-03 | null | null | null | null | ['paraphrase-generation', 'dialogue-evaluation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing', 'natural-language-processing'] | [ 4.75811720e-01 4.85305101e-01 -4.97761881e-03 -1.98147327e-01
-1.40781987e+00 -6.64146125e-01 1.21156979e+00 -2.99374619e-03
-3.75996053e-01 1.13986993e+00 7.48481452e-01 -2.61053473e-01
5.96063852e-01 -5.23198664e-01 -3.32710773e-01 -3.74296218e-01
6.85185552e-01 1.02161837e+00 -1.02217637e-01 -5.01439452... | [12.098097801208496, 9.322979927062988] |
3d30afdb-5de9-49f5-8ba3-db14841310e8 | transfer-learning-of-lexical-semantic | 2209.02495 | null | https://arxiv.org/abs/2209.02495v2 | https://arxiv.org/pdf/2209.02495v2.pdf | Transfer Learning of Lexical Semantic Families for Argumentative Discourse Units Identification | Argument mining tasks require an informed range of low to high complexity linguistic phenomena and commonsense knowledge. Previous work has shown that pre-trained language models are highly effective at encoding syntactic and semantic linguistic phenomena when applied with transfer learning techniques and built on diff... | ['António Branco', 'Ruben Branco', 'João Rodrigues'] | 2022-09-06 | null | null | null | null | ['argument-mining'] | ['natural-language-processing'] | [ 3.46029997e-01 5.27893841e-01 -5.07169724e-01 -3.87850493e-01
-8.00507605e-01 -6.71258092e-01 9.60844636e-01 5.50062835e-01
-5.10086119e-01 1.02426791e+00 7.96544492e-01 -8.45626175e-01
-2.07432508e-01 -7.84712195e-01 -6.17232621e-01 8.67770612e-02
-1.61641881e-01 6.05131328e-01 3.19322765e-01 -7.78645813... | [10.439194679260254, 9.084807395935059] |
631a4442-b002-487a-9586-b9a5a2ffb830 | exploration-with-multi-sample-target-values | 2202.02693 | null | https://arxiv.org/abs/2202.02693v1 | https://arxiv.org/pdf/2202.02693v1.pdf | Exploration with Multi-Sample Target Values for Distributional Reinforcement Learning | Distributional reinforcement learning (RL) aims to learn a value-network that predicts the full distribution of the returns for a given state, often modeled via a quantile-based critic. This approach has been successfully integrated into common RL methods for continuous control, giving rise to algorithms such as Distri... | ['Frank Wood', 'Michiel Van de Panne', 'Michael Teng'] | 2022-02-06 | null | null | null | null | ['distributional-reinforcement-learning', 'humanoid-control'] | ['methodology', 'robots'] | [-2.34722406e-01 3.55754048e-01 -4.21064436e-01 -1.60766020e-01
-9.76696432e-01 -5.33401132e-01 9.16352332e-01 2.48486221e-01
-7.41228282e-01 1.25506437e+00 2.02024803e-01 -2.21793741e-01
-2.96893388e-01 -5.10269523e-01 -5.73362410e-01 -9.59935665e-01
-3.79195809e-01 7.59287357e-01 -1.94782183e-01 -3.36495370... | [4.0794596672058105, 2.481621742248535] |
d3e4002e-5f27-432d-b6df-9ab22d3a5bff | biggreen-at-semeval-2021-task-1-lexical | 2104.09040 | null | https://arxiv.org/abs/2104.09040v1 | https://arxiv.org/pdf/2104.09040v1.pdf | BigGreen at SemEval-2021 Task 1: Lexical Complexity Prediction with Assembly Models | This paper describes a system submitted by team BigGreen to LCP 2021 for predicting the lexical complexity of English words in a given context. We assemble a feature engineering-based model with a deep neural network model founded on BERT. While BERT itself performs competitively, our feature engineering-based model he... | ['Soroush Vosoughi', 'Weicheng Ma', 'Aadil Islam'] | 2021-04-19 | null | https://aclanthology.org/2021.semeval-1.86 | https://aclanthology.org/2021.semeval-1.86.pdf | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-1.53378144e-01 1.87757298e-01 -3.00919581e-02 -5.42185366e-01
-8.21241796e-01 -5.34952104e-01 5.14307678e-01 3.14221054e-01
-5.61059535e-01 4.71951127e-01 6.59424901e-01 -6.53380930e-01
-2.26900145e-01 -6.27756476e-01 -2.18927428e-01 -1.08143121e-01
-3.18607129e-02 5.37045300e-01 -1.27119035e-01 -7.58503199... | [10.6235990524292, 10.398606300354004] |
dae9e539-9125-4b80-9e28-5635d568f2fd | machine-reading-with-background-knowledge | 1612.05348 | null | http://arxiv.org/abs/1612.05348v1 | http://arxiv.org/pdf/1612.05348v1.pdf | Machine Reading with Background Knowledge | Intelligent systems capable of automatically understanding natural language
text are important for many artificial intelligence applications including
mobile phone voice assistants, computer vision, and robotics. Understanding
language often constitutes fitting new information into a previously acquired
view of the wor... | ['Ndapandula Nakashole', 'Tom M. Mitchell'] | 2016-12-16 | null | null | null | null | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [ 3.46468180e-01 6.05155587e-01 -7.35158920e-01 -6.61671877e-01
-7.88072288e-01 -8.30910802e-01 9.08384621e-01 5.68260252e-01
-7.62454689e-01 7.93327391e-01 2.95323431e-01 -7.36135304e-01
1.65054336e-01 -8.81021023e-01 -6.74282193e-01 -1.57382622e-01
3.75361443e-01 1.02433538e+00 2.28002101e-01 -3.74684423... | [10.31358528137207, 9.127758979797363] |
78355024-9f31-418f-8812-09c93f3717b7 | generative-model-for-zero-shot-sketch-based | 1904.08542 | null | http://arxiv.org/abs/1904.08542v1 | http://arxiv.org/pdf/1904.08542v1.pdf | Generative Model for Zero-Shot Sketch-Based Image Retrieval | We present a probabilistic model for Sketch-Based Image Retrieval (SBIR)
where, at retrieval time, we are given sketches from novel classes, that were
not present at training time. Existing SBIR methods, most of which rely on
learning class-wise correspondences between sketches and images, typically work
well only for ... | ['Piyush Rai', 'Vinay Kumar Verma', 'Ashish Mishra', 'Aakansha Mishra'] | 2019-04-18 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 3.19786966e-01 -3.69476676e-01 -1.72489434e-01 -2.45800316e-01
-1.08039916e+00 -7.94990361e-01 1.20239997e+00 -3.64194125e-01
-1.70435548e-01 7.36282110e-01 5.38766943e-02 8.34861249e-02
-2.76858628e-01 -9.35794413e-01 -8.49502981e-01 -6.10837698e-01
4.17436391e-01 7.90035367e-01 -9.09112394e-03 -8.40332061... | [11.637124061584473, 0.6146345138549805] |
366ff198-dc82-447a-a743-71982abd0279 | calibrating-sequence-likelihood-improves | 2210.00045 | null | https://arxiv.org/abs/2210.00045v1 | https://arxiv.org/pdf/2210.00045v1.pdf | Calibrating Sequence likelihood Improves Conditional Language Generation | Conditional language models are predominantly trained with maximum likelihood estimation (MLE), giving probability mass to sparsely observed target sequences. While MLE trained models assign high probability to plausible sequences given the context, the model probabilities often do not accurately rank-order generated s... | ['Peter J. Liu', 'Mohammad Saleh', 'Shashi Narayan', 'Rishabh Joshi', 'Misha Khalman', 'Yao Zhao'] | 2022-09-30 | null | null | null | null | ['abstractive-text-summarization', 'data-to-text-generation', 'question-generation'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 7.79938161e-01 5.28787613e-01 -4.25543606e-01 -4.50231612e-01
-1.38690138e+00 -6.15015507e-01 9.02621210e-01 1.95573032e-01
-3.93433273e-01 1.09537065e+00 8.76304686e-01 -5.32624364e-01
1.64681952e-02 -5.21251142e-01 -8.38005185e-01 -1.66149527e-01
1.99905962e-01 9.71350312e-01 2.93559171e-02 -1.17441460... | [11.862068176269531, 9.056877136230469] |
d897c272-2952-4c4b-865f-1a12e7d6482b | iterative-weak-self-supervised-classification | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0167865521000507 | https://www.di.ubi.pt/~hugomcp/doc/Events_PRL.pdf | Iterative weak/self-supervised classification framework for abnormal events detection | The detection of abnormal events in surveillance footage remains a challenge and has been the scope of various research works. Having observed that the state-of-the-art performance is still unsatisfactory, this paper provides a novel solution to the problem, with four-fold contributions: 1) upon the work of Sultani et ... | ['Hugo Proença', 'Bruno Degardin'] | 2021-01-03 | null | null | null | null | ['self-supervised-anomaly-detection', 'anomaly-detection-in-surveillance-videos', 'semi-supervised-video-classification', 'abnormal-event-detection-in-video', 'semi-supervised-anomaly-detection', 'anomaly-detection-in-surveillance-videos', 'abnormal-event-detection-in-video'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'methodology', 'methodology'] | [ 4.23716366e-01 2.49870777e-01 -1.85733125e-01 -4.92791265e-01
-7.37863660e-01 -3.50109369e-01 6.28617048e-01 7.40628615e-02
-4.92008507e-01 6.50936306e-01 6.01724908e-02 -2.49465361e-01
-2.13297352e-01 -6.56512082e-01 -8.03450704e-01 -7.03998744e-01
-2.27305099e-01 1.70297205e-01 7.20185220e-01 -7.01151267... | [8.074804306030273, 1.1153432130813599] |
552b4229-99d9-4bbf-805f-67abe338194c | protranslator-zero-shot-protein-function | 2204.10286 | null | https://arxiv.org/abs/2204.10286v1 | https://arxiv.org/pdf/2204.10286v1.pdf | ProTranslator: zero-shot protein function prediction using textual description | Accurately finding proteins and genes that have a certain function is the prerequisite for a broad range of biomedical applications. Despite the encouraging progress of existing computational approaches in protein function prediction, it remains challenging to annotate proteins to a novel function that is not collected... | ['Sheng Wang', 'Hanwen Xu'] | 2022-04-20 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 4.61803287e-01 3.81835550e-02 1.10256311e-03 -5.19725561e-01
-4.11670387e-01 -1.05151737e+00 1.03104100e-01 6.82732821e-01
-2.93881029e-01 1.41403866e+00 -4.60978374e-02 -5.08318007e-01
-1.36623815e-01 -9.18062508e-01 -7.88569212e-01 -9.66583788e-01
1.92407042e-01 5.64289093e-01 3.49603146e-01 -6.09145872... | [4.784022331237793, 5.572320938110352] |
10137e35-80f9-4fb2-9332-6fdd0c96c28b | fedst-federated-shapelet-transformation-for | 2302.10631 | null | https://arxiv.org/abs/2302.10631v3 | https://arxiv.org/pdf/2302.10631v3.pdf | FedST: Secure Federated Shapelet Transformation for Time Series Classification | This paper explores how to customize time series classification (TSC) methods with the help of external data in a privacy-preserving federated learning (FL) scenario. To the best of our knowledge, we are the first to study on this essential topic. Achieving this goal requires us to seamlessly integrate the techniques f... | ['Hongzhi Wang', 'Zhiyu Liang'] | 2023-02-21 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 4.26674485e-02 -3.94868910e-01 -1.34106740e-01 -1.22259289e-01
-7.90607929e-01 -9.07513142e-01 3.35235715e-01 2.35227048e-01
-3.48867744e-01 4.00923520e-01 -1.97595190e-02 -6.51469588e-01
-2.64456987e-01 -7.52193272e-01 -6.05258286e-01 -8.70667875e-01
-3.87193710e-01 -1.15941577e-01 2.88354248e-01 -4.39752936... | [5.861489772796631, 6.602710723876953] |
5e4a4169-5c06-4888-96a0-c827e1480c5b | a-toolkit-for-data-driven-discovery-of | 2111.04870 | null | https://arxiv.org/abs/2111.04870v2 | https://arxiv.org/pdf/2111.04870v2.pdf | A toolkit for data-driven discovery of governing equations in high-noise regimes | We consider the data-driven discovery of governing equations from time-series data in the limit of high noise. The algorithms developed describe an extensive toolkit of methods for circumventing the deleterious effects of noise in the context of the sparse identification of nonlinear dynamics (SINDy) framework. We offe... | ['J. Nathan Kutz', 'Charles B. Delahunt'] | 2021-11-08 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 1.79256767e-01 -2.64489532e-01 4.40108478e-01 3.68484929e-02
-8.48936558e-01 -6.42393827e-01 4.66085017e-01 -3.43862325e-01
-3.12566906e-02 9.19007242e-01 3.57193798e-02 -4.03345197e-01
-7.24276900e-01 -5.05654871e-01 -4.62888718e-01 -1.06359577e+00
-3.90407324e-01 3.34077150e-01 -1.26286387e-01 -2.55361289... | [6.604948997497559, 3.4997315406799316] |
4b2304cd-879e-441b-99fd-de8edd8ebeb1 | binary-single-dimensional-convolutional | 2206.07518 | null | https://arxiv.org/abs/2206.07518v1 | https://arxiv.org/pdf/2206.07518v1.pdf | Binary Single-dimensional Convolutional Neural Network for Seizure Prediction | Nowadays, several deep learning methods are proposed to tackle the challenge of epileptic seizure prediction. However, these methods still cannot be implemented as part of implantable or efficient wearable devices due to their large hardware and corresponding high-power consumption. They usually require complex feature... | ['Mohamad Sawan', 'Yankun Xu', 'Jie Yang', 'Shiqi Zhao'] | 2022-06-08 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [-1.25427932e-01 -4.05345351e-01 1.45798415e-01 -3.72827441e-01
-2.98243076e-01 -8.70512277e-02 1.82223432e-02 2.36893952e-01
-7.27804005e-01 8.02567482e-01 -2.70036280e-01 -3.33765060e-01
-3.59315932e-01 -6.14263356e-01 -3.35669845e-01 -6.15249574e-01
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5631195c-ff76-4879-a7af-a9b361eacb02 | occlusion-robust-3d-human-pose-estimation | 2304.12069 | null | https://arxiv.org/abs/2304.12069v1 | https://arxiv.org/pdf/2304.12069v1.pdf | Occlusion Robust 3D Human Pose Estimation with StridedPoseGraphFormer and Data Augmentation | Occlusion is an omnipresent challenge in 3D human pose estimation (HPE). In spite of the large amount of research dedicated to 3D HPE, only a limited number of studies address the problem of occlusion explicitly. To fill this gap, we propose to combine exploitation of spatio-temporal features with synthetic occlusion a... | ['Alois Knoll', 'Alejandro Mendoza Garcia', 'Patricia Gschoßmann', 'Soubarna Banik'] | 2023-04-24 | null | null | null | null | ['3d-human-pose-estimation', 'occlusion-handling'] | ['computer-vision', 'computer-vision'] | [-5.15654758e-02 2.29232207e-01 8.64424035e-02 -6.28491268e-02
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1134ae55-792f-494d-a6a3-9544e2424d51 | towards-context-aware-interaction-recognition-1 | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Zhuang_Towards_Context-Aware_Interaction_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Zhuang_Towards_Context-Aware_Interaction_ICCV_2017_paper.pdf | Towards Context-Aware Interaction Recognition for Visual Relationship Detection | Recognizing how objects interact with each other is a crucial task in visual recognition. If we define the context of the interaction to be the objects involved, then most current methods can be categorized as either: (i) training a single classifier on the combination of the interaction and its context; or (ii) aiming... | ['Chunhua Shen', 'Lingqiao Liu', 'Bohan Zhuang', 'Ian Reid'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['visual-relationship-detection'] | ['computer-vision'] | [ 5.74165523e-01 -2.43581474e-01 2.41075503e-03 -3.21277738e-01
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1.99289113e-01 3.72326672e-01 4.10526752e-01 -1.34763777... | [9.795336723327637, 2.398699998855591] |
577401a9-e213-443b-9e53-5e3632eee8ae | how-do-i-update-my-model-on-the-resilience-of | 2109.03501 | null | https://arxiv.org/abs/2109.03501v1 | https://arxiv.org/pdf/2109.03501v1.pdf | How do I update my model? On the resilience of Predictive Process Monitoring models to change | Existing well investigated Predictive Process Monitoring techniques typically construct a predictive model based on past process executions, and then use it to predict the future of new ongoing cases, without the possibility of updating it with new cases when they complete their execution. This can make Predictive Proc... | ['Fabrizio Maria Maggi', 'Chiara Ghidini', 'Chiara Di Francescomarino', 'Williams Rizzi1'] | 2021-09-08 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 5.93362808e-01 3.85902151e-02 5.23659363e-02 -4.82988022e-02
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b410b40e-e7b3-4885-8e21-9e52b0e5944d | pests-persian-english-cross-lingual-corpus | 2305.07893 | null | https://arxiv.org/abs/2305.07893v1 | https://arxiv.org/pdf/2305.07893v1.pdf | PESTS: Persian_English Cross Lingual Corpus for Semantic Textual Similarity | One of the components of natural language processing that has received a lot of investigation recently is semantic textual similarity. In computational linguistics and natural language processing, assessing the semantic similarity of words, phrases, paragraphs, and texts is crucial. Calculating the degree of semantic r... | ['Behrouz Minaei Bidgoli', 'Poorya Piroozfar', 'Mohammad Abdous'] | 2023-05-13 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [-8.44279975e-02 -1.84050992e-01 -8.76407698e-02 -6.00547314e-01
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6.34807229e-01 6.30148172e-01 3.34433258e-01 -6.88858628... | [10.793557167053223, 9.664199829101562] |
ff30baa0-edbc-4ce2-b9a6-b93237f0d1ea | improving-transformer-based-networks-with | 2302.08639 | null | https://arxiv.org/abs/2302.08639v2 | https://arxiv.org/pdf/2302.08639v2.pdf | Improving Transformer-based Networks With Locality For Automatic Speaker Verification | Recently, Transformer-based architectures have been explored for speaker embedding extraction. Although the Transformer employs the self-attention mechanism to efficiently model the global interaction between token embeddings, it is inadequate for capturing short-range local context, which is essential for the accurate... | ['Jian Wu', 'John H. L. Hansen', 'Gang Liu', 'Yong Zhao', 'Mufan Sang'] | 2023-02-17 | null | null | null | null | ['speaker-verification'] | ['speech'] | [-1.82260871e-01 -8.23349729e-02 2.11326167e-01 -5.40676355e-01
-1.07881212e+00 -2.70587444e-01 5.44314981e-01 -1.63912214e-02
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-3.78122181e-02 -5.02373353e-02 7.83689171e-02 -1.36807725... | [14.302988052368164, 6.091313362121582] |
a4ec6255-27f1-4faa-a65a-8789a2990dbe | spatial-temporal-hypergraph-self-supervised | 2204.08587 | null | https://arxiv.org/abs/2204.08587v2 | https://arxiv.org/pdf/2204.08587v2.pdf | Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction | Crime has become a major concern in many cities, which calls for the rising demand for timely predicting citywide crime occurrence. Accurate crime prediction results are vital for the beforehand decision-making of government to alleviate the increasing concern about the public safety. While many efforts have been devot... | ['Jian Pei', 'Yong Xu', 'Lianghao Xia', 'Chao Huang', 'Zhonghang Li'] | 2022-04-18 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [ 5.35658449e-02 -2.15491444e-01 -5.78821063e-01 -4.84795302e-01
-7.67568827e-01 -1.56679854e-01 6.40968025e-01 5.77046216e-01
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-5.83506338e-02 9.77602899e-02 1.32892411e-02 -1.06517844... | [6.658615589141846, 2.0293571949005127] |
a77a0a22-8d1c-45c6-9999-ce8fca1d4cee | problems-in-evaluating-grammatical-error | null | null | https://aclanthology.org/C12-1038 | https://aclanthology.org/C12-1038.pdf | Problems in Evaluating Grammatical Error Detection Systems | null | ['Martin Chodorow', 'Markus Dickinson', 'Joel Tetreault', 'Ross Israel'] | 2012-12-01 | problems-in-evaluating-grammatical-error-1 | https://aclanthology.org/C12-1038 | https://aclanthology.org/C12-1038.pdf | coling-2012-12 | ['grammatical-error-detection'] | ['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
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.385275840759277, 3.770860433578491] |
228d8b05-6cb8-42f3-adb5-37dd6365d6cf | delphic-offline-reinforcement-learning-under | 2306.01157 | null | https://arxiv.org/abs/2306.01157v1 | https://arxiv.org/pdf/2306.01157v1.pdf | Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding | A prominent challenge of offline reinforcement learning (RL) is the issue of hidden confounding: unobserved variables may influence both the actions taken by the agent and the observed outcomes. Hidden confounding can compromise the validity of any causal conclusion drawn from data and presents a major obstacle to effe... | ['Guy Tennenholtz', 'Gunnar Rätsch', 'Bernhard Schölkopf', 'Hugo Yèche', 'Alizée Pace'] | 2023-06-01 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 1.04462713e-01 6.35790944e-01 -5.02087653e-01 -1.04447097e-01
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-4.58661675e-01 5.52360833e-01 -4.49957311e-01 2.79949307... | [4.185606956481934, 2.6475534439086914] |
bc88a528-3ff6-4e7a-aea4-cdde4b947d0c | one-bit-covariance-reconstruction-with-non | 2303.16455 | null | https://arxiv.org/abs/2303.16455v1 | https://arxiv.org/pdf/2303.16455v1.pdf | One-Bit Covariance Reconstruction with Non-zero Thresholds: Algorithm and Performance Analysis | Covariance matrix reconstruction is a topic of great significance in the field of one-bit signal processing and has numerous practical applications. Despite its importance, the conventional arcsine law with zero threshold is incapable of recovering the diagonal elements of the covariance matrix. To address this limitat... | ['Hing Cheung So', 'Cheng Qian', 'David Ramírez', 'Lei Huang', 'Yu-Hang Xiao'] | 2023-03-29 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 4.41006869e-01 -5.14914870e-01 -1.85784395e-03 -2.33019993e-01
-8.60841155e-01 -6.98544323e-01 1.65074915e-01 6.44242167e-02
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-3.58644724e-01 -3.24615121e-01 2.72739172e-01 5.63445389... | [6.508528232574463, 1.4404773712158203] |
f79c535e-d83e-482f-8d7b-d3d27873e48c | sevggnet-lstm-a-fused-deep-learning-model-for | 2210.17111 | null | https://arxiv.org/abs/2210.17111v1 | https://arxiv.org/pdf/2210.17111v1.pdf | SEVGGNet-LSTM: a fused deep learning model for ECG classification | This paper presents a fused deep learning algorithm for ECG classification. It takes advantages of the combined convolutional and recurrent neural network for ECG classification, and the weight allocation capability of attention mechanism. The input ECG signals are firstly segmented and normalized, and then fed into th... | ['Yicong Zhou', 'Wei Wang', 'Junxin Chen', 'Yiming Chen', 'Tongyue He'] | 2022-10-31 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 2.79404342e-01 -1.77573249e-01 -1.67962193e-01 -3.36827874e-01
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-5.26553929e-01 -3.85687202e-01 -8.18885118e-02 -1.03063561... | [14.281083106994629, 3.269165277481079] |
14b905dc-1e80-4a3a-b5fe-8ef1fdf448a3 | contextualized-emotion-recognition-in | null | null | https://aclanthology.org/2020.sigdial-1.23 | https://aclanthology.org/2020.sigdial-1.23.pdf | Contextualized Emotion Recognition in Conversation as Sequence Tagging | Emotion recognition in conversation (ERC) is an important topic for developing empathetic machines in a variety of areas including social opinion mining, health-care and so on. In this paper, we propose a method to model ERC task as sequence tagging where a Conditional Random Field (CRF) layer is leveraged to learn the... | ['Jing Xiao', 'Shaojun Wang', 'Jun Ma', 'Jiayu Zhang', 'Yan Wang'] | 2020-07-01 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-1.14579916e-01 3.40571135e-01 -9.30639878e-02 -1.10980356e+00
-6.46523356e-01 -1.43174499e-01 3.13933969e-01 -2.97436919e-02
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2.53190994e-02 1.28369510e-01 -5.07016599e-01 -2.69120008... | [13.01596736907959, 6.107749938964844] |
9beea27b-9424-4207-ba97-e2d53b4d49f0 | coupling-natural-language-processing-and | null | null | https://aclanthology.org/W15-2815 | https://aclanthology.org/W15-2815.pdf | Coupling Natural Language Processing and Animation Synthesis in Portuguese Sign Language Translation | null | ['C', "Lu{\\'\\i}sa Coheur", 'In{\\^e}s Almeida', 'Sara eias'] | 2015-09-01 | null | null | null | ws-2015-9 | ['sign-language-translation'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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165ffc2e-18b6-4ef4-a2ce-c2eef31c6e13 | linguistic-resources-for-entity-linking | null | null | https://aclanthology.org/L12-1118 | https://aclanthology.org/L12-1118.pdf | Linguistic Resources for Entity Linking Evaluation: from Monolingual to Cross-lingual | To advance information extraction and question answering technologies toward a more realistic path, the U.S. NIST (National Institute of Standards and Technology) initiated the KBP (Knowledge Base Population) task as one of the TAC (Text Analysis Conference) evaluation tracks. It aims to encourage research in automatic... | ['Heng Ji', 'Kira Griffitt', 'Joe Ellis', 'Xuansong Li', 'Stephanie Strassel'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['cross-lingual-entity-linking', 'knowledge-base-population'] | ['natural-language-processing', 'natural-language-processing'] | [-4.76414412e-01 6.05465531e-01 -3.70091707e-01 -3.92271221e-01
-1.42526507e+00 -1.07823467e+00 6.94692194e-01 8.78187597e-01
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-8.65519717e-02 -4.24391210e-01 -3.65774512e-01 3.01745355e-01
1.90127581e-01 1.06974387e+00 4.10929710e-01 -4.77038652... | [9.516894340515137, 9.268341064453125] |
18a4d915-64cf-4333-be11-934b33ef2bd8 | heterogeneous-datasets-for-federated-survival | 2301.12166 | null | https://arxiv.org/abs/2301.12166v2 | https://arxiv.org/pdf/2301.12166v2.pdf | Heterogeneous Datasets for Federated Survival Analysis Simulation | Survival analysis studies time-modeling techniques for an event of interest occurring for a population. Survival analysis found widespread applications in healthcare, engineering, and social sciences. However, the data needed to train survival models are often distributed, incomplete, censored, and confidential. In thi... | ['Matteo Matteucci', 'André Martin', 'Francesco Lattari', 'Eugenio Lomurno', 'Alberto Archetti'] | 2023-01-28 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-2.09532261e-01 -1.36077225e-01 -4.05288815e-01 -5.91699004e-01
-9.27820861e-01 -5.12233734e-01 1.88106894e-01 5.46871603e-01
-2.12566435e-01 1.19784153e+00 2.10142717e-01 -4.19622958e-01
-3.87598455e-01 -8.33236814e-01 -2.62318999e-01 -1.15846944e+00
-2.28546366e-01 6.39358461e-01 -1.82224348e-01 2.66009510... | [6.267098903656006, 6.414536476135254] |
27d8e492-a3a0-4572-af65-ac5154ab88bf | interpretability-analysis-of-heartbeat | null | null | https://doi.org/10.1109/ACCESS.2019.2933473 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8790681 | Interpretability Analysis of Heartbeat Classification Based on Heartbeat Activity’s Global Sequence Features and BiLSTM-Attention Neural Network | Arrhythmia is a disease that threatens human life. Therefore, timely diagnosis of arrhythmia is of great significance in preventing heart disease and sudden cardiac death. The BiLSTM-Attention neural network model with heartbeat activity's global sequence features can effectively improve the accuracy of heartbeat class... | ['Zongmin Wang', 'Runchuan Li', 'Bing Zhou', 'Xingjin Zhang', 'Honghua Dai'] | 2019-08-07 | null | null | null | ieee-access-2019-8 | ['arrhythmia-detection', 'heartbeat-classification', 'electrocardiography-ecg'] | ['medical', 'medical', 'methodology'] | [ 7.04176426e-02 -4.69091982e-01 -7.09094107e-02 1.45933136e-01
-4.58289683e-01 -8.03573579e-02 -3.72792929e-01 -2.67465919e-01
-3.04002315e-01 7.60442078e-01 4.18042503e-02 -3.75889689e-01
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-2.16880709e-01 -7.26144239e-02 -7.41077214e-02 -3.27892676... | [14.27821159362793, 3.2668044567108154] |
c0658ba3-8e2b-4bd2-a421-7aee6f736e30 | a-fluctuation-smoothing-approach-for | null | null | https://aclanthology.org/W16-4911 | https://aclanthology.org/W16-4911.pdf | A Fluctuation Smoothing Approach for Unsupervised Automatic Short Answer Grading | We offer a fluctuation smoothing computational approach for unsupervised automatic short answer grading (ASAG) techniques in the educational ecosystem. A major drawback of the existing techniques is the significant effect that variations in model answers could have on their performances. The proposed fluctuation smooth... | ['ipan', 'D', 'Shourya Roy', 'Y. Narahari', 'S apat'] | 2016-12-01 | null | null | null | ws-2016-12 | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 4.31907065e-02 3.80894467e-02 -1.46661364e-02 -4.15799499e-01
-7.44997144e-01 -7.52692521e-01 5.04089952e-01 8.17312896e-01
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-8.67988110e-01 -8.69671583e-01 -5.76280475e-01 -2.69396335e-01
2.12467372e-01 8.70657153e-03 7.96309054e-01 -4.71563101... | [10.320199012756348, 7.457664966583252] |
a4eca47b-fb78-4bc0-8ed5-5a9bf9448ddd | accurate-unsupervised-joint-named-entity | null | null | https://aclanthology.org/W12-4403 | https://aclanthology.org/W12-4403.pdf | Accurate Unsupervised Joint Named-Entity Extraction from Unaligned Parallel Text | null | ['Christopher D. Manning', 'Robert Munro'] | 2012-07-01 | accurate-unsupervised-joint-named-entity-1 | https://aclanthology.org/W12-4403 | https://aclanthology.org/W12-4403.pdf | ws-2012-7 | ['cross-domain-named-entity-recognition'] | ['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 -6.93831444... | [-7.28092098236084, 3.720447540283203] |
9581ff8b-1cc4-4076-8878-3048009f8212 | paired-point-lifting-for-enhanced-privacy | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lee_Paired-Point_Lifting_for_Enhanced_Privacy-Preserving_Visual_Localization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lee_Paired-Point_Lifting_for_Enhanced_Privacy-Preserving_Visual_Localization_CVPR_2023_paper.pdf | Paired-Point Lifting for Enhanced Privacy-Preserving Visual Localization | Visual localization refers to the process of recovering camera pose from input image relative to a known scene, forming a cornerstone of numerous vision and robotics systems. While many algorithms utilize sparse 3D point cloud of the scene obtained via structure-from-motion (SfM) for localization, recent studies ha... | ['Je Hyeong Hong', 'Chanhyuk Yun', 'Jaihoon Kim', 'Chunghwan Lee'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['visual-localization'] | ['computer-vision'] | [ 3.13596517e-01 -1.65162638e-01 -4.79735360e-02 -5.69804683e-02
-6.99482679e-01 -1.02065337e+00 4.41594034e-01 1.29812956e-01
-3.27859402e-01 3.83012116e-01 -8.67724568e-02 -4.66420919e-01
1.05994008e-01 -5.97701848e-01 -9.81388450e-01 -6.81754231e-01
-4.91330177e-02 -1.27547696e-01 1.93549350e-01 1.40157193... | [7.563477516174316, -2.1857566833496094] |
07baddd8-0b44-41f5-8e40-9278965ab39c | student-become-decathlon-master-in-retinal | 2203.03631 | null | https://arxiv.org/abs/2203.03631v3 | https://arxiv.org/pdf/2203.03631v3.pdf | Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain Adaptation | Unsupervised domain adaptation has been proposed recently to tackle the so-called domain shift between training data and test data with different distributions. However, most of them only focus on single-target domain adaptation and cannot be applied to the scenario with multiple target domains. In this paper, we propo... | ['Xiaoying Tang', 'Huaqing He', 'Pujin Cheng', 'Li Lin', 'Linkai Peng'] | 2022-03-07 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 3.32650900e-01 7.77485669e-02 -3.60327899e-01 -4.35631037e-01
-9.08314884e-01 -7.89879978e-01 3.10804635e-01 -5.11632068e-03
-4.21615958e-01 7.70582795e-01 -9.65691805e-02 -2.01075584e-01
-4.52817045e-03 -5.08183718e-01 -5.55789590e-01 -9.82864559e-01
5.49415946e-01 7.49374747e-01 6.31012917e-01 -5.73835075... | [14.563664436340332, -1.9131624698638916] |
4076eae6-032b-4fd1-a5a2-afd1e3088230 | facial-descriptors-for-human-interaction | 1509.05366 | null | http://arxiv.org/abs/1509.05366v1 | http://arxiv.org/pdf/1509.05366v1.pdf | Facial Descriptors for Human Interaction Recognition In Still Images | This paper presents a novel approach in a rarely studied area of computer
vision: Human interaction recognition in still images. We explore whether the
facial regions and their spatial configurations contribute to the recognition
of interactions. In this respect, our method involves extraction of several
visual feature... | ['Nazli Ikizler-Cinbis', 'Gokhan Tanisik', 'Cemil Zalluhoglu'] | 2015-09-17 | null | null | null | null | ['human-interaction-recognition'] | ['computer-vision'] | [ 3.33944075e-02 -4.36604947e-01 -1.08176842e-01 -7.95539439e-01
-7.25553259e-02 -4.09031659e-01 8.95025492e-01 -1.79124475e-01
-2.51914531e-01 4.46829706e-01 3.77696246e-01 5.19765079e-01
-1.40977770e-01 -4.78026241e-01 -4.08713967e-01 -8.54258478e-01
-3.32577527e-01 2.56775558e-01 2.44080331e-02 -1.89950675... | [13.25954818725586, 0.7585422992706299] |
5045ae77-dcee-47f2-b53c-5c127690e21f | near-optimal-fully-first-order-algorithms-for | 2306.14853 | null | https://arxiv.org/abs/2306.14853v1 | https://arxiv.org/pdf/2306.14853v1.pdf | Near-Optimal Fully First-Order Algorithms for Finding Stationary Points in Bilevel Optimization | Bilevel optimization has various applications such as hyper-parameter optimization and meta-learning. Designing theoretically efficient algorithms for bilevel optimization is more challenging than standard optimization because the lower-level problem defines the feasibility set implicitly via another optimization probl... | ['Jingzhao Zhang', 'Yaohua Ma', 'Lesi Chen'] | 2023-06-26 | null | null | null | null | ['meta-learning', 'bilevel-optimization'] | ['methodology', 'methodology'] | [-2.85476029e-01 2.53018558e-01 -2.27220610e-01 5.24467528e-02
-1.20218849e+00 -8.19346309e-01 -1.45244673e-01 2.66359508e-01
-7.05474019e-01 8.21580887e-01 -5.66619337e-01 -8.70831311e-01
-8.93166125e-01 -8.13732445e-01 -1.08380353e+00 -8.66189599e-01
-6.10693276e-01 6.40117407e-01 -7.68990442e-02 -2.36004770... | [6.451167583465576, 4.518571376800537] |
4086fd19-0c2c-4135-8e83-b30d808d5433 | audio2gestures-generating-diverse-gestures | 2108.06720 | null | https://arxiv.org/abs/2108.06720v1 | https://arxiv.org/pdf/2108.06720v1.pdf | Audio2Gestures: Generating Diverse Gestures from Speech Audio with Conditional Variational Autoencoders | Generating conversational gestures from speech audio is challenging due to the inherent one-to-many mapping between audio and body motions. Conventional CNNs/RNNs assume one-to-one mapping, and thus tend to predict the average of all possible target motions, resulting in plain/boring motions during inference. In order ... | ['Linchao Bao', 'Zhenyu He', 'Ying Zhang', 'Xuefei Zhe', 'Wenjie Pei', 'Di Kang', 'Jing Li'] | 2021-08-15 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Li_Audio2Gestures_Generating_Diverse_Gestures_From_Speech_Audio_With_Conditional_Variational_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Li_Audio2Gestures_Generating_Diverse_Gestures_From_Speech_Audio_With_Conditional_Variational_ICCV_2021_paper.pdf | iccv-2021-1 | ['gesture-generation'] | ['robots'] | [-2.24353105e-01 -1.56435162e-01 -2.73796678e-01 -1.54477447e-01
-1.05140626e+00 -4.73924637e-01 4.72157598e-01 -9.69295919e-01
2.40393505e-02 4.67310548e-01 7.87160993e-01 1.51961491e-01
2.05880180e-01 -4.14620697e-01 -7.54172206e-01 -1.08817816e+00
-5.58264330e-02 4.06182796e-01 4.15457934e-02 -1.36263981... | [5.765839576721191, -0.1945086121559143] |
80449f99-d517-4e95-acab-b02bccad1ed1 | mobileface-3d-face-reconstruction-with | 1809.08809 | null | http://arxiv.org/abs/1809.08809v1 | http://arxiv.org/pdf/1809.08809v1.pdf | MobileFace: 3D Face Reconstruction with Efficient CNN Regression | Estimation of facial shapes plays a central role for face transfer and
animation. Accurate 3D face reconstruction, however, often deploys iterative
and costly methods preventing real-time applications. In this work we design a
compact and fast CNN model enabling real-time face reconstruction on mobile
devices. For this... | ['Ivan Laptev', 'Nikolai Chinaev', 'Alexander Chigorin'] | 2018-09-24 | null | null | null | null | ['face-transfer'] | ['computer-vision'] | [-5.81360683e-02 -8.15961603e-03 2.59124450e-02 -4.42176014e-01
-4.39964592e-01 -3.73861790e-01 3.97134513e-01 -6.26860678e-01
-2.54725307e-01 3.83142322e-01 -2.46860281e-01 -4.59246606e-01
3.92887414e-01 -7.20106483e-01 -8.32007051e-01 -1.58640951e-01
3.87793332e-02 6.86404228e-01 1.15650885e-01 -1.60903841... | [13.149993896484375, 0.005481123458594084] |
1b7b219c-7c1a-46a4-af08-4fd173cb7e84 | recurrent-environment-simulators | 1704.02254 | null | http://arxiv.org/abs/1704.02254v2 | http://arxiv.org/pdf/1704.02254v2.pdf | Recurrent Environment Simulators | Models that can simulate how environments change in response to actions can
be used by agents to plan and act efficiently. We improve on previous
environment simulators from high-dimensional pixel observations by introducing
recurrent neural networks that are able to make temporally and spatially
coherent predictions f... | ['Sébastien Racaniere', 'Shakir Mohamed', 'Daan Wierstra', 'Silvia Chiappa'] | 2017-04-07 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [ 1.47504240e-01 9.45584557e-04 3.45019370e-01 -1.26584053e-01
-3.02947819e-01 -5.68607211e-01 6.20529413e-01 -1.09508157e-01
-7.13385046e-01 9.06559229e-01 3.56748730e-01 -5.25910914e-01
-6.13043159e-02 -1.09852397e+00 -6.58081472e-01 -4.46657866e-01
-6.19428039e-01 4.65682894e-01 5.32854736e-01 -5.06993890... | [4.019692897796631, 1.349628210067749] |
e1c4e3de-9d6f-4ea9-8239-6615ad578d2c | the-future-of-human-centric-explainable | 2307.00364 | null | https://arxiv.org/abs/2307.00364v1 | https://arxiv.org/pdf/2307.00364v1.pdf | The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations | Explainable Artificial Intelligence (XAI) plays a crucial role in enabling human understanding and trust in deep learning systems, often defined as determining which features are most important to a model's prediction. As models get larger, more ubiquitous, and pervasive in aspects of daily life, explainability is nece... | ['Tanja Käser', 'Jibril Frej', 'Vinitra Swamy'] | 2023-07-01 | null | null | null | null | ['explainable-artificial-intelligence'] | ['computer-vision'] | [ 4.81277108e-02 8.32987368e-01 -6.28705695e-02 -9.22160208e-01
1.08275682e-01 -2.22226918e-01 4.37625676e-01 -5.70089221e-02
5.89124067e-03 6.42559826e-01 1.86475098e-01 -8.90293360e-01
-6.68598592e-01 -5.66299200e-01 -8.53276372e-01 2.77057500e-03
9.84235555e-02 1.00458992e+00 -5.83961904e-01 -9.85337943... | [8.829498291015625, 5.855291843414307] |
bc9a565a-5966-4d3a-a470-dc3680b0653b | do-we-need-entire-training-data-for | 2303.06241 | null | https://arxiv.org/abs/2303.06241v2 | https://arxiv.org/pdf/2303.06241v2.pdf | Do we need entire training data for adversarial training? | Deep Neural Networks (DNNs) are being used to solve a wide range of problems in many domains including safety-critical domains like self-driving cars and medical imagery. DNNs suffer from vulnerability against adversarial attacks. In the past few years, numerous approaches have been proposed to tackle this problem by t... | ['Apurva Narayan', 'Vipul Gupta'] | 2023-03-10 | null | null | null | null | ['self-driving-cars'] | ['computer-vision'] | [ 4.06813264e-01 1.20794021e-01 2.53838122e-01 -3.54018748e-01
-7.40108013e-01 -8.81190121e-01 5.11670530e-01 -2.28367135e-01
-9.14428294e-01 7.30136871e-01 -3.60300869e-01 -5.86635828e-01
3.14234436e-01 -1.12617433e+00 -1.09203792e+00 -6.99379861e-01
-7.28394836e-02 4.30456907e-01 5.27544558e-01 -5.00624955... | [5.597856521606445, 7.871998310089111] |
ad12d8d6-28bb-465c-8243-168e42f157af | furniturebench-reproducible-real-world | 2305.12821 | null | https://arxiv.org/abs/2305.12821v1 | https://arxiv.org/pdf/2305.12821v1.pdf | FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation | Reinforcement learning (RL), imitation learning (IL), and task and motion planning (TAMP) have demonstrated impressive performance across various robotic manipulation tasks. However, these approaches have been limited to learning simple behaviors in current real-world manipulation benchmarks, such as pushing or pick-an... | ['Joseph J. Lim', 'Doohyun Lee', 'Youngwoon Lee', 'Minho Heo'] | 2023-05-22 | null | null | null | null | ['offline-rl', 'robot-manipulation', 'motion-planning'] | ['playing-games', 'robots', 'robots'] | [-5.24970852e-02 -2.54161894e-01 -2.88337290e-01 -5.57825901e-02
-5.78663886e-01 -8.98550868e-01 4.77409452e-01 -7.72477910e-02
-3.15683573e-01 9.59700286e-01 -1.05749220e-01 -3.81077528e-01
-6.69928074e-01 -2.56033123e-01 -1.04234970e+00 -3.14037621e-01
-7.13920414e-01 8.69045436e-01 2.66865611e-01 -5.36705792... | [4.658136367797852, 0.7139935493469238] |
1e3aadab-62d1-4363-a64a-c97f7610bfab | reproducible-biomarkers-leveraging-nonlinear | 2305.06954 | null | https://arxiv.org/abs/2305.06954v1 | https://arxiv.org/pdf/2305.06954v1.pdf | Reproducible biomarkers: Leveraging nonlinear descriptors in the face of non-ergodicity | Any reliable biomarker has to be specific, generalizable, and reproducible across individuals and contexts. The exact values of such a biomarker must represent similar health states in different individuals and at different times within the same individual to result in the minimum possible false-positive and false-nega... | ['Damian G. Kelty-Stephen', 'Ken Kiyono', 'Eiichi Watanabe', 'Junichiro Hayano', 'Arash Sadri', 'Madhur Mangalam'] | 2023-05-11 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 1.53379411e-01 -3.22640330e-01 -1.94384515e-01 3.82037796e-02
-3.16188902e-01 -6.51844323e-01 6.67645216e-01 3.86754036e-01
-7.50668645e-02 1.01139724e+00 4.40541320e-02 -3.18686962e-01
-6.11214876e-01 -6.93525612e-01 -3.18861276e-01 -1.00022376e+00
-8.30555320e-01 2.51748204e-01 1.88124150e-01 7.43999854... | [6.478990077972412, 4.25870943069458] |
a07aef91-a78d-451f-907e-38c8518345f2 | end-to-end-deep-multi-score-model-for-no-1 | 2211.01374 | null | https://arxiv.org/abs/2211.01374v1 | https://arxiv.org/pdf/2211.01374v1.pdf | End-to-end deep multi-score model for No-reference stereoscopic image quality assessment | Deep learning-based quality metrics have recently given significant improvement in Image Quality Assessment (IQA). In the field of stereoscopic vision, information is evenly distributed with slight disparity to the left and right eyes. However, due to asymmetric distortion, the objective quality ratings for the left an... | ['Aladine Chetouani', 'Oussama Messai'] | 2022-11-02 | end-to-end-deep-multi-score-model-for-no | https://ieeexplore.ieee.org/abstract/document/9897616 | https://www.researchgate.net/publication/364995366_End-to-end_deep_multi-score_model_for_No-reference_stereoscopic_image_quality_assessment | icip2022-2022-10 | ['stereoscopic-image-quality-assessment'] | ['computer-vision'] | [-1.01836637e-01 -1.97083652e-01 7.07311854e-02 -3.85659993e-01
-7.91120052e-01 -2.53768653e-01 2.00101331e-01 -2.48993903e-01
-2.89363354e-01 5.75221241e-01 4.96155739e-01 -3.91650535e-02
-1.25910491e-01 -6.78455114e-01 -4.68997836e-01 -6.25522494e-01
1.91476554e-01 -8.50859284e-03 2.01357394e-01 6.06032386... | [11.779619216918945, -1.9492688179016113] |
6606eef1-a562-472a-afd7-d231a13c2687 | fast-forward-through-opportunistic | null | null | https://aclanthology.org/P17-3019 | https://aclanthology.org/P17-3019.pdf | Fast Forward Through Opportunistic Incremental Meaning Representation Construction | null | ['Sergei Nirenburg', 'Petr Babkin'] | 2017-07-01 | null | null | null | acl-2017-7 | ['dialogue-understanding'] | ['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 -6.93831444... | [-7.205490589141846, 3.595468044281006] |
3a4bd95c-14e4-40ed-a01a-ba56bf66346e | classification-and-explanation-of-distributed | 2306.17190 | null | https://arxiv.org/abs/2306.17190v1 | https://arxiv.org/pdf/2306.17190v1.pdf | Classification and Explanation of Distributed Denial-of-Service (DDoS) Attack Detection using Machine Learning and Shapley Additive Explanation (SHAP) Methods | DDoS attacks involve overwhelming a target system with a large number of requests or traffic from multiple sources, disrupting the normal traffic of a targeted server, service, or network. Distinguishing between legitimate traffic and malicious traffic is a challenging task. It is possible to classify legitimate traffi... | ['Seyit Camtepe', 'Fariza Sabrina', 'Amardeep Singh', 'Julian Jang-Jaccard', 'Yuanyuan Wei'] | 2023-06-27 | null | null | null | null | ['explainable-artificial-intelligence', 'feature-importance', 'decision-making'] | ['computer-vision', 'methodology', 'reasoning'] | [-3.40227067e-01 -2.21523315e-01 -4.21600997e-01 -3.68947417e-01
1.66981861e-01 -2.79173315e-01 3.18099976e-01 -2.46140212e-01
4.07488406e-01 4.62941736e-01 -2.03558162e-01 -8.31208050e-01
-2.90620267e-01 -9.20570612e-01 -2.91427106e-01 -6.28573537e-01
-9.63845477e-02 6.52278543e-01 1.68037772e-01 3.42478640... | [5.090543270111084, 7.23425817489624] |
ec62c717-17da-4aad-b3c7-e188d351dc3a | significant-ties-graph-neural-networks-for | 2211.06590 | null | https://arxiv.org/abs/2211.06590v1 | https://arxiv.org/pdf/2211.06590v1.pdf | Significant Ties Graph Neural Networks for Continuous-Time Temporal Networks Modeling | Temporal networks are suitable for modeling complex evolving systems. It has a wide range of applications, such as social network analysis, recommender systems, and epidemiology. Recently, modeling such dynamic systems has drawn great attention in many domains. However, most existing approaches resort to taking discret... | ['Li Tao', 'Yansong Wang', 'Tao Jia', 'Jiayun Wu'] | 2022-11-12 | null | null | null | null | ['epidemiology'] | ['medical'] | [-2.39083692e-01 -6.61640167e-02 -6.45326138e-01 -2.25695342e-01
5.52523375e-01 -1.11898221e-01 5.63973546e-01 6.70687973e-01
-7.22089782e-02 8.99725974e-01 1.73217565e-01 -2.32439756e-01
-9.44995880e-01 -1.16115761e+00 -2.11959943e-01 -5.43003917e-01
-7.60788739e-01 4.18100357e-01 5.69280028e-01 -2.93312967... | [7.195656776428223, 5.855517387390137] |
a381e7d4-a45d-4be5-bbc7-76e7ebe28ba1 | egocentric-pose-estimation-from-human-vision | 2104.05167 | null | https://arxiv.org/abs/2104.05167v1 | https://arxiv.org/pdf/2104.05167v1.pdf | Egocentric Pose Estimation from Human Vision Span | Estimating camera wearer's body pose from an egocentric view (egopose) is a vital task in augmented and virtual reality. Existing approaches either use a narrow field of view front facing camera that barely captures the wearer, or an extruded head-mounted top-down camera for maximal wearer visibility. In this paper, we... | ['Vamsi Krishna Ithapu', 'Hao Jiang'] | 2021-04-12 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Jiang_Egocentric_Pose_Estimation_From_Human_Vision_Span_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Jiang_Egocentric_Pose_Estimation_From_Human_Vision_Span_ICCV_2021_paper.pdf | iccv-2021-1 | ['egocentric-pose-estimation'] | ['computer-vision'] | [-1.41864121e-01 2.15848889e-02 -2.39061508e-02 -3.11546117e-01
-3.01620692e-01 -3.86728853e-01 1.19158730e-01 -5.25704801e-01
-2.27684915e-01 2.65997201e-01 2.28835404e-01 2.67722487e-01
1.27366260e-01 -3.07832807e-01 -8.43940616e-01 -1.95287094e-01
3.78502905e-01 4.13848758e-01 6.79280758e-02 -2.21439451... | [7.072167873382568, -1.0213358402252197] |
638d6734-01c1-47bd-801a-8f72a1e511db | exploiting-modality-invariant-feature-for | 2210.15359 | null | https://arxiv.org/abs/2210.15359v1 | https://arxiv.org/pdf/2210.15359v1.pdf | Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalities | Multimodal emotion recognition leverages complementary information across modalities to gain performance. However, we cannot guarantee that the data of all modalities are always present in practice. In the studies to predict the missing data across modalities, the inherent difference between heterogeneous modalities, n... | ['Haizhou Li', 'Guanglai Gao', 'Jinming Zhao', 'Rui Liu', 'Haolin Zuo'] | 2022-10-27 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 2.13240847e-01 -9.53879207e-02 -3.47971588e-01 -4.98531610e-01
-1.07747757e+00 -4.68863517e-01 5.57626307e-01 -3.10622305e-01
-1.22752793e-01 5.54079831e-01 6.63064063e-01 2.26618588e-01
-1.52904078e-01 -2.44158804e-01 -6.23099148e-01 -7.68370390e-01
4.61129814e-01 -8.61615241e-02 -6.61501586e-01 -2.24669650... | [13.147934913635254, 5.030982494354248] |
16faf190-219f-4038-836a-4dfe489aec05 | the-speaker-independent-lipreading-play-off-a | 1810.10597 | null | http://arxiv.org/abs/1810.10597v1 | http://arxiv.org/pdf/1810.10597v1.pdf | The speaker-independent lipreading play-off; a survey of lipreading machines | Lipreading is a difficult gesture classification task. One problem in
computer lipreading is speaker-independence. Speaker-independence means to
achieve the same accuracy on test speakers not included in the training set as
speakers within the training set. Current literature is limited on
speaker-independent lipreadin... | ['Madhi Saleh', 'Jake Burton', 'Nassir Navab', 'Helen L. Bear', 'David Frank'] | 2018-10-24 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 2.36543626e-01 -1.56637505e-01 -6.38327599e-01 -5.56160748e-01
-1.29326940e+00 -2.96795756e-01 6.91655219e-01 -6.36499941e-01
-5.88045418e-01 9.19601560e-01 4.90977764e-01 -4.80734080e-01
2.90547729e-01 3.47214669e-01 -4.17047560e-01 -8.67437840e-01
3.12669933e-01 5.66292882e-01 -7.63012990e-02 2.49149576... | [14.3162260055542, 5.005472183227539] |
230f0362-3c3e-485d-ac6d-7e1ecb05e2ed | vvs-video-to-video-retrieval-with-irrelevant | 2303.08906 | null | https://arxiv.org/abs/2303.08906v1 | https://arxiv.org/pdf/2303.08906v1.pdf | VVS: Video-to-Video Retrieval with Irrelevant Frame Suppression | In content-based video retrieval (CBVR), dealing with large-scale collections, efficiency is as important as accuracy. For this reason, several video-level feature-based studies have actively been conducted; nevertheless, owing to the severe difficulty of embedding a lengthy and untrimmed video into a single feature, t... | ['Yukyung Choi', 'Byungsoo Ko', 'Hyunwoo Kim', 'Gwangjin Lee', 'Geuntaek Lim', 'Won Jo'] | 2023-03-15 | null | null | null | null | ['video-retrieval'] | ['computer-vision'] | [ 2.80590832e-01 -7.96438456e-01 -2.05588758e-01 2.12444421e-02
-9.06086922e-01 -2.63394237e-01 5.91142297e-01 -5.89623488e-02
-5.06317496e-01 5.06731510e-01 2.03132868e-01 -5.84490318e-03
-3.55320603e-01 -4.79164630e-01 -4.48627263e-01 -7.72453010e-01
-2.05507383e-01 -1.59574255e-01 4.43821043e-01 -2.81687647... | [10.316306114196777, 0.7058624625205994] |
e89f5d23-b7c5-4f3d-9c16-d2214140e50c | representation-learning-for-dynamic | 2112.10154 | null | https://arxiv.org/abs/2112.10154v4 | https://arxiv.org/pdf/2112.10154v4.pdf | Dynamic Representation Learning with Temporal Point Processes for Higher-Order Interaction Forecasting | The explosion of digital information and the growing involvement of people in social networks led to enormous research activity to develop methods that can extract meaningful information from interaction data. Commonly, interactions are represented by edges in a network or a graph, which implicitly assumes that the int... | ['Ambedkar Dukkipati', 'Tony Gracious'] | 2021-12-19 | null | null | null | null | ['hyperedge-prediction'] | ['graphs'] | [ 1.39096424e-01 1.67834684e-01 -3.21716070e-01 -4.78338033e-01
6.20174706e-01 -6.99823976e-01 8.55412722e-01 5.70781529e-01
-3.92304128e-03 9.40191627e-01 4.18215320e-02 -3.82809788e-01
-5.39996624e-01 -1.29368567e+00 -7.66627073e-01 -5.55783629e-01
-8.54361892e-01 9.27802682e-01 6.19299650e-01 -4.08733010... | [7.218418598175049, 5.975901126861572] |
058b1695-1004-442c-9448-619495167e96 | a-decomposition-based-hybrid-ensemble-cnn | 2203.09477 | null | https://arxiv.org/abs/2203.09477v2 | https://arxiv.org/pdf/2203.09477v2.pdf | A Decomposition-Based Hybrid Ensemble CNN Framework for Driver Fatigue Recognition | Electroencephalogram (EEG) has become increasingly popular in driver fatigue monitoring systems. Several decomposition methods have been attempted to analyze the EEG signals that are complex, nonlinear and non-stationary and improve the EEG decoding performance in different applications. However, it remains challenging... | ['P. N. Suganthan', 'Ruobin Gao', 'Ruilin Li'] | 2022-03-14 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 2.59401262e-01 -4.37953383e-01 4.15097564e-01 -3.10438901e-01
-5.53191543e-01 -2.36389324e-01 2.26458862e-01 -4.41960424e-01
-4.76441205e-01 7.12802649e-01 4.41165417e-02 -1.91396788e-01
-3.45469624e-01 -2.00700402e-01 -2.46899083e-01 -1.02146387e+00
9.22357887e-02 -4.36046243e-01 -2.65863359e-01 -3.97421658... | [13.093361854553223, 3.38547420501709] |
c9d4371d-eb7a-4368-8e82-eb1afe86c931 | image-based-indian-sign-language-recognition | 2304.14710 | null | https://arxiv.org/abs/2304.14710v1 | https://arxiv.org/pdf/2304.14710v1.pdf | Image-based Indian Sign Language Recognition: A Practical Review using Deep Neural Networks | People with vocal and hearing disabilities use sign language to express themselves using visual gestures and signs. Although sign language is a solution for communication difficulties faced by deaf people, there are still problems as most of the general population cannot understand this language, creating a communicati... | ['M A Lekhana', 'Sanjam Kaur Bedi', 'Harleen Kaur', 'Mallikharjuna Rao K'] | 2023-04-28 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 5.90399131e-02 -3.81409794e-01 1.78034592e-04 -4.00861412e-01
-2.28548020e-01 -5.17186046e-01 1.74765989e-01 -6.32261813e-01
-7.19423771e-01 5.84618509e-01 5.12749791e-01 -6.93829179e-01
-4.42133434e-02 -5.62861860e-01 -4.03617183e-03 -4.70074952e-01
2.26208135e-01 1.17253155e-01 3.36963952e-01 -4.70636189... | [9.060050964355469, -6.364983558654785] |
cb3868cb-b6b1-4382-a9ea-3fabdd29631f | rgb-d-local-implicit-function-for-depth | 2104.00622 | null | https://arxiv.org/abs/2104.00622v1 | https://arxiv.org/pdf/2104.00622v1.pdf | RGB-D Local Implicit Function for Depth Completion of Transparent Objects | Majority of the perception methods in robotics require depth information provided by RGB-D cameras. However, standard 3D sensors fail to capture depth of transparent objects due to refraction and absorption of light. In this paper, we introduce a new approach for depth completion of transparent objects from a single RG... | ['Dieter Fox', 'Shoubhik Debnath', 'Jozef van Eenbergen', 'Hammad Mazhar', 'Yu Xiang', 'Arsalan Mousavian', 'Luyang Zhu'] | 2021-04-01 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhu_RGB-D_Local_Implicit_Function_for_Depth_Completion_of_Transparent_Objects_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhu_RGB-D_Local_Implicit_Function_for_Depth_Completion_of_Transparent_Objects_CVPR_2021_paper.pdf | cvpr-2021-1 | ['transparent-objects'] | ['computer-vision'] | [ 2.67048270e-01 9.50079784e-02 4.79572207e-01 -5.50495923e-01
-6.28698707e-01 -3.92935723e-01 3.36196214e-01 -1.67996496e-01
-2.65744239e-01 4.97580707e-01 -1.23819984e-01 -2.13550590e-02
3.54524314e-01 -1.15125096e+00 -9.21149731e-01 -4.59366798e-01
2.14206770e-01 5.13395250e-01 9.09322977e-01 1.51573550... | [8.69498062133789, -2.678400993347168] |
3f76a725-6414-44b4-b060-ce045c9ecd52 | learning-spatial-and-temporal-variations-for | 2207.04673 | null | https://arxiv.org/abs/2207.04673v1 | https://arxiv.org/pdf/2207.04673v1.pdf | Learning Spatial and Temporal Variations for 4D Point Cloud Segmentation | LiDAR-based 3D scene perception is a fundamental and important task for autonomous driving. Most state-of-the-art methods on LiDAR-based 3D recognition tasks focus on single frame 3D point cloud data, and the temporal information is ignored in those methods. We argue that the temporal information across the frames prov... | ['Lin Guosheng', 'Liu Fayao', 'Wang Hao', 'Wei Jiacheng', 'Shi Hanyu'] | 2022-07-11 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [-1.52208731e-01 -3.91666025e-01 -3.39232981e-01 -7.25056887e-01
-5.18550575e-01 -2.41284296e-01 5.58059037e-01 6.10256381e-03
-3.62468868e-01 1.45219758e-01 -2.80529380e-01 -3.83766770e-01
-1.12830669e-01 -1.11057639e+00 -8.93330455e-01 -4.84981060e-01
-1.83005691e-01 5.91121256e-01 9.36789155e-01 -4.74063009... | [8.077600479125977, -2.548356533050537] |
3f75acfb-a7f7-4bc9-a147-171cba0f6f4f | panet-lidar-panoptic-segmentation-with-sparse | 2306.15348 | null | https://arxiv.org/abs/2306.15348v1 | https://arxiv.org/pdf/2306.15348v1.pdf | PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and Aggregation | Reliable LiDAR panoptic segmentation (LPS), including both semantic and instance segmentation, is vital for many robotic applications, such as autonomous driving. This work proposes a new LPS framework named PANet to eliminate the dependency on the offset branch and improve the performance on large objects, which are a... | ['Yong liu', 'Laijian Li', 'Xiaojun Hou', 'Mengmeng Wang', 'Yu Yang', 'Jianbiao Mei'] | 2023-06-27 | null | null | null | null | ['panoptic-segmentation', 'instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.20622618e-02 -1.36859044e-01 -1.92371413e-01 -6.23008907e-01
-7.09910452e-01 -3.28572899e-01 4.53992307e-01 1.22165971e-01
-3.18594038e-01 5.45691073e-01 -5.79085052e-01 -4.08421755e-02
-1.71506956e-01 -1.06740487e+00 -7.45021522e-01 -9.50457573e-01
1.25734776e-01 8.05406034e-01 7.70376503e-01 1.25862822... | [8.058475494384766, -2.8934803009033203] |
f1b9fb8a-b84e-4da3-949b-c32854f638f4 | promoting-saliency-from-depth-deep-1 | 2205.07179 | null | https://arxiv.org/abs/2205.07179v1 | https://arxiv.org/pdf/2205.07179v1.pdf | Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection | Growing interests in RGB-D salient object detection (RGB-D SOD) have been witnessed in recent years, owing partly to the popularity of depth sensors and the rapid progress of deep learning techniques. Unfortunately, existing RGB-D SOD methods typically demand large quantity of training images being thoroughly annotated... | ['Li Cheng', 'Jie Liu', 'Chuan Guo', 'Qi Bi', 'Jingjing Li', 'Wei Ji'] | 2022-05-15 | promoting-saliency-from-depth-deep | https://openreview.net/forum?id=BZnnMbt0pW | https://openreview.net/pdf?id=BZnnMbt0pW | iclr-2022-4 | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 5.52691817e-01 3.26388478e-01 -1.84388533e-01 -3.29656363e-01
-7.47607231e-01 -1.38305560e-01 4.65753943e-01 9.51073468e-02
-3.00062150e-01 5.80722272e-01 1.02653585e-01 2.36686934e-02
-1.39792055e-01 -4.66629893e-01 -4.82331276e-01 -8.61013710e-01
3.54701877e-01 5.86606674e-02 6.56390965e-01 -3.05631310... | [9.670427322387695, -0.7807585000991821] |
114a50a4-d8a3-4ca5-b5e4-5da9c6648e6e | sg-shuffle-multi-aspect-shuffle-transformer | 2211.04773 | null | https://arxiv.org/abs/2211.04773v1 | https://arxiv.org/pdf/2211.04773v1.pdf | SG-Shuffle: Multi-aspect Shuffle Transformer for Scene Graph Generation | Scene Graph Generation (SGG) serves a comprehensive representation of the images for human understanding as well as visual understanding tasks. Due to the long tail bias problem of the object and predicate labels in the available annotated data, the scene graph generated from current methodologies can be biased toward ... | ['Josiah Poon', 'Soyeon Caren Han', 'Anh Duc Bui'] | 2022-11-09 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 5.16163468e-01 2.49840975e-01 -5.33088967e-02 -7.54542112e-01
-4.88930196e-01 -5.51901817e-01 6.79651678e-01 2.63365716e-01
1.40954643e-01 7.24290431e-01 3.52203518e-01 -1.84108481e-01
-2.17769854e-02 -1.09790266e+00 -8.65509927e-01 -5.16769826e-01
2.19905719e-01 7.46836066e-01 2.36088216e-01 -4.70878780... | [10.34929370880127, 1.6398999691009521] |
9a6eefc6-a662-4613-97a3-33c870a9e61f | mask-shadowgan-learning-to-remove-shadows | 1903.10683 | null | https://arxiv.org/abs/1903.10683v3 | https://arxiv.org/pdf/1903.10683v3.pdf | Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data | This paper presents a new method for shadow removal using unpaired data, enabling us to avoid tedious annotations and obtain more diverse training samples. However, directly employing adversarial learning and cycle-consistency constraints is insufficient to learn the underlying relationship between the shadow and shado... | ['Pheng-Ann Heng', 'Chi-Wing Fu', 'Xiaowei Hu', 'Yitong Jiang'] | 2019-03-26 | mask-shadowgan-learning-to-remove-shadows-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Hu_Mask-ShadowGAN_Learning_to_Remove_Shadows_From_Unpaired_Data_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Hu_Mask-ShadowGAN_Learning_to_Remove_Shadows_From_Unpaired_Data_ICCV_2019_paper.pdf | iccv-2019-10 | ['shadow-removal'] | ['computer-vision'] | [ 7.38876164e-01 3.90553087e-01 3.23086590e-01 -4.41039532e-01
-5.00814617e-01 -7.96359479e-01 4.28441346e-01 -1.03017831e+00
7.17319474e-02 1.00236082e+00 -1.81468517e-01 -4.98600036e-01
5.19405961e-01 -6.32094264e-01 -8.45461190e-01 -1.10794926e+00
1.28755271e-01 9.52868387e-02 4.01419878e-01 -1.65931746... | [10.846380233764648, -4.1050591468811035] |
f13506b8-8b80-4969-bf12-bf22638fee53 | bayesian-calibration-of-differentiable-agent | 2305.15340 | null | https://arxiv.org/abs/2305.15340v1 | https://arxiv.org/pdf/2305.15340v1.pdf | Bayesian calibration of differentiable agent-based models | Agent-based modelling (ABMing) is a powerful and intuitive approach to modelling complex systems; however, the intractability of ABMs' likelihood functions and the non-differentiability of the mathematical operations comprising these models present a challenge to their use in the real world. These difficulties have in ... | ['Joel Dyer', 'Michael Wooldridge', 'Anisoara Calinescu', 'Ayush Chopra', 'Arnau Quera-Bofarull'] | 2023-05-24 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 5.05852327e-02 2.38891914e-01 -5.68024144e-02 -3.21621448e-01
-5.77206254e-01 -3.12644303e-01 9.06739354e-01 -1.04601188e-02
-2.18700036e-01 1.01758182e+00 -1.81655139e-02 -7.24172473e-01
-6.05083764e-01 -6.23015761e-01 -5.44069946e-01 -5.52901804e-01
-3.97656769e-01 8.24776173e-01 -6.50038943e-02 3.42333652... | [6.50487756729126, 4.0286431312561035] |
01429410-db61-4dd5-8631-fad44ff8ab6c | assessing-domain-adaptation-techniques-for | 2109.00869 | null | https://arxiv.org/abs/2109.00869v3 | https://arxiv.org/pdf/2109.00869v3.pdf | Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images | Breast cancer is the most commonly diagnosed cancer worldwide, with over two million new cases each year. During diagnostic tumour grading, pathologists manually count the number of dividing cells (mitotic figures) in biopsy or tumour resection specimens. Since the process is subjective and time-consuming, data-driven ... | ['Nishant Ravikumar', 'Nicolas M. Orsi', 'Kieran Zucker', 'Jack Breen'] | 2021-09-01 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 6.04406416e-01 1.16901062e-01 -1.23482540e-01 6.19772412e-02
-1.05957627e+00 -7.96143830e-01 7.22227573e-01 2.36609101e-01
-8.46347630e-01 6.59982681e-01 -9.78278294e-02 -3.14318746e-01
2.82119751e-01 -7.17904150e-01 -2.43787348e-01 -1.11871743e+00
3.60542953e-01 7.41406441e-01 3.75503063e-01 2.54104566... | [15.105311393737793, -3.12191104888916] |
83a0cbb6-b828-4bb9-a1dd-d7b8fa04a63b | cluener2020-fine-grained-name-entity | 2001.04351 | null | https://arxiv.org/abs/2001.04351v4 | https://arxiv.org/pdf/2001.04351v4.pdf | CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark for Chinese | In this paper, we introduce the NER dataset from CLUE organization (CLUENER2020), a well-defined fine-grained dataset for named entity recognition in Chinese. CLUENER2020 contains 10 categories. Apart from common labels like person, organization, and location, it contains more diverse categories. It is more challenging... | ['Weitang Liu', 'Yu tong', 'Liang Xu', 'Xuanwei Zhang', 'Lu Li', 'Yixuan Liao', 'Yin Tian', 'Qianqian Dong', 'Caiquan Liu', 'Cong Yu'] | 2020-01-13 | null | null | null | null | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-6.20818555e-01 -1.44987121e-01 1.81788225e-02 -4.32526499e-01
-9.04405534e-01 -1.20794940e+00 8.62006128e-01 1.06256850e-01
-1.18211234e+00 1.19130278e+00 8.79662275e-01 -2.33724803e-01
4.58788723e-01 -6.20297849e-01 -1.77906528e-01 -1.53226510e-01
2.19027340e-01 5.83942056e-01 2.59411603e-01 -2.43882626... | [9.766706466674805, 9.62215518951416] |
8a546655-0fe2-4fa9-8477-a14817980a08 | deepfake-text-detection-in-the-wild | 2305.13242 | null | https://arxiv.org/abs/2305.13242v1 | https://arxiv.org/pdf/2305.13242v1.pdf | Deepfake Text Detection in the Wild | Recent advances in large language models have enabled them to reach a level of text generation comparable to that of humans. These models show powerful capabilities across a wide range of content, including news article writing, story generation, and scientific writing. Such capability further narrows the gap between h... | ['Yue Zhang', 'Shuming Shi', 'Linyi Yang', 'Longyue Wang', 'Wei Bi', 'Leyang Cui', 'Qintong Li', 'Yafu Li'] | 2023-05-22 | null | null | null | null | ['face-swapping', 'story-generation'] | ['computer-vision', 'natural-language-processing'] | [-1.28225878e-01 2.15888396e-01 -1.51767820e-01 1.07662432e-01
-1.08142626e+00 -8.20265949e-01 1.08610380e+00 1.20411508e-01
-2.24163398e-01 7.04421639e-01 2.94212878e-01 -3.94589186e-01
5.24839103e-01 -6.48025513e-01 -5.80434680e-01 -1.80434540e-01
2.53573507e-01 7.67091513e-01 3.58799249e-01 -3.24502468... | [8.295258522033691, 10.053071022033691] |
844f0c85-5da2-4738-906d-124eefc30a0f | dense-learning-based-semi-supervised-object | 2204.07300 | null | https://arxiv.org/abs/2204.07300v1 | https://arxiv.org/pdf/2204.07300v1.pdf | Dense Learning based Semi-Supervised Object Detection | Semi-supervised object detection (SSOD) aims to facilitate the training and deployment of object detectors with the help of a large amount of unlabeled data. Though various self-training based and consistency-regularization based SSOD methods have been proposed, most of them are anchor-based detectors, ignoring the fac... | ['Xian-Sheng Hua', 'Lei Zhang', 'Biao Wang', 'Xiang Chen', 'Pengyu Li', 'Binghui Chen'] | 2022-04-15 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_Dense_Learning_Based_Semi-Supervised_Object_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_Dense_Learning_Based_Semi-Supervised_Object_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 9.24326479e-03 -7.97879100e-02 -2.50390500e-01 -5.12370706e-01
-8.29084456e-01 -1.81227431e-01 4.29354966e-01 1.54334292e-01
-4.30429161e-01 4.90966618e-01 -1.97225749e-01 1.13984838e-01
-1.84988938e-02 -4.28184986e-01 -5.70725858e-01 -9.63912547e-01
2.23971397e-01 3.23788494e-01 9.54644561e-01 3.96374762... | [9.17805290222168, 1.3230438232421875] |
855e49d8-f03f-4f13-8b48-56081ff2a512 | mnist-mix-a-multi-language-handwritten-digit | 2004.03848 | null | https://arxiv.org/abs/2004.03848v1 | https://arxiv.org/pdf/2004.03848v1.pdf | MNIST-MIX: A Multi-language Handwritten Digit Recognition Dataset | In this letter, we contribute a multi-language handwritten digit recognition dataset named MNIST-MIX, which is the largest dataset of the same type in terms of both languages and data samples. With the same data format with MNIST, MNIST-MIX can be seamlessly applied in existing studies for handwritten digit recognition... | ['Weiwei Jiang'] | 2020-04-08 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-3.63007516e-01 -7.63879240e-01 -6.36402547e-01 -5.20348430e-01
-4.62931365e-01 -6.53878629e-01 5.51960886e-01 -3.00405681e-01
-6.55567467e-01 6.42591894e-01 8.36075693e-02 -4.12966907e-01
3.34365755e-01 -7.42982626e-01 -4.62248623e-01 -3.74905258e-01
4.20727849e-01 6.48070216e-01 -7.58142173e-02 -6.24078661... | [11.836922645568848, 2.591524124145508] |
4b33d55d-260d-44fc-86a4-047f721506ba | fanet-quality-aware-feature-aggregation | 1811.09855 | null | https://arxiv.org/abs/1811.09855v2 | https://arxiv.org/pdf/1811.09855v2.pdf | FANet: Quality-Aware Feature Aggregation Network for Robust RGB-T Tracking | This paper investigates how to perform robust visual tracking in adverse and challenging conditions using complementary visual and thermal infrared data (RGBT tracking). We propose a novel deep network architecture called qualityaware Feature Aggregation Network (FANet) for robust RGBT tracking. Unlike existing RGBT tr... | ['Jin Tang', 'Bin Luo', 'Yabin Zhu', 'Chenglong Li'] | 2018-11-24 | null | null | null | null | ['rgb-t-tracking'] | ['computer-vision'] | [ 3.96133661e-02 -6.19553983e-01 1.15611941e-01 -3.20836872e-01
-5.47343552e-01 -4.85653549e-01 3.45299035e-01 -3.01148325e-01
-5.86780727e-01 3.52066696e-01 1.00206308e-01 6.34719953e-02
3.99986235e-03 -5.04163504e-01 -7.35261500e-01 -7.89902270e-01
8.88822079e-02 -1.83914810e-01 2.52707064e-01 1.59608021... | [6.343447685241699, -2.2055482864379883] |
ddd88a59-311d-4977-8575-2539ecadd711 | active-learning-enhances-classification-of | 2303.01342 | null | https://arxiv.org/abs/2303.01342v1 | https://arxiv.org/pdf/2303.01342v1.pdf | Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-based Multiple Instance Learning | In many histopathology tasks, sample classification depends on morphological details in tissue or single cells that are only visible at the highest magnification. For a pathologist, this implies tedious zooming in and out, while for a computational decision support algorithm, it leads to the analysis of a huge number o... | ['Carsten Marr', 'Nassir Navab', 'Ario Sadafi'] | 2023-03-02 | null | null | null | null | ['whole-slide-images', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 6.45627379e-01 5.76416850e-01 -2.47992918e-01 -3.71522576e-01
-1.50281274e+00 -4.31051433e-01 2.60359168e-01 1.08233106e+00
-9.06584799e-01 6.95129395e-01 -2.06820220e-01 -3.28160644e-01
-2.37355605e-01 -5.97084403e-01 -6.16955638e-01 -1.27703309e+00
5.43393120e-02 8.72935295e-01 4.90949988e-01 1.56171963... | [15.056796073913574, -2.9079513549804688] |
588e6d7e-95a7-4008-b43b-22382d8eccc3 | automatic-image-cropping-for-visual-aesthetic | 1712.09048 | null | http://arxiv.org/abs/1712.09048v2 | http://arxiv.org/pdf/1712.09048v2.pdf | Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression | Despite recent progress, computational visual aesthetic is still challenging.
Image cropping, which refers to the removal of unwanted scene areas, is an
important step to improve the aesthetic quality of an image. However, it is
challenging to evaluate whether cropping leads to aesthetically pleasing
results because th... | ['Hong-Yuan Mark Liao', 'Chunhua Shen', 'Yan Yan', 'Hanzi Wang', 'Guanjun Guo'] | 2017-12-25 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 5.84854186e-01 -9.40938368e-02 1.25608563e-01 -2.90576905e-01
-6.07579529e-01 -2.16370597e-01 2.50604928e-01 -2.29523167e-01
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7.09692061e-01 -3.98308367e-01 -2.07889900e-01 -2.49806106... | [11.44752025604248, -0.9940369129180908] |
5bee2239-3000-4458-9f9c-d0682ba20c14 | protoformer-embedding-prototypes-for-1 | 2206.12710 | null | https://arxiv.org/abs/2206.12710v1 | https://arxiv.org/pdf/2206.12710v1.pdf | Protoformer: Embedding Prototypes for Transformers | Transformers have been widely applied in text classification. Unfortunately, real-world data contain anomalies and noisy labels that cause challenges for state-of-art Transformers. This paper proposes Protoformer, a novel self-learning framework for Transformers that can leverage problematic samples for text classifica... | ['Zhishan Guo', 'Arthur Huang', 'Haiyan Bai', 'Nan Hua', 'Ning Sui', 'Ashkan Farhangi'] | 2022-06-25 | protoformer-embedding-prototypes-for | https://link.springer.com/chapter/10.1007/978-3-031-05933-9_35 | https://link.springer.com/chapter/10.1007/978-3-031-05933-9_35 | pakdd-2022-advances-in-knowledge-discovery | ['classification', 'self-learning'] | ['methodology', 'natural-language-processing'] | [-3.65952909e-01 -2.32339710e-01 -3.12426031e-01 -4.12081093e-01
-7.53021955e-01 -6.51709020e-01 7.45347619e-01 5.57100236e-01
-1.73000485e-01 4.73703355e-01 1.57724917e-01 -4.25887078e-01
7.80132860e-02 -8.65581989e-01 -3.03340554e-01 -4.23989177e-01
-2.26023793e-01 3.74874026e-01 2.23566785e-01 -2.26873487... | [10.322749137878418, 7.012661457061768] |
8c34ee25-b1ad-41e0-bba6-8a0372af2a88 | u-2-net-going-deeper-with-nested-u-structure | 2005.09007 | null | https://arxiv.org/abs/2005.09007v3 | https://arxiv.org/pdf/2005.09007v3.pdf | U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection | In this paper, we design a simple yet powerful deep network architecture, U$^2$-Net, for salient object detection (SOD). The architecture of our U$^2$-Net is a two-level nested U-structure. The design has the following advantages: (1) it is able to capture more contextual information from different scales thanks to the... | ['Osmar R. Zaiane', 'Xuebin Qin', 'Martin Jagersand', 'Chenyang Huang', 'Masood Dehghan', 'Zichen Zhang'] | 2020-05-18 | null | null | null | null | ['dichotomous-image-segmentation'] | ['computer-vision'] | [-7.79753327e-02 1.20295271e-01 1.24786556e-01 -9.58290398e-02
-3.60439152e-01 -1.47592917e-01 1.06737472e-01 -6.96695521e-02
-4.92741913e-01 4.55867350e-01 -5.56847900e-02 -4.72221583e-01
3.66631508e-01 -1.17127252e+00 -9.90969658e-01 -5.05897462e-01
-4.17289138e-01 -4.44501609e-01 8.34429324e-01 -3.97636890... | [9.381202697753906, -0.4846106469631195] |
db9b23dd-903e-468e-ae78-5eb1f49c5214 | bi3d-stereo-depth-estimation-via-binary | 2005.07274 | null | https://arxiv.org/abs/2005.07274v2 | https://arxiv.org/pdf/2005.07274v2.pdf | Bi3D: Stereo Depth Estimation via Binary Classifications | Stereo-based depth estimation is a cornerstone of computer vision, with state-of-the-art methods delivering accurate results in real time. For several applications such as autonomous navigation, however, it may be useful to trade accuracy for lower latency. We present Bi3D, a method that estimates depth via a series of... | ['Orazio Gallo', 'Jan Kautz', 'Pradeep Sen', 'Kihwan Kim', 'Alejandro Troccoli', 'Abhishek Badki'] | 2020-05-14 | bi3d-stereo-depth-estimation-via-binary-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Badki_Bi3D_Stereo_Depth_Estimation_via_Binary_Classifications_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Badki_Bi3D_Stereo_Depth_Estimation_via_Binary_Classifications_CVPR_2020_paper.pdf | cvpr-2020-6 | ['stereo-depth-estimation'] | ['computer-vision'] | [ 1.53602704e-01 -2.85094738e-01 1.53531823e-02 -3.56386065e-01
-8.28678906e-01 -8.05733919e-01 3.03040057e-01 2.71253109e-01
-6.84446394e-01 5.07219195e-01 -1.93074241e-01 -4.38294888e-01
3.28744948e-01 -1.15072024e+00 -5.56738496e-01 -4.51123923e-01
-2.15619407e-03 5.67278862e-01 1.00889611e+00 -1.89811818... | [8.769789695739746, -2.47129225730896] |
7e07aac0-1008-4b12-a51e-31eac95737bc | supernmt-neural-machine-translation-with | null | null | https://aclanthology.org/P18-3010 | https://aclanthology.org/P18-3010.pdf | SuperNMT: Neural Machine Translation with Semantic Supersenses and Syntactic Supertags | In this paper we incorporate semantic supersensetags and syntactic supertag features into EN{--}FR and EN{--}DE factored NMT systems. In experiments on various test sets, we observe that such features (and particularly when combined) help the NMT model training to converge faster and improve the model quality according... | ['Eva Vanmassenhove', 'Andy Way'] | 2018-07-01 | null | null | null | acl-2018-7 | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [ 2.63485521e-01 2.45263323e-01 -4.22742635e-01 -6.18195295e-01
-8.54413688e-01 -8.21003914e-01 7.96227098e-01 -3.13738547e-03
-7.55926847e-01 7.35911191e-01 4.71363574e-01 -4.57227916e-01
-3.53686251e-02 -5.03289640e-01 -4.79717702e-01 -4.07075077e-01
2.11660430e-01 7.83824623e-01 2.70690173e-01 -6.08021855... | [10.772211074829102, 9.449883460998535] |
c2c7b4cb-e82e-4da8-8ade-faa78ba72c4c | effect-of-analysis-window-and-feature | 2002.00461 | null | https://arxiv.org/abs/2002.00461v4 | https://arxiv.org/pdf/2002.00461v4.pdf | Effect of Analysis Window and Feature Selection on Classification of Hand Movements Using EMG Signal | Electromyography (EMG) signals have been successfully employed for driving prosthetic limbs of a single or double degree of freedom. This principle works by using the amplitude of the EMG signals to decide between one or two simpler movements. This method underperforms as compare to the contemporary advances done at th... | ['Sarwan Ali', 'Safiullah Faizullah', 'Muhammad Asad Khan', 'Asad Ullah', 'Imdadullah Khan'] | 2020-02-02 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 4.90789473e-01 -7.81077296e-02 -6.32518113e-01 9.42242704e-03
-4.32536960e-01 -2.88201153e-01 1.34234995e-01 -8.11413646e-01
-7.52323568e-01 8.11145484e-01 -1.89818460e-02 -4.63811345e-02
-5.38134992e-01 -2.82701194e-01 -2.66852111e-01 -8.73023033e-01
-3.79235774e-01 1.70033611e-02 6.84136897e-02 -1.63891196... | [6.863928318023682, 0.18986418843269348] |
d968bc2d-7efe-4d33-8272-2b2181d3d7d8 | automated-program-repair-based-on-code-review | 2304.07840 | null | https://arxiv.org/abs/2304.07840v1 | https://arxiv.org/pdf/2304.07840v1.pdf | Automated Program Repair Based on Code Review: How do Pre-trained Transformer Models Perform? | Sequence-to-sequence models have been used to transform erroneous programs into correct ones when trained with a large enough dataset. Some recent studies also demonstrated strong empirical evidence that code review (natural language instruction about suggestive changes in code) can improve the program repair further. ... | ['Anindya Iqbal', 'Masum Hasan', 'Md. Mohib Hossain', 'Rishov Paul'] | 2023-04-16 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 2.13470533e-01 2.16224700e-01 -5.48205674e-01 -1.53525874e-01
-8.69118929e-01 -6.21352553e-01 4.60676968e-01 4.68986034e-01
-2.28805810e-01 1.68998331e-01 2.57018626e-01 -1.05861855e+00
3.03762496e-01 -7.41956949e-01 -1.27026784e+00 1.62217602e-01
-5.39136305e-03 -1.76737979e-01 4.24118757e-01 -4.10729051... | [7.685062408447266, 7.790798187255859] |
795fd1f0-abe0-4ef0-9eb5-d2a63d8efa3c | adversarial-skill-chaining-for-long-horizon | 2111.07999 | null | https://arxiv.org/abs/2111.07999v1 | https://arxiv.org/pdf/2111.07999v1.pdf | Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization | Skill chaining is a promising approach for synthesizing complex behaviors by sequentially combining previously learned skills. Yet, a naive composition of skills fails when a policy encounters a starting state never seen during its training. For successful skill chaining, prior approaches attempt to widen the policy's ... | ['Yuke Zhu', 'Anima Anandkumar', 'Joseph J. Lim', 'Youngwoon Lee'] | 2021-11-15 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 4.65577304e-01 1.96250960e-01 -3.13296467e-01 2.11464494e-01
-6.52800441e-01 -1.01123571e+00 5.83601296e-01 -1.76804572e-01
-3.19183022e-01 1.27112913e+00 -1.93514287e-01 -7.15096116e-01
-1.81957304e-01 -5.80436289e-01 -9.88849223e-01 -6.03545010e-01
-9.30034276e-03 4.79570270e-01 3.29719782e-01 -4.10992056... | [4.172247409820557, 1.6347771883010864] |
9e711df0-c9f7-46f8-9b01-219d30caa930 | fair-a-causal-framework-for-accurately | 2306.11585 | null | https://arxiv.org/abs/2306.11585v1 | https://arxiv.org/pdf/2306.11585v1.pdf | FAIR: A Causal Framework for Accurately Inferring Judgments Reversals | Artificial intelligence researchers have made significant advances in legal intelligence in recent years. However, the existing studies have not focused on the important value embedded in judgments reversals, which limits the improvement of the efficiency of legal intelligence. In this paper, we propose a causal Framew... | ['Yaying Chen', 'Qionghui Zhang', 'Yuntao Shi', 'Nanfei Gu', 'Minghua He'] | 2023-06-20 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 9.77687538e-02 5.28118908e-02 -5.64521909e-01 -6.85907066e-01
-3.19373757e-01 -1.67383015e-01 4.97627795e-01 -2.01867700e-01
-2.60926813e-01 8.48142743e-01 6.82629287e-01 -8.60763311e-01
-7.05116093e-01 -1.02654231e+00 -5.24547994e-01 -2.75541663e-01
1.97094381e-01 3.03176343e-01 2.79626906e-01 -4.24701989... | [9.75507926940918, 7.853357791900635] |
d64a875a-cb15-4195-bfe0-84be7f9e00e1 | semi-supervised-music-emotion-recognition | 2112.00702 | null | https://arxiv.org/abs/2112.00702v2 | https://arxiv.org/pdf/2112.00702v2.pdf | Semi-supervised music emotion recognition using noisy student training and harmonic pitch class profiles | We present Mirable's submission to the 2021 Emotions and Themes in Music challenge. In this work, we intend to address the question: can we leverage semi-supervised learning techniques on music emotion recognition? With that, we experiment with noisy student training, which has improved model performance in the image c... | ['Hao Hao Tan'] | 2021-12-01 | null | null | null | null | ['music-emotion-recognition'] | ['music'] | [ 2.03753561e-01 -8.93855169e-02 -1.83078706e-01 -2.12911680e-01
-9.25779045e-01 -7.19660521e-01 2.05809399e-01 8.15258175e-02
-5.77728033e-01 2.29550838e-01 5.71006954e-01 8.36681053e-02
-9.85884517e-02 -3.49476159e-01 -3.88421863e-01 -4.96539265e-01
-2.14253366e-01 1.12696532e-02 5.25557389e-03 -1.84337422... | [15.812292098999023, 5.214594841003418] |
c5ebbee5-467b-4625-8992-23fb14efa505 | hopper-multi-hop-transformer-for-1 | 2103.10574 | null | https://arxiv.org/abs/2103.10574v2 | https://arxiv.org/pdf/2103.10574v2.pdf | Hopper: Multi-hop Transformer for Spatiotemporal Reasoning | This paper considers the problem of spatiotemporal object-centric reasoning in videos. Central to our approach is the notion of object permanence, i.e., the ability to reason about the location of objects as they move through the video while being occluded, contained or carried by other objects. Existing deep learning ... | ['Hans Peter Graf', 'Mubbasir Kapadia', 'Martin Renqiang Min', 'Alexandru Niculescu-Mizil', 'Farley Lai', 'Asim Kadav', 'Honglu Zhou'] | 2021-03-19 | hopper-multi-hop-transformer-for | https://openreview.net/forum?id=MaZFq7bJif7 | https://openreview.net/pdf?id=MaZFq7bJif7 | iclr-2021-1 | ['video-object-tracking'] | ['computer-vision'] | [-2.34394655e-01 -1.34759068e-01 -2.58859336e-01 -2.46120423e-01
-8.10870707e-01 -6.07499897e-01 2.76822418e-01 2.12999806e-01
-4.85815853e-01 3.60406131e-01 1.05355494e-01 -4.12275493e-02
-1.86269835e-01 -4.87880498e-01 -1.45507514e+00 -3.85953575e-01
-4.22449321e-01 3.83047730e-01 8.86282504e-01 1.00248352... | [8.633698463439941, 0.4250791668891907] |
c7e4a936-c711-4627-bcc1-a196cc168743 | integrating-diverse-extraction-pathways-using | 2110.08144 | null | https://arxiv.org/abs/2110.08144v2 | https://arxiv.org/pdf/2110.08144v2.pdf | milIE: Modular & Iterative Multilingual Open Information Extraction | Open Information Extraction (OpenIE) is the task of extracting (subject, predicate, object) triples from natural language sentences. Current OpenIE systems extract all triple slots independently. In contrast, we explore the hypothesis that it may be beneficial to extract triple slots iteratively: first extract easy slo... | ['Ammar Shaker', 'Daniel Oñoro Rubio', 'Mathias Niepert', 'Makoto Takamoto', 'Vanesa Rodriguez-Tembras', 'Carolin Lawrence', 'Kiril Gashteovski', 'Bhushan Kotnis'] | 2021-10-15 | null | https://aclanthology.org/2022.acl-long.478 | https://aclanthology.org/2022.acl-long.478.pdf | acl-2022-5 | ['open-information-extraction'] | ['natural-language-processing'] | [ 1.02847733e-01 9.79542553e-01 -4.18016165e-01 -1.90827459e-01
-7.46385396e-01 -7.01736987e-01 5.21068931e-01 1.22855440e-01
-3.30128133e-01 1.02185285e+00 2.73966968e-01 -6.65989578e-01
-1.10587321e-01 -9.99559343e-01 -6.54990077e-01 1.94042921e-01
-1.53762400e-01 7.92562127e-01 2.06714824e-01 -4.32667613... | [9.529929161071777, 8.686266899108887] |
7c25156b-8304-4c23-a2d4-9f57849c9a41 | visual-question-answering-based-on-formal | 2111.04785 | null | https://arxiv.org/abs/2111.04785v1 | https://arxiv.org/pdf/2111.04785v1.pdf | Visual Question Answering based on Formal Logic | Visual question answering (VQA) has been gaining a lot of traction in the machine learning community in the recent years due to the challenges posed in understanding information coming from multiple modalities (i.e., images, language). In VQA, a series of questions are posed based on a set of images and the task at han... | ['J. Clayton Kerce', 'Faramarz Fekri', 'Ali Payani', 'Muralikrishnna G. Sethuraman'] | 2021-11-08 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 4.18633103e-01 5.56388855e-01 1.55195311e-01 -3.31314951e-01
-7.92224467e-01 -8.91325891e-01 7.58780658e-01 2.50713468e-01
-9.31179076e-02 5.29170454e-01 -6.12930283e-02 -6.87395990e-01
-2.45496035e-02 -1.09043407e+00 -1.07274389e+00 -1.06067523e-01
3.72940540e-01 6.86249614e-01 6.07160270e-01 -2.24397913... | [10.784552574157715, 1.87483549118042] |
4630d92c-de01-4fe3-a66a-0417e124583c | automatic-classification-of-geologic-units-in | 1901.03786 | null | http://arxiv.org/abs/1901.03786v1 | http://arxiv.org/pdf/1901.03786v1.pdf | Automatic classification of geologic units in seismic images using partially interpreted examples | Geologic interpretation of large seismic stacked or migrated seismic images
can be a time-consuming task for seismic interpreters. Neural network based
semantic segmentation provides fast and automatic interpretations, provided a
sufficient number of example interpretations are available. Networks that map
from image-t... | ['Bas Peters', 'Justin Granek', 'Eldad Haber'] | 2019-01-12 | null | null | null | null | ['seismic-interpretation'] | ['miscellaneous'] | [ 6.03377819e-01 6.56037390e-01 2.39104107e-01 -8.97099197e-01
-1.35594428e+00 -4.78887677e-01 3.81747484e-01 1.86364055e-01
-9.23681080e-01 7.28875339e-01 2.28973016e-01 -4.50971633e-01
9.33901966e-02 -9.42983925e-01 -8.63800287e-01 -6.59985483e-01
-6.33611441e-01 1.08698142e+00 7.58003712e-01 -1.04740322... | [7.221382141113281, 2.1298177242279053] |
d820a3ef-7eda-463f-9aa2-d424b6ca5f9d | overexposure-mask-fusion-generalizable | 2210.11511 | null | https://arxiv.org/abs/2210.11511v1 | https://arxiv.org/pdf/2210.11511v1.pdf | Overexposure Mask Fusion: Generalizable Reverse ISP Multi-Step Refinement | With the advent of deep learning methods replacing the ISP in transforming sensor RAW readings into RGB images, numerous methodologies solidified into real-life applications. Equally potent is the task of inverting this process which will have applications in enhancing computational photography tasks that are conducted... | ['Jinwei Gu', 'Jun Jiang', 'Jinha Kim'] | 2022-10-20 | null | null | null | null | ['raw-reconstruction'] | ['computer-vision'] | [ 6.59623981e-01 2.80613810e-01 5.06001472e-01 -6.18049800e-01
-8.83470297e-01 -1.33938417e-01 6.58352375e-01 -4.10934776e-01
-9.13927794e-01 6.68534577e-01 2.84866005e-01 -2.72152513e-01
8.24022144e-02 -8.29505801e-01 -9.42371845e-01 -6.74260616e-01
1.83710873e-01 1.66724876e-01 5.28417230e-01 -3.80654335... | [10.259044647216797, -2.4464666843414307] |
65c45cea-35ad-4c48-ac00-c7fd072fed9d | higher-order-correlation-analysis-for-multi | 2201.11949 | null | https://arxiv.org/abs/2201.11949v1 | https://arxiv.org/pdf/2201.11949v1.pdf | Higher Order Correlation Analysis for Multi-View Learning | Multi-view learning is frequently used in data science. The pairwise correlation maximization is a classical approach for exploring the consensus of multiple views. Since the pairwise correlation is inherent for two views, the extensions to more views can be diversified and the intrinsic interconnections among views ar... | ['Zequn Zheng', 'Li Wang', 'Jiawang Nie'] | 2022-01-28 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-4.14141268e-01 -1.46899730e-01 -2.09389642e-01 -2.52210736e-01
-8.56672645e-01 -6.87468708e-01 3.65506202e-01 -3.06012899e-01
1.12147450e-01 5.39171278e-01 3.48198414e-01 3.19108367e-01
-5.38889110e-01 -3.48102272e-01 -3.84243697e-01 -8.28880847e-01
-2.96168298e-01 3.10946107e-01 -6.29057549e-03 -7.59483501... | [8.250303268432617, 4.607059001922607] |
62c73593-adaf-43b2-a98c-e83f2de170bb | answer-type-prediction-for-visual-question | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Kafle_Answer-Type_Prediction_for_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Kafle_Answer-Type_Prediction_for_CVPR_2016_paper.pdf | Answer-Type Prediction for Visual Question Answering | Recently, algorithms for object recognition and related tasks have become sufficiently proficient that new vision tasks can now be pursued. In this paper, we build a system capable of answering open-ended text-based questions about images, which is known as Visual Question Answering (VQA). Our approach's key insight is... | ['Kushal Kafle', 'Christopher Kanan'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['type-prediction'] | ['computer-code'] | [-1.34502456e-01 -6.44259006e-02 6.22013770e-02 -5.93463123e-01
-1.07666337e+00 -7.23290741e-01 7.54246235e-01 -1.37653649e-01
-2.77277082e-01 3.76456201e-01 7.28661790e-02 -4.75136846e-01
-4.78662085e-03 -5.62055469e-01 -6.98765755e-01 -4.58495259e-01
5.09861529e-01 6.40520334e-01 6.25613034e-01 -2.49086022... | [10.902334213256836, 1.721983551979065] |
ebf6a5b6-c37a-4352-8692-0c8b318fe9d0 | matteformer-transformer-based-image-matting | 2203.15662 | null | https://arxiv.org/abs/2203.15662v1 | https://arxiv.org/pdf/2203.15662v1.pdf | MatteFormer: Transformer-Based Image Matting via Prior-Tokens | In this paper, we propose a transformer-based image matting model called MatteFormer, which takes full advantage of trimap information in the transformer block. Our method first introduces a prior-token which is a global representation of each trimap region (e.g. foreground, background and unknown). These prior-tokens ... | ['Nojun Kwak', 'SeHo Kim', 'Jaeyoung Yoo', 'Sungjoon Son', 'Gyutae Park'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Park_MatteFormer_Transformer-Based_Image_Matting_via_Prior-Tokens_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Park_MatteFormer_Transformer-Based_Image_Matting_via_Prior-Tokens_CVPR_2022_paper.pdf | cvpr-2022-1 | ['image-matting'] | ['computer-vision'] | [ 2.21886560e-01 4.07036357e-02 -1.36399716e-01 -2.91611135e-01
-6.32343709e-01 5.91577180e-02 4.93586123e-01 -2.95078903e-01
-2.83739299e-01 4.13477212e-01 3.31537873e-01 -2.28169903e-01
4.94102240e-01 -9.07284498e-01 -1.14186537e+00 -8.96690071e-01
1.25622436e-01 2.14459166e-01 6.72417521e-01 3.37452218... | [10.609184265136719, -0.9069545269012451] |
b2cb5936-2237-4eb4-8d97-e4a64b21752a | theoretical-evaluation-of-feature-selection | 1609.06575 | null | http://arxiv.org/abs/1609.06575v2 | http://arxiv.org/pdf/1609.06575v2.pdf | Theoretical Evaluation of Feature Selection Methods based on Mutual Information | Feature selection methods are usually evaluated by wrapping specific
classifiers and datasets in the evaluation process, resulting very often in
unfair comparisons between methods. In this work, we develop a theoretical
framework that allows obtaining the true feature ordering of two-dimensional
sequential forward feat... | ['António Pacheco', 'M. Rosário Oliveira', 'Cláudia Pascoal', 'Rui Valadas'] | 2016-09-21 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 3.60738844e-01 -1.10164657e-01 -1.22759357e-01 -5.64169586e-01
-2.47652575e-01 -4.79874492e-01 7.04613507e-01 2.58042067e-01
-4.87449378e-01 1.04448128e+00 -9.21792760e-02 -3.58300120e-01
-8.59230995e-01 -7.91859686e-01 -2.68088281e-02 -8.16047013e-01
-1.88294083e-01 3.25762212e-01 5.12967184e-02 1.40778720... | [7.928206443786621, 4.511325836181641] |
c48584ba-00f3-4848-ab90-3cb8623b1289 | uncovering-the-evolution-of-non-stationary | 1510.07280 | null | http://arxiv.org/abs/1510.07280v1 | http://arxiv.org/pdf/1510.07280v1.pdf | Uncovering the evolution of non-stationary stochastic variables: the example of asset volume-price fluctuations | We present a framework for describing the evolution of stochastic observables
having a non-stationary distribution of values. The framework is applied to
empirical volume-prices from assets traded at the New York stock exchange.
Using Kullback-Leibler divergence we evaluate the best model out from four
biparametric mod... | [] | 2015-10-25 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [-3.47242057e-01 -3.69065434e-01 4.03982997e-01 -2.51689583e-01
-1.94094211e-01 -7.09854543e-01 7.80954182e-01 2.80163556e-01
-9.05022264e-01 1.04381990e+00 -1.71807185e-01 -2.79365718e-01
-4.84876007e-01 -8.99047494e-01 -5.69246948e-01 -1.19119418e+00
-5.14576554e-01 6.07222199e-01 2.56627351e-01 -3.89804691... | [4.98030424118042, 4.044376850128174] |
4559d732-5730-4f14-87bd-8d1fb1fead5d | hard-attention-control-by-mutual-information-1 | 2103.06371 | null | https://arxiv.org/abs/2103.06371v1 | https://arxiv.org/pdf/2103.06371v1.pdf | Hard Attention Control By Mutual Information Maximization | Biological agents have adopted the principle of attention to limit the rate of incoming information from the environment. One question that arises is if an artificial agent has access to only a limited view of its surroundings, how can it control its attention to effectively solve tasks? We propose an approach for lear... | ['Charles Isbell', 'Himanshu Sahni'] | 2021-03-10 | hard-attention-control-by-mutual-information | https://openreview.net/forum?id=TV9INIrmtWN | https://openreview.net/pdf?id=TV9INIrmtWN | null | ['hard-attention'] | ['methodology'] | [ 3.02700460e-01 5.26199222e-01 2.49470230e-02 -1.82229467e-02
-1.52987361e-01 -2.80053169e-01 6.02352798e-01 1.52652308e-01
-7.14258552e-01 8.05590093e-01 7.47695565e-02 2.18097836e-01
2.21503690e-01 -7.13038087e-01 -7.89377332e-01 -8.95982862e-01
-1.40777200e-01 6.44463837e-01 3.61277401e-01 -1.48505107... | [4.242661476135254, 1.273516297340393] |
7d5e85ec-6ea2-4fb6-ae37-901ceb547045 | meta-heuristic-based-deep-learning-model-for | null | null | https://link.springer.com/article/10.1007/s11063-022-10880-z#Abs1 | https://link.springer.com/content/pdf/10.1007/s11063-022-10880-z.pdf?pdf=button | Meta-Heuristic Based Deep Learning Model for Leaf Diseases Detection. | The automatic detection of leaf disease is necessary to improve the quality and quantity of agricultural production. This paper proposes a framework based on optimal deep neural network (ODNN) to detect the plant leaf disease using the leaf images of healthy and diseased plants. The proposed work uses Convolutional Neu... | ['P. Ramkumar', 'A. Meenakshi', 'R. Uma', 'J. Anitha Ruth'] | 2022-05-05 | null | null | null | neural-processing-letters-volume-54-2022-5 | ['specificity'] | ['natural-language-processing'] | [ 3.38906236e-02 -2.87510097e-01 -1.23724164e-02 -5.01880646e-02
5.24699092e-01 -4.52486217e-01 1.35703951e-01 3.22181404e-01
-2.58964717e-01 4.41191614e-01 -3.38664889e-01 -3.82683426e-01
-4.89704162e-01 -9.31854546e-01 -5.16823195e-02 -8.61469924e-01
-1.00422472e-01 1.31205320e-01 1.51130125e-01 -2.42312193... | [9.216402053833008, -1.5516475439071655] |
7a913563-9629-4626-a6ae-89996f7db363 | transfer-reinforcement-learning-for-differing | 2202.02442 | null | https://arxiv.org/abs/2202.02442v3 | https://arxiv.org/pdf/2202.02442v3.pdf | Transfer Reinforcement Learning for Differing Action Spaces via Q-Network Representations | Transfer learning approaches in reinforcement learning aim to assist agents in learning their target domains by leveraging the knowledge learned from other agents that have been trained on similar source domains. For example, recent research focus within this space has been placed on knowledge transfer between tasks th... | ['Hieu Tran', 'Abhiramon Rajasekharan', 'Nathan Beck'] | 2022-02-05 | null | null | null | null | ['transfer-reinforcement-learning', 'acrobot'] | ['methodology', 'playing-games'] | [ 5.38048567e-03 2.71752980e-02 -3.31814557e-01 -1.72520310e-01
-6.45403683e-01 -6.51438534e-01 9.33138609e-01 5.02516627e-02
-7.82800913e-01 1.17511165e+00 1.68938860e-01 -1.38110638e-01
-3.62346411e-01 -8.09446812e-01 -6.43709958e-01 -4.89841819e-01
-3.69656622e-01 4.57338482e-01 5.04934013e-01 -6.04232550... | [4.1832780838012695, 1.3997753858566284] |
c2d638cd-c12c-471d-9afe-601b8ba72545 | hmc-at-semeval-2016-task-11-identifying | null | null | https://aclanthology.org/S16-1161 | https://aclanthology.org/S16-1161.pdf | HMC at SemEval-2016 Task 11: Identifying Complex Words Using Depth-limited Decision Trees | null | ['Maury Quijada', 'Julie Medero'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['complex-word-identification'] | ['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 -6.93831444... | [-7.322602272033691, 3.7859106063842773] |
5533e0f8-907b-4573-aeae-140a45e2acea | attention-convolutional-binary-neural-tree | 1909.11378 | null | https://arxiv.org/abs/1909.11378v2 | https://arxiv.org/pdf/1909.11378v2.pdf | Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization | Fine-grained visual categorization (FGVC) is an important but challenging task due to high intra-class variances and low inter-class variances caused by deformation, occlusion, illumination, etc. An attention convolutional binary neural tree architecture is presented to address those problems for weakly supervised FGVC... | ['Yanjun Wu', 'Xianglong Liu', 'Longyin Wen', 'Ruyi Ji', 'Libo Zhang', 'Dawei Du', 'Chen Zhao', 'Feiyue Huang'] | 2019-09-25 | attention-convolutional-binary-neural-tree-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Ji_Attention_Convolutional_Binary_Neural_Tree_for_Fine-Grained_Visual_Categorization_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Ji_Attention_Convolutional_Binary_Neural_Tree_for_Fine-Grained_Visual_Categorization_CVPR_2020_paper.pdf | cvpr-2020-6 | ['fine-grained-visual-categorization'] | ['computer-vision'] | [-1.10057574e-02 -8.91709477e-02 -3.84908319e-02 -8.14566910e-01
-5.10061502e-01 -4.70994651e-01 4.49916393e-01 -1.37977600e-01
-3.73343192e-02 2.97444999e-01 1.30415335e-01 -6.97046518e-02
-7.05573261e-02 -6.68540657e-01 -5.84245145e-01 -6.38150275e-01
-1.04886308e-01 2.93222189e-01 3.45200211e-01 2.16434136... | [9.581439971923828, 2.0369575023651123] |
a9a91199-e254-439e-893a-1bc96b746cde | leveraging-users-social-network-embeddings | 2211.10672 | null | https://arxiv.org/abs/2211.10672v1 | https://arxiv.org/pdf/2211.10672v1.pdf | Leveraging Users' Social Network Embeddings for Fake News Detection on Twitter | Social networks (SNs) are increasingly important sources of news for many people. The online connections made by users allows information to spread more easily than traditional news media (e.g., newspaper, television). However, they also make the spread of fake news easier than in traditional media, especially through ... | ['Iadh Ounis', 'Craig Macdonald', 'Ting Su'] | 2022-11-19 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [-4.56407040e-01 4.38449472e-01 -8.97493243e-01 2.34670475e-01
-7.25999922e-02 -7.11158574e-01 1.21687579e+00 6.79410040e-01
-2.17581183e-01 4.16417152e-01 4.76407140e-01 -5.46509206e-01
2.00841188e-01 -1.32158649e+00 -3.39709699e-01 -1.79735813e-02
-2.43297257e-02 3.96405041e-01 4.04251099e-01 -7.37993181... | [8.163690567016602, 10.252068519592285] |
6b82a71c-dc27-4487-990c-64617cf4b0e5 | multi-target-domain-adaptation-with | 2106.03418 | null | https://arxiv.org/abs/2106.03418v1 | https://arxiv.org/pdf/2106.03418v1.pdf | Multi-Target Domain Adaptation with Collaborative Consistency Learning | Recently unsupervised domain adaptation for the semantic segmentation task has become more and more popular due to high-cost of pixel-level annotation on real-world images. However, most domain adaptation methods are only restricted to single-source-single-target pair, and can not be directly extended to multiple targe... | ['Shengjin Wang', 'Huchuan Lu', 'Jianzhuang Liu', 'Yongjie Shi', 'Jianzhong He', 'Shuaijun Chen', 'Xu Jia', 'Takashi Isobe'] | 2021-06-07 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Isobe_Multi-Target_Domain_Adaptation_With_Collaborative_Consistency_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Isobe_Multi-Target_Domain_Adaptation_With_Collaborative_Consistency_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 6.00056469e-01 7.81623498e-02 -3.73478383e-01 -6.09746695e-01
-8.51746321e-01 -6.64721608e-01 2.09394202e-01 5.60998172e-02
-4.44627583e-01 7.85643756e-01 -2.66355366e-01 2.06860870e-01
1.33647293e-01 -6.49372935e-01 -6.58129096e-01 -7.65230477e-01
5.48676491e-01 7.99148798e-01 9.38275039e-01 1.56360175... | [9.67799186706543, 1.4161624908447266] |
65ac7889-22a4-462c-b759-13eb946fe0c0 | enhancing-speech-to-speech-translation-with | 2304.04618 | null | https://arxiv.org/abs/2304.04618v1 | https://arxiv.org/pdf/2304.04618v1.pdf | Enhancing Speech-to-Speech Translation with Multiple TTS Targets | It has been known that direct speech-to-speech translation (S2ST) models usually suffer from the data scarcity issue because of the limited existing parallel materials for both source and target speech. Therefore to train a direct S2ST system, previous works usually utilize text-to-speech (TTS) systems to generate samp... | ['Shinji Watanabe', 'Juan Pino', 'Changhan Wang', 'Hirofumi Inaguma', 'Ann Lee', 'Yun Tang', 'Jiatong Shi'] | 2023-04-10 | null | null | null | null | ['speech-to-text-translation', 'speech-to-speech-translation'] | ['natural-language-processing', 'speech'] | [ 4.54530627e-01 5.26318373e-03 -1.46765247e-01 -5.81539214e-01
-1.60276175e+00 -6.21061206e-01 8.35453153e-01 -7.09183633e-01
-2.28492960e-01 8.03794146e-01 4.37052369e-01 -7.98068702e-01
7.02275634e-01 -1.24940403e-01 -8.82433355e-01 -7.60595381e-01
7.40541458e-01 5.56723654e-01 7.74008557e-02 -4.39591765... | [14.509264945983887, 7.143739700317383] |
62e1e081-59cb-4efa-8a19-95610ce2ab28 | sdr-gain-a-high-real-time-occluded-pedestrian | 2306.03538 | null | https://arxiv.org/abs/2306.03538v3 | https://arxiv.org/pdf/2306.03538v3.pdf | A Work Based on GAN | This work will enter the submission stage, so specific information will be temporarily hidden, also hide the title. | ['Honghao Fu'] | 2023-06-06 | null | null | null | null | ['pedestrian-detection', 'imputation', 'dimensionality-reduction', 'imputation', 'imputation'] | ['computer-vision', 'computer-vision', 'methodology', 'miscellaneous', 'time-series'] | [-8.66713747e-02 2.30293274e-01 -6.09110594e-01 -2.36244366e-01
2.23623976e-01 -8.77684712e-01 2.78536499e-01 -1.47029702e-02
-1.90746263e-01 1.39325416e+00 -1.59079313e-01 -6.63209438e-01
-4.31020558e-02 -4.17905718e-01 -1.83056578e-01 -4.99030113e-01
-3.14066976e-01 1.59463674e-01 3.71912748e-01 -1.43274039... | [9.450182914733887, 8.391992568969727] |
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