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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
c642471f-9118-4fcb-995f-ac6de101941f | the-mvtec-3d-ad-dataset-for-unsupervised-3d | 2112.09045 | null | https://arxiv.org/abs/2112.09045v1 | https://arxiv.org/pdf/2112.09045v1.pdf | The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization | We introduce the first comprehensive 3D dataset for the task of unsupervised anomaly detection and localization. It is inspired by real-world visual inspection scenarios in which a model has to detect various types of defects on manufactured products, even if it is trained only on anomaly-free data. There are defects t... | ['Carsten Steger', 'David Sattlegger', 'Xin Jin', 'Paul Bergmann'] | 2021-12-16 | null | null | null | null | ['rgb-3d-anomaly-detection-and-segmentation', '3d-anomaly-detection-and-segmentation', 'depth-anomaly-detection-and-segmentation', 'rgb-depth-anomaly-detection-and-segmentation'] | ['methodology', 'methodology', 'methodology', 'methodology'] | [ 5.25125802e-01 3.03019613e-01 6.49573863e-01 -3.74965280e-01
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-3.34939867e-01 6.07932329e-01 8.71764719e-01 -1.72669426... | [7.524821758270264, 2.030536651611328] |
d15d0c4a-16df-4a7c-a83c-e578d1520d44 | unified-optimal-transport-framework-for | 2210.17067 | null | https://arxiv.org/abs/2210.17067v2 | https://arxiv.org/pdf/2210.17067v2.pdf | Unified Optimal Transport Framework for Universal Domain Adaptation | Universal Domain Adaptation (UniDA) aims to transfer knowledge from a source domain to a target domain without any constraints on label sets. Since both domains may hold private classes, identifying target common samples for domain alignment is an essential issue in UniDA. Most existing methods require manually specifi... | ['Jingya Wang', 'Hoang Duong Tuan', 'Ye Shi', 'Wanxing Chang'] | 2022-10-31 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [ 2.93078214e-01 -4.59151566e-01 -3.81179929e-01 -3.05081993e-01
-9.16985571e-01 -6.54102027e-01 5.14578938e-01 3.29827927e-02
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3.11289042e-01 9.68972266e-01 4.54537690e-01 1.17955450... | [10.335369110107422, 3.121565341949463] |
0d1f7962-eab8-4433-ba06-0f7e2401052a | panoptic-3d-scene-reconstruction-from-a | 2111.02444 | null | https://arxiv.org/abs/2111.02444v2 | https://arxiv.org/pdf/2111.02444v2.pdf | Panoptic 3D Scene Reconstruction From a Single RGB Image | Understanding 3D scenes from a single image is fundamental to a wide variety of tasks, such as for robotics, motion planning, or augmented reality. Existing works in 3D perception from a single RGB image tend to focus on geometric reconstruction only, or geometric reconstruction with semantic segmentation or instance s... | ['Angela Dai', 'Matthias Nießner', 'Ji Hou', 'Manuel Dahnert'] | 2021-11-03 | null | http://proceedings.neurips.cc/paper/2021/hash/46031b3d04dc90994ca317a7c55c4289-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/46031b3d04dc90994ca317a7c55c4289-Paper.pdf | neurips-2021-12 | ['3d-instance-segmentation-1', '3d-scene-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 6.92094207e-01 3.58325183e-01 -2.44403491e-03 -4.75127220e-01
-5.40489793e-01 -6.64941072e-01 6.94701791e-01 1.80102691e-01
-9.92187858e-02 7.09370673e-02 -6.05198033e-02 -3.98774862e-01
4.42670770e-02 -8.65174711e-01 -9.39671874e-01 -3.87065709e-01
4.17261958e-01 7.38675237e-01 2.13498071e-01 -1.15496339... | [8.461353302001953, -2.906177520751953] |
270b6475-f14d-4210-937c-8d998ea46558 | sequential-deformation-for-accurate-scene | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6576_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123740103.pdf | Sequential Deformation for Accurate Scene Text Detection | Scene text detection has been significantly advanced over recent years, especially after the emergence of deep neural network. However, due to high diversity of scene texts in scale, orientation, shape and aspect ratio, as well as the inherent limitation of convolutional neural network for geometric transformations, to... | ['Liangrui Peng', 'Keyu An', 'Jaesik Min', 'Shanyu Xiao', 'Gang Yao', 'Ruijie Yan'] | null | null | null | null | eccv-2020-8 | ['scene-text-detection'] | ['computer-vision'] | [ 2.78340638e-01 -6.93960309e-01 1.72723114e-01 -2.96850830e-01
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5.42005658e-01 5.59874713e-01 6.16750062e-01 -1.24349862... | [12.052302360534668, 2.261716842651367] |
f57eb06a-3a3f-46de-a0a3-91c4e68c7127 | sentiment-analysis-in-finance-from | 2306.03997 | null | https://arxiv.org/abs/2306.03997v1 | https://arxiv.org/pdf/2306.03997v1.pdf | Sentiment Analysis in Finance: From Transformers Back to eXplainable Lexicons (XLex) | Lexicon-based sentiment analysis (SA) in finance leverages specialized, manually annotated lexicons created by human experts to extract sentiment from financial texts. Although lexicon-based methods are simple to implement and fast to operate on textual data, they require considerable manual annotation efforts to creat... | ['Dimitar Trajanov', 'Milos Jovanovik', 'Kostadin Mishev', 'Hristijan Peshov', 'Maryan Rizinski'] | 2023-06-06 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-1.93889409e-01 2.39349321e-01 -1.13262720e-01 -4.71823186e-01
-6.50422931e-01 -9.19835508e-01 5.18975675e-01 4.81140077e-01
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-4.38261367e-02 -8.74692082e-01 -6.27495706e-01 -1.86758369e-01
2.32963040e-01 8.29800904e-01 -2.50183851e-01 -5.28979659... | [11.1276273727417, 6.9837541580200195] |
46d42675-55b0-4ec0-82e5-9be159638a4c | frustratingly-easy-cross-lingual-transfer-for | null | null | https://aclanthology.org/N16-1121 | https://aclanthology.org/N16-1121.pdf | Frustratingly Easy Cross-Lingual Transfer for Transition-Based Dependency Parsing | null | ['Fran{\\c{c}}ois Yvon', "Oph{\\'e}lie Lacroix", 'Lauriane Aufrant', 'Guillaume Wisniewski'] | 2016-06-01 | null | null | null | naacl-2016-6 | ['transition-based-dependency-parsing'] | ['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... | [-7.338669776916504, 3.7057290077209473] |
6eb7cccf-6668-4d51-9e82-aa173f26489f | turing-an-accurate-and-interpretable-multi | 2106.04559 | null | https://arxiv.org/abs/2106.04559v1 | https://arxiv.org/pdf/2106.04559v1.pdf | Turing: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface | A natural language database interface (NLDB) can democratize data-driven insights for non-technical users. However, existing Text-to-SQL semantic parsers cannot achieve high enough accuracy in the cross-database setting to allow good usability in practice. This work presents Turing, a NLDB system toward bridging this g... | ['Yanshuai Cao', 'Meidan Alon', 'Harsh Barot', 'Jawad Ateeq', 'Wei Yang', 'Keyi Tang', 'Ákos Kádár', 'Hamidreza Shahidi', 'Wenjie Zi', 'Peng Xu'] | 2021-06-08 | null | https://aclanthology.org/2021.acl-demo.36 | https://aclanthology.org/2021.acl-demo.36.pdf | acl-2021-5 | ['value-prediction'] | ['computer-code'] | [-2.16936588e-01 7.17507184e-01 -1.82994887e-01 -7.94499695e-01
-1.14185774e+00 -8.00470173e-01 3.02913010e-01 1.21841729e-01
1.12754941e-01 5.45282841e-01 1.32948741e-01 -8.02865028e-01
-1.33416653e-01 -1.14305890e+00 -6.51333749e-01 2.44253322e-01
1.50297508e-01 9.91627216e-01 4.05701071e-01 -5.03311574... | [9.826875686645508, 7.83422327041626] |
e9718359-3be1-4db8-9de0-d3daa1c774b9 | identifying-subgroups-of-icu-patients-using | 2306.02121 | null | https://arxiv.org/abs/2306.02121v1 | https://arxiv.org/pdf/2306.02121v1.pdf | Identifying Subgroups of ICU Patients Using End-to-End Multivariate Time-Series Clustering Algorithm Based on Real-World Vital Signs Data | This study employed the MIMIC-IV database as data source to investigate the use of dynamic, high-frequency, multivariate time-series vital signs data, including temperature, heart rate, mean blood pressure, respiratory rate, and SpO2, monitored first 8 hours data in the ICU stay. Various clustering algorithms were comp... | ['Guilan Kong', 'Huiying Zhao', 'Shuai Jin', 'Jianguo Hao', 'Junhua Fang', 'Wentie Liu', 'Zhilong Zhang', 'Tongyue Shi'] | 2023-06-03 | null | null | null | null | ['icu-mortality', 'time-series-clustering'] | ['medical', 'time-series'] | [-4.69796062e-01 -6.62593484e-01 1.34249389e-01 -1.28183261e-01
-2.22808525e-01 -4.55139816e-01 -1.74386874e-01 1.05865681e+00
-5.99238455e-01 3.63706738e-01 3.29119682e-01 -5.97581923e-01
-9.66399014e-01 -5.09519100e-01 2.06817523e-01 -8.31666946e-01
-6.62488341e-01 6.16392791e-01 -2.30791181e-01 3.07664812... | [7.976280212402344, 6.113954067230225] |
bd25fc08-e32e-4979-89c1-58924a691b90 | exploring-semantic-variations-in-gan-latent | 2305.14551 | null | https://arxiv.org/abs/2305.14551v1 | https://arxiv.org/pdf/2305.14551v1.pdf | Exploring Semantic Variations in GAN Latent Spaces via Matrix Factorization | Controlled data generation with GANs is desirable but challenging due to the nonlinearity and high dimensionality of their latent spaces. In this work, we explore image manipulations learned by GANSpace, a state-of-the-art method based on PCA. Through quantitative and qualitative assessments we show: (a) GANSpace produ... | ['Adil Khan', 'Rustam A. Lukmanov', 'Andrey Palaev'] | 2023-05-23 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 3.43762875e-01 -1.49778731e-03 -1.91770032e-01 2.71582484e-01
-6.66115105e-01 -1.11596572e+00 1.09482813e+00 -6.17085934e-01
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-1.80530325e-01 -5.89151025e-01 -7.53204942e-01 -1.01061153e+00
1.52290836e-01 1.88375086e-01 -3.55757892e-01 -9.60679948... | [11.650444984436035, -0.3575461506843567] |
a0eb48ad-af43-4e70-a337-40f92b2a860f | multi-task-deep-morphological-analyzer | 1811.08619 | null | https://arxiv.org/abs/1811.08619v2 | https://arxiv.org/pdf/1811.08619v2.pdf | Multi Task Deep Morphological Analyzer: Context Aware Joint Morphological Tagging and Lemma Prediction | The ambiguities introduced by the recombination of morphemes constructing several possible inflections for a word makes the prediction of syntactic traits in Morphologically Rich Languages (MRLs) a notoriously complicated task. We propose the Multi Task Deep Morphological analyzer (MT-DMA), a character-level neural mor... | ['Anil Kumar Singh', 'Akhilesh Sudhakar', 'Saurav Jha'] | 2018-11-21 | null | null | null | null | ['morphological-tagging'] | ['natural-language-processing'] | [-4.11246456e-02 -2.31957972e-01 1.80177748e-01 -5.41902065e-01
-1.22962844e+00 -8.56851459e-01 2.18476653e-01 5.42686343e-01
-7.29549706e-01 6.72781646e-01 1.64696455e-01 -7.09674001e-01
-6.17353246e-02 -6.46202087e-01 -5.83734035e-01 -7.28366673e-01
6.77613192e-04 7.98930943e-01 -8.88545364e-02 -1.58331737... | [10.445755958557129, 10.080602645874023] |
3ee0dbca-a6fc-482c-aa9b-8006a4bb10d2 | predicting-protein-secondary-structure-with | 1809.09210 | null | https://arxiv.org/abs/1809.09210v2 | https://arxiv.org/pdf/1809.09210v2.pdf | Predicting protein secondary structure with Neural Machine Translation | We present analysis of a novel tool for protein secondary structure prediction using the recently-investigated Neural Machine Translation framework. The tool provides a fast and accurate folding prediction based on primary structure with subsecond prediction time even for batched inputs. We hypothesize that Neural Mach... | ['Ian Bulovic', 'Evan Weissburg'] | 2018-09-24 | null | null | null | null | ['protein-secondary-structure-prediction'] | ['medical'] | [ 6.59923851e-01 1.00917175e-01 -3.27343851e-01 -7.53525078e-01
-6.65713787e-01 -5.76628447e-01 1.05440594e-01 2.12371439e-01
-4.02321517e-01 1.43344259e+00 1.32471606e-01 -1.13336146e+00
2.39850059e-01 -4.96917725e-01 -1.11061037e+00 -7.02969313e-01
-1.13121152e-01 6.02525055e-01 2.95248311e-02 -4.81421858... | [4.72514009475708, 5.600780963897705] |
2eaad1ce-78df-4120-a2e7-05d17b259811 | real-time-online-multi-object-tracking-in | 2204.02081 | null | https://arxiv.org/abs/2204.02081v1 | https://arxiv.org/pdf/2204.02081v1.pdf | Real-time Online Multi-Object Tracking in Compressed Domain | Recent online Multi-Object Tracking (MOT) methods have achieved desirable tracking performance. However, the tracking speed of most existing methods is rather slow. Inspired from the fact that the adjacent frames are highly relevant and redundant, we divide the frames into key and non-key frames respectively and track ... | ['Nenghai Yu', 'Weihai Li', 'Yue Wu', 'Bin Liu', 'Qiankun Liu'] | 2022-04-05 | null | null | null | null | ['online-multi-object-tracking'] | ['computer-vision'] | [-1.60974577e-01 -3.70047003e-01 -3.33546460e-01 1.84089869e-01
-3.72289658e-01 -4.11533177e-01 1.61289215e-01 1.14896119e-01
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1.89169958e-01 -4.88407642e-01 -7.11011827e-01 -7.97714591e-01
7.97831416e-02 1.98084071e-01 1.08004928e+00 3.21499586... | [6.422514915466309, -2.1157805919647217] |
5d1ae32b-5a52-4af8-9aeb-2a53432acdc4 | explaining-dialogue-evaluation-metrics-using | null | null | https://aclanthology.org/2022.naacl-main.430 | https://aclanthology.org/2022.naacl-main.430.pdf | Explaining Dialogue Evaluation Metrics using Adversarial Behavioral Analysis | There is an increasing trend in using neural methods for dialogue model evaluation. Lack of a framework to investigate these metrics can cause dialogue models to reflect their biases and cause unforeseen problems during interactions. In this work, we propose an adversarial test-suite which generates problematic variati... | ['Sungjin Lee', 'Baber Khalid'] | null | null | null | null | naacl-2022-7 | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 2.61353076e-01 5.95596254e-01 1.39269128e-01 -7.16657400e-01
-6.10570908e-01 -8.36119473e-01 1.03131449e+00 1.22845225e-01
-3.95234555e-01 1.06323004e+00 7.70024657e-01 -6.47132099e-01
-1.09473117e-01 -6.84425414e-01 -2.90870905e-01 -1.78284734e-01
1.21036157e-01 7.32529521e-01 -2.10579857e-01 -6.35494590... | [12.689497947692871, 8.116058349609375] |
db3dc823-c42f-4da0-ac7a-ccf4a5a30620 | rethinking-symmetric-matrix-factorization-a | 2209.02528 | null | https://arxiv.org/abs/2209.02528v2 | https://arxiv.org/pdf/2209.02528v2.pdf | Rethinking Symmetric Matrix Factorization: A More General and Better Clustering Perspective | Nonnegative matrix factorization (NMF) is widely used for clustering with strong interpretability. Among general NMF problems, symmetric NMF is a special one that plays an important role in graph clustering where each element measures the similarity between data points. Most existing symmetric NMF algorithms require fa... | ['Kai Liu', 'Mengyuan Zhang'] | 2022-09-06 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 1.80807654e-02 -1.99519143e-01 -3.15668434e-01 -1.24593481e-01
-1.92126572e-01 -6.85548365e-01 1.13791451e-01 5.43897115e-02
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-6.63126230e-01 -6.70855105e-01 -2.72240102e-01 -8.53568494e-01
6.87800422e-02 5.52342355e-01 -1.68097720e-01 -2.58182585... | [7.429959297180176, 4.836345672607422] |
3f7c4b99-f325-4f36-bc4b-aa1c4ec17666 | hierarchical-memory-networks | 1605.07427 | null | http://arxiv.org/abs/1605.07427v1 | http://arxiv.org/pdf/1605.07427v1.pdf | Hierarchical Memory Networks | Memory networks are neural networks with an explicit memory component that
can be both read and written to by the network. The memory is often addressed
in a soft way using a softmax function, making end-to-end training with
backpropagation possible. However, this is not computationally scalable for
applications which ... | ['Hugo Larochelle', 'Pascal Vincent', 'Yoshua Bengio', 'Gerald Tesauro', 'Sungjin Ahn', 'Sarath Chandar'] | 2016-05-24 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 9.82890949e-02 4.10364240e-01 -1.25480786e-01 -6.63961470e-01
-5.96095324e-01 -3.68597627e-01 3.90300274e-01 2.46078968e-01
-7.24065125e-01 9.06770468e-01 -6.77904934e-02 -4.09062624e-01
-7.86181763e-02 -1.36063170e+00 -1.48179913e+00 -4.24440503e-01
3.36889178e-01 9.65508103e-01 3.01983684e-01 1.04714446... | [11.001983642578125, 7.686696529388428] |
f0ad48c4-cf14-487c-aa68-b8e29f2d02e6 | encoding-structure-texture-relation-with-p | 2008.03632 | null | https://arxiv.org/abs/2008.03632v1 | https://arxiv.org/pdf/2008.03632v1.pdf | Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images | Anomaly detection in retinal image refers to the identification of abnormality caused by various retinal diseases/lesions, by only leveraging normal images in training phase. Normal images from healthy subjects often have regular structures (e.g., the structured blood vessels in the fundus image, or structured anatomy ... | ['Zaiwang Gu', 'Jianlong Yang', 'Wen Liu', 'Jiang Liu', 'Yuting Xiao', 'Shenghua Gao', 'Kang Zhou', 'Weixin Luo', 'Jun Cheng'] | 2020-08-09 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3484_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123650358.pdf | eccv-2020-8 | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 1.97999641e-01 8.91268998e-02 1.53945595e-01 -3.15404326e-01
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-1.08972616e-01 -6.92955315e-01 -6.86302006e-01 -1.11530697e+00
1.12638347e-01 -3.60471815e-01 1.25249147e-01 1.26593351... | [15.815592765808105, -3.9953906536102295] |
9ce4cb46-b028-4b91-a42b-21a28e71a4a3 | uor-universal-backdoor-attacks-on-pre-trained | 2305.09574 | null | https://arxiv.org/abs/2305.09574v1 | https://arxiv.org/pdf/2305.09574v1.pdf | UOR: Universal Backdoor Attacks on Pre-trained Language Models | Backdoors implanted in pre-trained language models (PLMs) can be transferred to various downstream tasks, which exposes a severe security threat. However, most existing backdoor attacks against PLMs are un-targeted and task-specific. Few targeted and task-agnostic methods use manually pre-defined triggers and output re... | ['Gongshen Liu', 'Haodong Zhao', 'Boqun Li', 'Peixuan Li', 'Wei Du'] | 2023-05-16 | null | null | null | null | ['backdoor-attack'] | ['adversarial'] | [-5.38259260e-02 -4.22344297e-01 -6.12565756e-01 -4.92671989e-02
-7.37426579e-01 -1.24548972e+00 8.62781584e-01 5.18419631e-02
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5.87900616e-02 5.43692261e-02 4.79561180e-01 -5.22530556... | [6.086513042449951, 7.950823783874512] |
eff4cc88-d702-4e7c-9aa1-3659984309e6 | wild-devs-at-semeval-2017-task-2-using-neural | null | null | https://aclanthology.org/S17-2042 | https://aclanthology.org/S17-2042.pdf | Wild Devs' at SemEval-2017 Task 2: Using Neural Networks to Discover Word Similarity | This paper presents Wild Devs{'} participation in the SemEval-2017 Task 2 {``}Multi-lingual and Cross-lingual Semantic Word Similarity{''}, which tries to automatically measure the semantic similarity between two words. The system was build using neural networks, having as input a collection of word pairs, whereas the ... | ['Diana ab{\\u{a}}{\\textcommabelow{t}}', 'Tr', 'Alina Beatrice Loren{\\c{t}}', 'Mihaela Pl{\\u{a}}mad{\\u{a}}-Onofrei', '{\\textcommabelow{S}}tefan Oprea', 'Ionu{\\textcommabelow{t}} Hulub', 'R{\\u{a}}zvan-Gabriel Rotari', 'Adrian Iftene', 'Raluca Preisler'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['multilingual-word-embeddings'] | ['methodology'] | [-1.44261867e-02 4.77673346e-03 2.36507609e-01 -7.41667628e-01
-7.82028377e-01 -7.20921516e-01 7.85402298e-01 8.44334960e-01
-1.12868166e+00 2.69828200e-01 3.28887075e-01 -1.70309678e-01
2.10750476e-02 -5.82888663e-01 -4.36830580e-01 -3.19095284e-01
3.10789913e-01 7.81178176e-01 2.55979747e-01 -5.66644788... | [10.761832237243652, 9.615560531616211] |
7bd74741-59db-4c7d-9045-1c595c0b4722 | analyzing-coreference-and-bridging-in-product | null | null | https://aclanthology.org/2022.crac-1.3 | https://aclanthology.org/2022.crac-1.3.pdf | Analyzing Coreference and Bridging in Product Reviews | Product reviews may have complex discourse including coreference and bridging relations to a main product, competing products, and interacting products. Current approaches to aspect-based sentiment analysis (ABSA) and opinion summarization largely ignore this complexity. On the other hand, existing systems for corefere... | ['Christopher Malon', 'Hideo Kobayashi'] | null | null | null | null | coling-crac-2022-10 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 2.70916730e-01 1.04558432e+00 -6.36213183e-01 -4.49528098e-01
-1.29976392e+00 -1.00175619e+00 6.53785825e-01 7.55475581e-01
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1.51841253e-01 -4.24415231e-01 -4.09716547e-01 -7.70685524e-02
4.92872566e-01 9.34900582e-01 1.71854272e-01 -8.32168460... | [11.156558990478516, 7.052066802978516] |
bdfb1ae1-42a7-43e9-bc9c-70f17d8aeff6 | co-attention-network-with-label-embedding-for | null | null | https://mqianliu.github.io/files/CNLE_Neurocomputing22.pdf | https://mqianliu.github.io/files/CNLE_Neurocomputing22.pdf | Co-attention network with label embedding for text classification | Most existing methods for text classification focus on extracting a highly discriminative text representation, which, however, is typically computationally inefficient. To alleviate this issue, label embedding frameworks are proposed to adopt the label-to-text attention that directly uses label information to construct... | ['Qing Du', 'Junyi Cao', 'Lizhao Liu', 'Minqian Liu'] | 2021-11-04 | null | null | null | neurocomputing-2021-11 | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 4.89078790e-01 1.10870205e-01 -6.35751486e-01 -7.01226950e-01
-8.46858382e-01 -4.30116326e-01 6.99087858e-01 2.21859485e-01
-3.94143134e-01 4.08404946e-01 3.36843669e-01 -1.11337304e-01
1.93669543e-01 -5.43974400e-01 -1.04978383e-01 -8.38538587e-01
5.99590242e-01 4.32754040e-01 -1.57486275e-01 1.86705917... | [9.70422649383545, 4.448497295379639] |
757a0473-3223-4785-9b98-45eff70d197a | learning-implicit-text-generation-via-feature | 2005.03588 | null | https://arxiv.org/abs/2005.03588v2 | https://arxiv.org/pdf/2005.03588v2.pdf | Learning Implicit Text Generation via Feature Matching | Generative feature matching network (GFMN) is an approach for training implicit generative models for images by performing moment matching on features from pre-trained neural networks. In this paper, we present new GFMN formulations that are effective for sequential data. Our experimental results show the effectiveness... | ['Cicero Nogueira dos santos', 'Inkit Padhi', 'Youssef Mroueh', 'Pierre Dognin', 'Payel Das', 'Vijil Chenthamarakshan', 'Ke Bai'] | 2020-05-07 | learning-implicit-text-generation-via-feature-1 | https://aclanthology.org/2020.acl-main.354 | https://aclanthology.org/2020.acl-main.354.pdf | acl-2020-6 | ['conditional-text-generation'] | ['natural-language-processing'] | [ 6.93800032e-01 1.71191618e-01 4.29239981e-02 -5.46570301e-01
-8.42619598e-01 -3.32905382e-01 1.27373159e+00 -6.54576600e-01
-1.27684161e-01 1.01472831e+00 3.95111710e-01 -9.49937403e-02
4.15245742e-01 -8.93195152e-01 -6.54134870e-01 -5.91635525e-01
5.80528796e-01 7.40948021e-01 -2.26778522e-01 -2.79608369... | [11.858040809631348, 9.346879959106445] |
a061597b-9f15-4654-ad47-ec85feee1e5c | bijective-mapping-network-for-shadow-removal | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhu_Bijective_Mapping_Network_for_Shadow_Removal_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhu_Bijective_Mapping_Network_for_Shadow_Removal_CVPR_2022_paper.pdf | Bijective Mapping Network for Shadow Removal | Shadow removal, which aims to restore the background in the shadow regions, is challenging due to the highly ill-posed nature. Most existing deep learning-based methods individually remove the shadow by only considering the content of the matched paired images, barely taking into account the auxiliary supervision o... | ['Zheng-Jun Zha', 'Qibin Sun', 'Feng Zhao', 'Xueyang Fu', 'Jie Huang', 'Yurui Zhu'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['shadow-removal'] | ['computer-vision'] | [ 3.15003067e-01 -2.44491383e-01 2.31366143e-01 -3.06936473e-01
-2.61475742e-01 -3.31540674e-01 2.63685584e-01 -5.27155876e-01
-1.96808532e-01 8.46566260e-01 9.05552134e-03 -5.34049094e-01
1.90908298e-01 -6.45170212e-01 -6.97433174e-01 -1.17282748e+00
1.64100841e-01 -2.32894178e-02 4.83558714e-01 -2.29233593... | [10.859941482543945, -4.038919925689697] |
50c72e76-e376-4f2b-9bbc-3f085c7f628d | trustworthy-reinforcement-learning-for | 2302.11694 | null | https://arxiv.org/abs/2302.11694v1 | https://arxiv.org/pdf/2302.11694v1.pdf | Trustworthy Reinforcement Learning for Quadrotor UAV Tracking Control Systems | Simultaneously accurate and reliable tracking control for quadrotors in complex dynamic environments is challenging. As aerodynamics derived from drag forces and moment variations are chaotic and difficult to precisely identify, most current quadrotor tracking systems treat them as simple `disturbances' in conventional... | ['David Boyle', 'Yanran Wang'] | 2023-02-22 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.13731766e-01 -8.08138102e-02 -3.55948240e-01 5.35834849e-01
-4.08094019e-01 -1.06001616e+00 4.04911786e-01 -9.77560654e-02
-2.69899875e-01 1.14788270e+00 -3.25940967e-01 -3.18634897e-01
-4.73545820e-01 -3.16162735e-01 -9.39144135e-01 -1.03591967e+00
-7.50386044e-02 5.23289852e-02 -3.46214534e-03 -2.93130726... | [5.015445709228516, 2.3321259021759033] |
c69e72ed-0190-40fb-a0d9-b80aff708b2f | a-novel-deep-arrhythmia-diagnosis-network-for | null | null | https://doi.org/10.1109/ACCESS.2019.2918792 | https://ieeexplore.ieee.org/ielx7/6287639/8600701/08721643.pdf | A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram Signals | Atrial fibrillation (AF), a common abnormal heartbeat rhythm, is a life-threatening recurrent disease that affects older adults. Automatic classification is one of the most valuable topics in medical sciences and bioinformatics, especially the detection of atrial fibrillation. However, it is difficult to accurately exp... | ['Hao Dang', 'Xingqun Qi', 'Xiaoguang Zhou', 'Guanhong Zhang', 'Qing Chang', 'Muyi Sun'] | 2019-05-24 | null | null | null | ieee-access-2019-5 | ['arrhythmia-detection', 'atrial-fibrillation-detection', 'electrocardiography-ecg'] | ['medical', 'medical', 'methodology'] | [ 1.14473850e-01 -3.84773165e-01 1.32600412e-01 -7.18847513e-02
-2.97807217e-01 -1.15306549e-01 -1.72960088e-01 1.53995112e-01
-3.36094469e-01 1.09660470e+00 -3.57708007e-01 -5.18857956e-01
-4.20374572e-02 -7.96120405e-01 -1.12242542e-01 -8.34916353e-01
-2.21867591e-01 9.88818407e-02 -2.57061332e-01 1.51795313... | [14.249869346618652, 3.295222043991089] |
e8c564d6-6638-4b7e-92be-acba30c4ce78 | frequency-of-interest-based-noise-attenuation | 2210.11068 | null | https://arxiv.org/abs/2210.11068v3 | https://arxiv.org/pdf/2210.11068v3.pdf | Frequency of Interest-based Noise Attenuation Method to Improve Anomaly Detection Performance | Accurately extracting driving events is the way to maximize computational efficiency and anomaly detection performance in the tire frictional nose-based anomaly detection task. This study proposes a concise and highly useful method for improving the precision of the event extraction that is hindered by extra noise such... | ['Won Seok Park', 'Myung Jin Kim', 'YeongHyeon Park'] | 2022-10-20 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 2.34725326e-01 -4.44490999e-01 2.06537634e-01 7.88823590e-02
-4.16229934e-01 -1.95263222e-01 4.32660758e-01 2.39873856e-01
-7.12536156e-01 4.25829351e-01 -2.29298413e-01 -3.30658168e-01
-3.76629204e-01 -1.11033452e+00 -4.06698793e-01 -9.36006308e-01
6.37771562e-02 -1.56336099e-01 5.50528705e-01 -3.20697635... | [8.00756549835205, -0.8205987811088562] |
146d8e57-8801-4097-b31f-33f08714ae53 | deep-supervision-for-pancreatic-cyst | 1706.07346 | null | http://arxiv.org/abs/1706.07346v1 | http://arxiv.org/pdf/1706.07346v1.pdf | Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans | Automatic segmentation of an organ and its cystic region is a prerequisite of
computer-aided diagnosis. In this paper, we focus on pancreatic cyst
segmentation in abdominal CT scan. This task is important and very useful in
clinical practice yet challenging due to the low contrast in boundary, the
variability in locati... | ['Elliot K. Fishman', 'Yuyin Zhou', 'Lingxi Xie', 'Alan L. Yuille'] | 2017-06-22 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [-1.02770068e-01 3.28948051e-01 -1.79800466e-01 -3.96619737e-01
-3.54039311e-01 -4.46489125e-01 1.47087917e-01 4.36454773e-01
-6.58389091e-01 4.22558188e-01 3.61820422e-02 -2.01009676e-01
-9.34533700e-02 -4.76513892e-01 -6.32922888e-01 -8.58774543e-01
-2.92248368e-01 6.60151839e-01 3.40894610e-01 2.90778458... | [14.585638999938965, -2.7163445949554443] |
bdb17e0f-095b-415c-a762-acbcdfc96c67 | fairface-face-attribute-dataset-for-balanced | 1908.04913 | null | https://arxiv.org/abs/1908.04913v1 | https://arxiv.org/pdf/1908.04913v1.pdf | FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age | Existing public face datasets are strongly biased toward Caucasian faces, and other races (e.g., Latino) are significantly underrepresented. This can lead to inconsistent model accuracy, limit the applicability of face analytic systems to non-White race groups, and adversely affect research findings based on such skewe... | ['Kimmo Kärkkäinen', 'Jungseock Joo'] | 2019-08-14 | null | null | null | null | ['facial-attribute-classification'] | ['computer-vision'] | [ 4.40910906e-02 -7.47667253e-02 -6.10224128e-01 -9.20437217e-01
-2.18427703e-01 -3.93623948e-01 4.95001882e-01 -2.39743993e-01
-3.04692835e-01 7.12392330e-01 5.23907423e-01 -2.44910449e-01
1.35174431e-02 -7.71188200e-01 -2.30017468e-01 -3.30575824e-01
2.23826200e-01 2.48457298e-01 -8.83702040e-01 1.68264225... | [13.0468168258667, 1.2537099123001099] |
aee0e32b-7f52-45da-a6d7-df53641dec56 | towards-better-understanding-with-uniformity | 2206.07960 | null | https://arxiv.org/abs/2206.07960v1 | https://arxiv.org/pdf/2206.07960v1.pdf | Towards Better Understanding with Uniformity and Explicit Regularization of Embeddings in Embedding-based Neural Topic Models | Embedding-based neural topic models could explicitly represent words and topics by embedding them to a homogeneous feature space, which shows higher interpretability. However, there are no explicit constraints for the training of embeddings, leading to a larger optimization space. Also, a clear description of the chang... | ['Linqi Song', 'Shihua Ma', 'Shuqi Liu', 'Lei Huang', 'Wei Shao'] | 2022-06-16 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-1.62456751e-01 3.31630737e-01 -3.25579673e-01 -4.15029734e-01
-5.09686351e-01 -4.15732741e-01 8.01984966e-01 8.05417150e-02
-3.63997996e-01 1.59287348e-01 5.11487067e-01 -1.40846357e-01
-8.94742906e-02 -8.66779327e-01 -6.01387203e-01 -5.99887788e-01
-9.14550871e-02 7.92917758e-02 -1.28087765e-02 -9.79450494... | [10.502030372619629, 7.145901203155518] |
9cba1bed-2a10-4ec4-bd9c-7889e91f9feb | timereplayer-unlocking-the-potential-of-event | 2203.13859 | null | https://arxiv.org/abs/2203.13859v1 | https://arxiv.org/pdf/2203.13859v1.pdf | TimeReplayer: Unlocking the Potential of Event Cameras for Video Interpolation | Recording fast motion in a high FPS (frame-per-second) requires expensive high-speed cameras. As an alternative, interpolating low-FPS videos from commodity cameras has attracted significant attention. If only low-FPS videos are available, motion assumptions (linear or quadratic) are necessary to infer intermediate fra... | ['Jianxing Liao', 'Yaoyuan Wang', 'Huchuan Lu', 'Wenhui Wang', 'Ziyang Zhang', 'Xu Jia', 'Zhendong Qiao', 'Kaichao You', 'Weihua He'] | 2022-03-25 | null | http://openaccess.thecvf.com//content/CVPR2022/html/He_TimeReplayer_Unlocking_the_Potential_of_Event_Cameras_for_Video_Interpolation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/He_TimeReplayer_Unlocking_the_Potential_of_Event_Cameras_for_Video_Interpolation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['event-based-vision'] | ['computer-vision'] | [ 2.37084419e-01 -1.89841688e-01 -1.59073733e-02 -9.37195495e-02
-3.25456679e-01 -2.44837940e-01 2.67621040e-01 -4.21611905e-01
-5.70318997e-01 6.40419781e-01 -3.74467403e-01 -2.91395485e-01
2.29235202e-01 -7.07782507e-01 -1.02875721e+00 -4.11060482e-01
-1.48271248e-01 -2.06630364e-01 7.84222424e-01 2.93706600... | [8.681621551513672, -1.315900444984436] |
08ef8436-aa3a-482f-b7f3-a2fe9056131c | disentangling-monocular-3d-object-detection | 1905.12365 | null | https://arxiv.org/abs/1905.12365v1 | https://arxiv.org/pdf/1905.12365v1.pdf | Disentangling Monocular 3D Object Detection | In this paper we propose an approach for monocular 3D object detection from a single RGB image, which leverages a novel disentangling transformation for 2D and 3D detection losses and a novel, self-supervised confidence score for 3D bounding boxes. Our proposed loss disentanglement has the twofold advantage of simplify... | ['Manuel López-Antequera', 'Samuel Rota Rota Bulò', 'Peter Kontschieder', 'Lorenzo Porzi', 'Andrea Simonelli'] | 2019-05-29 | disentangling-monocular-3d-object-detection-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Simonelli_Disentangling_Monocular_3D_Object_Detection_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Simonelli_Disentangling_Monocular_3D_Object_Detection_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-object-detection-from-monocular-images'] | ['computer-vision'] | [ 4.77413312e-02 -1.85309827e-01 -2.15348497e-01 -2.39437416e-01
-8.95065427e-01 -8.63384724e-01 6.37804449e-01 -2.49380507e-02
-7.47618794e-01 4.61558282e-01 -2.70160347e-01 -4.56383616e-01
-8.35859755e-05 -4.86719340e-01 -8.94163847e-01 -8.28113079e-01
-1.94793586e-02 3.24389279e-01 6.18703604e-01 2.79308632... | [7.771485328674316, -2.6213741302490234] |
0ce9c862-49c4-4bff-83f8-7d10ab88410d | online-multi-object-tracking-using-cnn-based | 1708.02843 | null | http://arxiv.org/abs/1708.02843v2 | http://arxiv.org/pdf/1708.02843v2.pdf | Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism | In this paper, we propose a CNN-based framework for online MOT. This
framework utilizes the merits of single object trackers in adapting appearance
models and searching for target in the next frame. Simply applying single
object tracker for MOT will encounter the problem in computational efficiency
and drifted results ... | ['Nenghai Yu', 'Wanli Ouyang', 'Hongsheng Li', 'Bin Liu', 'Qi Chu', 'Xiaogang Wang'] | 2017-08-09 | online-multi-object-tracking-using-cnn-based-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Chu_Online_Multi-Object_Tracking_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Chu_Online_Multi-Object_Tracking_ICCV_2017_paper.pdf | iccv-2017-10 | ['online-multi-object-tracking'] | ['computer-vision'] | [-1.78782687e-01 -3.15526068e-01 5.02036558e-03 -2.93477535e-01
-3.10647100e-01 -1.76398501e-01 3.16461295e-01 -1.47146270e-01
-7.10232258e-01 5.72820425e-01 -1.86955258e-02 4.47563112e-01
5.81927560e-02 -4.97301608e-01 -8.93995345e-01 -9.96272862e-01
-1.80628717e-01 3.15910012e-01 8.45423758e-01 8.27650428... | [6.3127875328063965, -2.1071486473083496] |
6c5b09dc-1eed-4385-aa72-8ee0231fff71 | musical-instrument-sound-classification-with | 1512.07370 | null | http://arxiv.org/abs/1512.07370v1 | http://arxiv.org/pdf/1512.07370v1.pdf | Musical instrument sound classification with deep convolutional neural network using feature fusion approach | A new musical instrument classification method using convolutional neural
networks (CNNs) is presented in this paper. Unlike the traditional methods, we
investigated a scheme for classifying musical instruments using the learned
features from CNNs. To create the learned features from CNNs, we not only used
a convention... | ['Lee Taejin', 'Park Taejin'] | 2015-12-23 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 3.51010054e-01 -3.90317053e-01 1.29495293e-01 -1.51114285e-01
-5.95149815e-01 -8.43122423e-01 4.27523166e-01 -1.92463044e-02
-4.29269075e-01 5.56557655e-01 1.44316629e-01 1.40161887e-01
-4.47611392e-01 -9.54560935e-01 -5.50238550e-01 -6.63921535e-01
-1.65878966e-01 -4.22241658e-01 -2.00376093e-01 -2.52725512... | [15.800642967224121, 5.256643772125244] |
ad284e83-e5c9-4533-986c-b04bc3d4f704 | uncorrelated-semi-paired-subspace-learning | 2011.11124 | null | https://arxiv.org/abs/2011.11124v1 | https://arxiv.org/pdf/2011.11124v1.pdf | Uncorrelated Semi-paired Subspace Learning | Multi-view datasets are increasingly collected in many real-world applications, and we have seen better learning performance by existing multi-view learning methods than by conventional single-view learning methods applied to each view individually. But, most of these multi-view learning methods are built on the assump... | ['Ren-cang Li', 'Chungen Shen', 'Lei-Hong Zhang', 'Li Wang'] | 2020-11-22 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 4.63127531e-02 -4.11115527e-01 -2.78093129e-01 -2.60497153e-01
-1.00184286e+00 -7.82066345e-01 5.73619008e-01 -5.97329259e-01
-1.63413465e-01 5.82325757e-01 3.61529440e-01 1.29013762e-01
-3.31822723e-01 -3.77616078e-01 -4.53637928e-01 -1.03286481e+00
2.20289052e-01 4.54989523e-01 -1.53822929e-01 5.51667847... | [8.363335609436035, 4.551107406616211] |
bb429903-02b6-4c40-ade6-52d83d091d41 | searching-for-discriminative-words-in | 2211.14631 | null | https://arxiv.org/abs/2211.14631v1 | https://arxiv.org/pdf/2211.14631v1.pdf | Searching for Discriminative Words in Multidimensional Continuous Feature Space | Word feature vectors have been proven to improve many NLP tasks. With recent advances in unsupervised learning of these feature vectors, it became possible to train it with much more data, which also resulted in better quality of learned features. Since it learns joint probability of latent features of words, it has th... | ['Maria Bielikova', 'Michal Barla', 'Marius Sajgalik'] | 2022-11-26 | null | null | null | null | ['part-of-speech-tagging'] | ['natural-language-processing'] | [ 2.05066532e-01 2.08486468e-01 -4.23631102e-01 -5.94151556e-01
-8.12504709e-01 -7.25728095e-01 1.03654301e+00 5.47607720e-01
-5.52432597e-01 5.33061266e-01 5.78882813e-01 -2.33930126e-01
-4.91963536e-01 -7.30695486e-01 -3.13873053e-01 -7.84265757e-01
-1.57711685e-01 5.80795228e-01 3.16354930e-01 -2.25054830... | [10.424077987670898, 8.381789207458496] |
93f3e823-96f4-49fc-8681-abffc1088848 | synthetically-generating-human-like-data-for | 2304.07280 | null | https://arxiv.org/abs/2304.07280v1 | https://arxiv.org/pdf/2304.07280v1.pdf | Synthetically Generating Human-like Data for Sequential Decision Making Tasks via Reward-Shaped Imitation Learning | We consider the problem of synthetically generating data that can closely resemble human decisions made in the context of an interactive human-AI system like a computer game. We propose a novel algorithm that can generate synthetic, human-like, decision making data while starting from a very small set of decision makin... | ['Prithviraj Dasgupta', 'Bryan Brandt'] | 2023-04-14 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 4.53399926e-01 7.03091979e-01 5.82079351e-01 -7.31689706e-02
-3.03267449e-01 -6.69221580e-01 9.49734688e-01 2.58289482e-02
-9.51298773e-01 9.42696810e-01 -2.12564304e-01 -1.79589570e-01
-6.41949847e-02 -6.82291985e-01 -3.89478266e-01 -5.08875430e-01
1.51876882e-02 1.02622819e+00 2.61947751e-01 -4.91565526... | [3.8855087757110596, 1.6143741607666016] |
a6fd04ae-29a8-404d-9df6-501125a69791 | learning-structural-representations-for | 2110.01209 | null | https://arxiv.org/abs/2110.01209v2 | https://arxiv.org/pdf/2110.01209v2.pdf | Learning Structural Representations for Recipe Generation and Food Retrieval | Food is significant to human daily life. In this paper, we are interested in learning structural representations for lengthy recipes, that can benefit the recipe generation and food cross-modal retrieval tasks. Different from the common vision-language data, here the food images contain mixed ingredients and target rec... | ['Chunyan Miao', 'Steven C. H. Hoi', 'Guosheng Lin', 'Hao Wang'] | 2021-10-04 | null | null | null | null | ['recipe-generation'] | ['miscellaneous'] | [ 3.37078989e-01 -2.29043469e-01 -1.50797904e-01 -4.07339096e-01
-1.04610443e+00 -8.84889960e-01 4.19083178e-01 6.17869139e-01
-1.67499948e-02 8.64484534e-02 7.23993957e-01 5.00863083e-02
1.49702337e-02 -1.13101327e+00 -9.42176223e-01 -8.01444769e-01
2.12807164e-01 3.18416893e-01 -2.23157331e-01 -3.34521085... | [11.516178131103516, 4.4272003173828125] |
52927812-0666-47c9-9adf-2e264a835f80 | talking-detection-in-collaborative-learning | 2110.07646 | null | https://arxiv.org/abs/2110.07646v1 | https://arxiv.org/pdf/2110.07646v1.pdf | Talking Detection In Collaborative Learning Environments | We study the problem of detecting talking activities in collaborative learning videos. Our approach uses head detection and projections of the log-magnitude of optical flow vectors to reduce the problem to a simple classification of small projection images without the need for training complex, 3-D activity classificat... | ['Carlos LópezLeiva', 'Sylvia Celedón-Pattichis', 'Marios S. Pattichis', 'Wenjing Shi'] | 2021-10-14 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [ 1.80320397e-01 3.79886091e-01 -4.83805239e-02 -1.71317965e-01
-7.22040117e-01 -6.38607085e-01 7.75482178e-01 -3.73822033e-01
-2.45895907e-01 2.71038115e-01 4.53853339e-01 -1.26778170e-01
2.88145781e-01 -1.75454646e-01 -3.84205848e-01 -6.75465822e-01
-2.59571970e-01 2.88460255e-01 3.73498231e-01 4.60600019... | [14.42126750946045, 5.0834574699401855] |
df3ecdda-cc28-427b-9331-cc332ca82430 | an-investigation-into-pre-training-object | 2302.04419 | null | https://arxiv.org/abs/2302.04419v3 | https://arxiv.org/pdf/2302.04419v3.pdf | An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning | Unsupervised object-centric representation (OCR) learning has recently drawn attention as a new paradigm of visual representation. This is because of its potential of being an effective pre-training technique for various downstream tasks in terms of sample efficiency, systematic generalization, and reasoning. Although ... | ['Sungjin Ahn', 'Heechul Bae', 'Yi-Fu Wu', 'Jaesik Yoon'] | 2023-02-09 | null | null | null | null | ['optical-character-recognition', 'systematic-generalization'] | ['computer-vision', 'reasoning'] | [ 3.20743799e-01 -1.00718774e-01 -3.64805609e-01 -2.09797636e-01
-8.45429957e-01 -4.39990252e-01 5.38125753e-01 3.71627241e-01
-7.93967128e-01 5.28193235e-01 5.77585436e-02 -3.98797810e-01
-1.54854685e-01 -5.99384785e-01 -8.61550212e-01 -6.80530131e-01
-5.95904365e-02 1.69472173e-01 3.16700667e-01 -2.04797342... | [9.761754989624023, 1.7031868696212769] |
da8ed64d-6679-432e-885c-06aff5b68e28 | towards-unifying-multi-lingual-and-cross | 2305.09220 | null | https://arxiv.org/abs/2305.09220v1 | https://arxiv.org/pdf/2305.09220v1.pdf | Towards Unifying Multi-Lingual and Cross-Lingual Summarization | To adapt text summarization to the multilingual world, previous work proposes multi-lingual summarization (MLS) and cross-lingual summarization (CLS). However, these two tasks have been studied separately due to the different definitions, which limits the compatible and systematic research on both of them. In this pape... | ['Jie zhou', 'Jianfeng Qu', 'Zhixu Li', 'Yunlong Liang', 'Duo Zheng', 'Fandong Meng', 'Jiaan Wang'] | 2023-05-16 | null | null | null | null | ['text-summarization'] | ['natural-language-processing'] | [ 5.26569821e-02 2.47312933e-01 -4.50924367e-01 -2.34287411e-01
-1.52024007e+00 -6.05571508e-01 9.03649628e-01 2.38499165e-01
-3.04199517e-01 9.73553300e-01 9.46999729e-01 -2.76759207e-01
5.06947458e-01 -4.30583954e-01 -7.20780313e-01 -2.70835251e-01
2.67182678e-01 5.14575124e-01 2.02631548e-01 -3.67187589... | [12.423633575439453, 9.54761028289795] |
9737953f-5e58-4ac2-9b6d-3ed67734120d | optimized-eeg-based-mood-detection-with | 2304.01349 | null | https://arxiv.org/abs/2304.01349v1 | https://arxiv.org/pdf/2304.01349v1.pdf | Optimized EEG based mood detection with signal processing and deep neural networks for brain-computer interface | Electroencephalogram (EEG) is a very promising and widely implemented procedure to study brain signals and activities by amplifying and measuring the post-synaptical potential arising from electrical impulses produced by neurons and detected by specialized electrodes attached to specific points in the scalp. It can be ... | ['Deepraj Chowdhury', 'Biswajit Saha', 'Kushal Jain', 'Subhrangshu Adhikary'] | 2023-03-30 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 3.07294220e-01 -3.45878720e-01 5.13227284e-01 -3.23127091e-01
1.70282841e-01 -2.12403789e-01 3.54080558e-01 3.35134417e-01
-6.02275014e-01 8.12013090e-01 -5.50717190e-02 3.54694091e-02
-2.16329172e-01 -5.31674385e-01 -1.77729860e-01 -8.61166120e-01
-5.07614076e-01 -1.11809708e-01 -1.02732599e-01 5.91717884... | [13.309934616088867, 3.2971301078796387] |
fe39b6d8-1858-4bbb-96e0-3aabdb474e8f | occupancy-planes-for-single-view-rgb-d-human | 2208.02817 | null | https://arxiv.org/abs/2208.02817v2 | https://arxiv.org/pdf/2208.02817v2.pdf | Occupancy Planes for Single-view RGB-D Human Reconstruction | Single-view RGB-D human reconstruction with implicit functions is often formulated as per-point classification. Specifically, a set of 3D locations within the view-frustum of the camera are first projected independently onto the image and a corresponding feature is subsequently extracted for each 3D location. The featu... | ['Alexander G. Schwing', 'Zhongzheng Ren', 'Yuan-Ting Hu', 'Xiaoming Zhao'] | 2022-08-04 | null | null | null | null | ['3d-human-reconstruction'] | ['computer-vision'] | [ 2.24832416e-01 6.66218325e-02 -8.81062001e-02 -2.38021657e-01
-4.87848014e-01 -5.78131139e-01 7.63276756e-01 2.52794147e-01
-2.01717928e-01 4.82939243e-01 1.65419698e-01 4.94957305e-02
-1.10353880e-01 -6.84535742e-01 -5.82496881e-01 -6.83308184e-01
5.53326793e-02 6.65628314e-01 3.14261287e-01 9.04669017... | [7.6331787109375, -2.6096267700195312] |
d76c27f7-a7ff-497d-a726-f3f8e0782cde | unsupervised-semantic-correspondence-using | 2305.15581 | null | https://arxiv.org/abs/2305.15581v1 | https://arxiv.org/pdf/2305.15581v1.pdf | Unsupervised Semantic Correspondence Using Stable Diffusion | Text-to-image diffusion models are now capable of generating images that are often indistinguishable from real images. To generate such images, these models must understand the semantics of the objects they are asked to generate. In this work we show that, without any training, one can leverage this semantic knowledge ... | ['Kwang Moo Yi', 'Andrea Tagliasacchi', 'Abhishek Kar', 'Hossam Isack', 'Shweta Mahajan', 'Gopal Sharma', 'Eric Hedlin'] | 2023-05-24 | null | null | null | null | ['semantic-correspondence'] | ['computer-vision'] | [ 3.56008440e-01 4.30513471e-01 -1.80265293e-01 -3.12061787e-01
-5.61503589e-01 -7.25352705e-01 1.15686047e+00 2.52967458e-02
-5.24622858e-01 5.83535075e-01 5.08176506e-01 -6.43186346e-02
-1.30451709e-01 -9.56999481e-01 -8.65630209e-01 -6.57868147e-01
1.94902554e-01 8.04836094e-01 3.42111766e-01 -1.36147916... | [11.115790367126465, -0.01372417714446783] |
f7dc8592-5189-4717-a605-854f78f4fcdd | automated-refugee-case-analysis-an-nlp | 2305.15533 | null | https://arxiv.org/abs/2305.15533v1 | https://arxiv.org/pdf/2305.15533v1.pdf | Automated Refugee Case Analysis: An NLP Pipeline for Supporting Legal Practitioners | In this paper, we introduce an end-to-end pipeline for retrieving, processing, and extracting targeted information from legal cases. We investigate an under-studied legal domain with a case study on refugee law in Canada. Searching case law for past similar cases is a key part of legal work for both lawyers and judges,... | ['Nehal Bhuta', 'Michael Rovatsos', 'Claire Barale'] | 2023-05-24 | null | null | null | null | ['named-entity-recognition-ner'] | ['natural-language-processing'] | [-8.22561681e-02 1.81383774e-01 -4.11685169e-01 -6.83310986e-01
-1.26934361e+00 -7.85828412e-01 6.77396536e-01 3.38734061e-01
-1.24720645e+00 6.40574694e-01 9.07463253e-01 -5.95698178e-01
-5.61294973e-01 -6.91972494e-01 -3.53079438e-01 1.41083766e-02
-1.96408853e-02 6.64214551e-01 1.04599029e-01 -2.24001318... | [9.864666938781738, 9.301202774047852] |
38231643-c552-48b8-a531-c5d35f637c1e | sclifd-supervised-contrastive-knowledge | 2302.05929 | null | https://arxiv.org/abs/2302.05929v1 | https://arxiv.org/pdf/2302.05929v1.pdf | SCLIFD:Supervised Contrastive Knowledge Distillation for Incremental Fault Diagnosis under Limited Fault Data | Intelligent fault diagnosis has made extraordinary advancements currently. Nonetheless, few works tackle class-incremental learning for fault diagnosis under limited fault data, i.e., imbalanced and long-tailed fault diagnosis, which brings about various notable challenges. Initially, it is difficult to extract discrim... | ['Weiming Shen', 'Hongwei Wang', 'Gongzhuang Peng', 'Mengxuan Li', 'Hanrong Zhang', 'Peng Peng'] | 2023-02-12 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [ 2.13904440e-01 -5.31751633e-01 -2.44543493e-01 -1.09125383e-01
-1.95803270e-01 5.18164597e-02 1.39114141e-01 2.44949520e-01
3.62760462e-02 8.46175075e-01 -3.99838477e-01 -1.30128577e-01
-6.66717768e-01 -8.16830397e-01 -4.34740692e-01 -9.80687976e-01
8.46184641e-02 4.79281962e-01 3.18769217e-01 -7.25577846... | [7.3403639793396, 2.307368755340576] |
865739ac-1abc-418f-9db6-066e9f5fea60 | phase-retrieval-via-non-rigid-image | 2306.15701 | null | https://arxiv.org/abs/2306.15701v1 | https://arxiv.org/pdf/2306.15701v1.pdf | Phase retrieval via non-rigid image registration | Phase retrieval is the numerical procedure of recovering a complex-valued signal from knowledge about its amplitude and some additional information. Here, an indirect registration procedure, based on the large deformation diffeomorphic metric mapping (LDDMM) formalism, is investigated as a phase retrieval method for co... | ['Erik Malm'] | 2023-06-26 | null | null | null | null | ['image-registration', 'retrieval'] | ['computer-vision', 'methodology'] | [ 4.98024911e-01 1.19498610e-01 4.24452811e-01 -3.10838014e-01
-5.94277799e-01 -7.08572716e-02 6.49024367e-01 -5.88946879e-01
-6.47624195e-01 5.95835626e-01 -2.62065860e-03 2.79473782e-01
-7.41555989e-01 -8.08300614e-01 -1.39450133e-01 -1.15263319e+00
-3.19144577e-02 7.37467468e-01 2.01329261e-01 -9.36510973... | [12.907132148742676, -2.7212347984313965] |
b7547f5d-913b-40d9-89ab-c707609b9db9 | spottarget-rethinking-the-effect-of-target | 2306.00899 | null | https://arxiv.org/abs/2306.00899v1 | https://arxiv.org/pdf/2306.00899v1.pdf | SpotTarget: Rethinking the Effect of Target Edges for Link Prediction in Graph Neural Networks | Graph Neural Networks (GNNs) have demonstrated promising outcomes across various tasks, including node classification and link prediction. Despite their remarkable success in various high-impact applications, we have identified three common pitfalls in message passing for link prediction. Particularly, in prevalent GNN... | ['Danai Koutra', 'Xiang Song', 'Wei Ai', 'Shengyi Qian', 'Vassilis N. Ioannidis', 'YuHang Zhou', 'Jing Zhu'] | 2023-06-01 | null | null | null | null | ['link-prediction'] | ['graphs'] | [-4.68945429e-02 1.93064645e-01 -4.91304100e-01 -7.61284083e-02
-8.71475264e-02 -4.96659279e-01 1.95664778e-01 4.41751838e-01
9.36251208e-02 8.24482024e-01 -4.27837372e-01 -9.50237811e-01
-2.85704762e-01 -1.32108676e+00 -8.82595301e-01 -2.58029252e-01
-8.33291233e-01 4.64032024e-01 5.35918832e-01 -2.61185337... | [6.958458423614502, 6.074742794036865] |
1f57265e-4505-422a-b5d3-9b80509c0fe5 | domain-translation-with-conditional-gans-from | 1901.08101 | null | http://arxiv.org/abs/1901.08101v1 | http://arxiv.org/pdf/1901.08101v1.pdf | Domain Translation with Conditional GANs: from Depth to RGB Face-to-Face | Can faces acquired by low-cost depth sensors be useful to catch some
characteristic details of the face? Typically the answer is no. However, new
deep architectures can generate RGB images from data acquired in a different
modality, such as depth data. In this paper, we propose a new
\textit{Deterministic Conditional G... | ['Roberto Vezzani', 'Matteo Fabbri', 'Guido Borghi', 'Rita Cucchiara', 'Fabio Lanzi', 'Simone Calderara'] | 2019-01-23 | null | null | null | null | ['face-to-face-translation'] | ['computer-vision'] | [ 3.53445768e-01 5.22769451e-01 2.82361954e-01 -7.64614284e-01
-6.28772557e-01 -4.32207942e-01 6.05036914e-01 -4.98797208e-01
5.38805127e-03 9.24754560e-01 -2.18551293e-01 2.21289262e-01
5.94189577e-02 -9.57961738e-01 -8.94788802e-01 -9.88564372e-01
3.01372737e-01 8.47324550e-01 -1.67737785e-03 -2.10715428... | [12.811930656433105, -0.20958364009857178] |
8acb0a96-5df1-422b-aed1-0b6f1313c984 | weakly-supervised-caricature-face-parsing | 1905.05091 | null | https://arxiv.org/abs/1905.05091v1 | https://arxiv.org/pdf/1905.05091v1.pdf | Weakly-supervised Caricature Face Parsing through Domain Adaptation | A caricature is an artistic form of a person's picture in which certain striking characteristics are abstracted or exaggerated in order to create a humor or sarcasm effect. For numerous caricature related applications such as attribute recognition and caricature editing, face parsing is an essential pre-processing step... | ['Ming-Hsuan Yang', 'Yi-Hsuan Tsai', 'Wei-Chih Hung', 'Wenqing Chu', 'Deng Cai'] | 2019-05-13 | null | null | null | null | ['face-parsing', 'caricature'] | ['computer-vision', 'computer-vision'] | [ 4.91699874e-01 2.55278230e-01 -1.28825933e-01 -7.74722040e-01
-6.29191816e-01 -5.53316653e-01 4.02367979e-01 -6.94238722e-01
1.58506498e-01 5.46391845e-01 5.14419377e-02 1.32751456e-02
3.50572854e-01 -9.52400386e-01 -9.94644046e-01 -6.08610809e-01
5.88671327e-01 3.89234960e-01 -2.19294056e-01 -1.75672516... | [12.611674308776855, -0.13065862655639648] |
ff199b0c-9b37-4ed0-b368-335cdcec4cc3 | language-driven-semantic-segmentation-1 | 2201.03546 | null | https://arxiv.org/abs/2201.03546v2 | https://arxiv.org/pdf/2201.03546v2.pdf | Language-driven Semantic Segmentation | We present LSeg, a novel model for language-driven semantic image segmentation. LSeg uses a text encoder to compute embeddings of descriptive input labels (e.g., "grass" or "building") together with a transformer-based image encoder that computes dense per-pixel embeddings of the input image. The image encoder is train... | ['René Ranftl', 'Vladlen Koltun', 'Serge Belongie', 'Kilian Q. Weinberger', 'Boyi Li'] | 2022-01-10 | language-driven-semantic-segmentation | https://openreview.net/forum?id=RriDjddCLN | https://openreview.net/pdf?id=RriDjddCLN | iclr-2022-4 | ['zero-shot-segmentation'] | ['computer-vision'] | [ 2.96246827e-01 2.86037594e-01 -2.21588895e-01 -7.37653136e-01
-1.00191760e+00 -6.01418674e-01 4.01981771e-01 5.21656461e-02
-4.49325085e-01 2.86597133e-01 9.64053646e-02 5.66895492e-02
3.21243018e-01 -9.46152031e-01 -9.44544852e-01 -6.62488461e-01
2.88936794e-01 7.50856876e-01 3.42646718e-01 4.26555909... | [9.693638801574707, 0.8477755188941956] |
06167d3d-dfa5-4f70-8dec-8f41bcb98522 | ixa-cogcomp-at-semeval-2023-task-2-context | 2304.10637 | null | https://arxiv.org/abs/2304.10637v3 | https://arxiv.org/pdf/2304.10637v3.pdf | IXA/Cogcomp at SemEval-2023 Task 2: Context-enriched Multilingual Named Entity Recognition using Knowledge Bases | Named Entity Recognition (NER) is a core natural language processing task in which pre-trained language models have shown remarkable performance. However, standard benchmarks like CoNLL 2003 do not address many of the challenges that deployed NER systems face, such as having to classify emerging or complex entities in ... | ['Dan Roth', 'Ander Salaberria', 'Oscar Sainz', 'Jon Ander Campos', 'Iker García-Ferrero'] | 2023-04-20 | null | null | null | null | ['named-entity-recognition-ner', 'multilingual-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-4.11918104e-01 -1.59154624e-01 -1.33440465e-01 -4.06122208e-01
-1.02715087e+00 -9.80695069e-01 7.29405463e-01 5.27322412e-01
-1.05785298e+00 9.06223834e-01 6.79979205e-01 -1.72842801e-01
2.03789137e-02 -8.98282170e-01 -5.15828967e-01 1.15873724e-01
-1.52277872e-01 5.49932301e-01 1.40794545e-01 -2.48247460... | [9.657301902770996, 9.49191951751709] |
8d19f12d-9bdb-428a-9036-a98d2b660db1 | regularisation-can-mitigate-poisoning-attacks | 2003.00040 | null | https://arxiv.org/abs/2003.00040v2 | https://arxiv.org/pdf/2003.00040v2.pdf | Regularisation Can Mitigate Poisoning Attacks: A Novel Analysis Based on Multiobjective Bilevel Optimisation | Machine Learning (ML) algorithms are vulnerable to poisoning attacks, where a fraction of the training data is manipulated to deliberately degrade the algorithms' performance. Optimal poisoning attacks, which can be formulated as bilevel optimisation problems, help to assess the robustness of learning algorithms in wor... | ['Phillippa Spencer', 'Luis Muñoz-González', 'Javier Carnerero-Cano', 'Emil C. Lupu'] | 2020-02-28 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 2.03846410e-01 2.88104434e-02 -3.96567881e-02 2.59684324e-01
-4.74284798e-01 -8.54087174e-01 9.51363385e-01 2.85653681e-01
-8.05907428e-01 7.36949384e-01 -1.60328284e-01 -4.33281958e-01
-5.96931875e-01 -7.10867882e-01 -8.71246457e-01 -1.26643407e+00
-3.43727976e-01 2.77802825e-01 2.52014786e-01 -1.57329589... | [5.794511318206787, 7.540472507476807] |
a50231f4-010d-4f0d-92e5-d7aaa9246f35 | exploiting-programmatic-behavior-of-llms-dual | 2302.05733 | null | https://arxiv.org/abs/2302.05733v1 | https://arxiv.org/pdf/2302.05733v1.pdf | Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks | Recent advances in instruction-following large language models (LLMs) have led to dramatic improvements in a range of NLP tasks. Unfortunately, we find that the same improved capabilities amplify the dual-use risks for malicious purposes of these models. Dual-use is difficult to prevent as instruction-following capabil... | ['Tatsunori Hashimoto', 'Matei Zaharia', 'Carlos Guestrin', 'Ion Stoica', 'Xuechen Li', 'Daniel Kang'] | 2023-02-11 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [-1.79337993e-01 2.35985518e-01 -6.89441800e-01 4.96605262e-02
-1.06237996e+00 -1.16021276e+00 8.04524064e-01 2.00565234e-01
-3.26059699e-01 2.69158244e-01 3.46423328e-01 -1.33747661e+00
4.28372979e-01 -5.41918874e-01 -9.24984634e-01 -2.47013211e-01
-1.92757234e-01 -2.58635521e-01 1.70371741e-01 -3.63893270... | [6.109522342681885, 7.848875999450684] |
26d44fa7-ba79-43db-8c8f-b9f0688519b7 | sememnn-a-semantic-matrix-based-memory-neural | 2003.01857 | null | https://arxiv.org/abs/2003.01857v1 | https://arxiv.org/pdf/2003.01857v1.pdf | SeMemNN: A Semantic Matrix-Based Memory Neural Network for Text Classification | Text categorization is the task of assigning labels to documents written in a natural language, and it has numerous real-world applications including sentiment analysis as well as traditional topic assignment tasks. In this paper, we propose 5 different configurations for the semantic matrix-based memory neural network... | ['Hiroshi Ishiguro', 'Chaoran Liu', 'Changzeng Fu', 'Carlos Toshinori Ishi', 'Yuichiro Yoshikawa'] | 2020-03-04 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [-7.12106079e-02 -4.86886129e-03 -3.02150369e-01 -7.02021360e-01
-2.84745097e-01 -3.36842656e-01 8.85466695e-01 4.62889463e-01
-7.80849397e-01 6.25087440e-01 4.45761919e-01 -1.27291709e-01
-9.88630429e-02 -8.82809579e-01 -3.60787690e-01 -1.37623489e-01
4.15733568e-02 8.18833768e-01 6.65379584e-01 -1.37233332... | [10.415522575378418, 6.9566731452941895] |
cb1fa575-be3d-4a27-b2b3-bd267b901d98 | hpn-personalized-federated-hyperparameter | 2304.05195 | null | https://arxiv.org/abs/2304.05195v1 | https://arxiv.org/pdf/2304.05195v1.pdf | HPN: Personalized Federated Hyperparameter Optimization | Numerous research studies in the field of federated learning (FL) have attempted to use personalization to address the heterogeneity among clients, one of FL's most crucial and challenging problems. However, existing works predominantly focus on tailoring models. Yet, due to the heterogeneity of clients, they may each ... | ['Jian Cheng', 'Yaliang Li', 'Zhen Wang', 'Anda Cheng'] | 2023-04-11 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [ 1.58411320e-02 -5.17560206e-02 -2.94636458e-01 -5.27155638e-01
-1.04570282e+00 -5.57641923e-01 2.01585203e-01 -4.18130845e-01
-3.58273417e-01 8.34062397e-01 2.97232240e-01 -2.03784287e-01
-4.60675597e-01 -6.28192604e-01 -6.21573865e-01 -1.20464098e+00
1.29480332e-01 5.65656304e-01 -2.90326804e-01 1.90943018... | [5.849506855010986, 6.3966064453125] |
431b1130-c3f0-4fbe-bad3-69c3a999ccf1 | invertible-image-dataset-protection | 2112.14420 | null | https://arxiv.org/abs/2112.14420v1 | https://arxiv.org/pdf/2112.14420v1.pdf | Invertible Image Dataset Protection | Deep learning has achieved enormous success in various industrial applications. Companies do not want their valuable data to be stolen by malicious employees to train pirated models. Nor do they wish the data analyzed by the competitors after using them online. We propose a novel solution for dataset protection in this... | ['Xinpeng Zhang', 'Zhenxing Qian', 'Sheng Li', 'Qichao Ying', 'Xianhan Zeng', 'Kejiang Chen'] | 2021-12-29 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 5.55816352e-01 4.32672888e-01 5.96326543e-03 -3.76509845e-01
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3.61982584e-01 3.43923382e-02 3.44104096e-02 -2.80350029... | [5.612184047698975, 7.984772682189941] |
cd550104-530e-4398-bf3d-4b4d8fcc4f3e | a-simple-method-for-detecting-chaos-in-nature | 1904.00986 | null | http://arxiv.org/abs/1904.00986v3 | http://arxiv.org/pdf/1904.00986v3.pdf | A simple method for detecting chaos in nature | Chaos, or exponential sensitivity to small perturbations, appears everywhere
in nature. Moreover, chaos is predicted to play diverse functional roles in
living systems. A method for detecting chaos from empirical measurements should
therefore be a key component of the biologist's toolkit. But, classic
chaos-detection t... | [] | 2020-01-10 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [-1.19798128e-02 -5.35905480e-01 5.46526074e-01 1.00334540e-01
-1.01915061e-01 -9.15295124e-01 5.32659888e-01 5.18213511e-01
-2.31444657e-01 8.65617096e-01 -6.56320900e-02 -3.28178108e-01
4.18020934e-02 -4.92738575e-01 -3.46551806e-01 -9.02316988e-01
-4.63899195e-01 2.88508505e-01 4.39676017e-01 -2.12746978... | [6.317785739898682, 4.313699245452881] |
b02550e0-e2dd-406f-b92c-7b6269350838 | moving-object-detection-in-video-using | 1509.09089 | null | http://arxiv.org/abs/1509.09089v1 | http://arxiv.org/pdf/1509.09089v1.pdf | Moving Object Detection in Video Using Saliency Map and Subspace Learning | Moving object detection is a key to intelligent video analysis. On the one
hand, what moves is not only interesting objects but also noise and cluttered
background. On the other hand, moving objects without rich texture are prone
not to be detected. So there are undesirable false alarms and missed alarms in
many algori... | ['Xuelong. Li', 'Li Ye', 'Jing Pan', 'Yanwei Pang'] | 2015-09-30 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 4.56892669e-01 -2.95687884e-01 -1.64915714e-02 2.93421466e-03
-2.66490966e-01 -1.48064807e-01 1.21530317e-01 -6.20328309e-03
-1.02130413e-01 7.64803946e-01 1.82634592e-01 2.12394431e-01
-1.40103638e-01 -5.65862894e-01 -3.92031312e-01 -8.52099001e-01
-1.74995810e-01 -2.03908443e-01 8.75135958e-01 1.22318335... | [9.207443237304688, -0.6316173672676086] |
975e4fac-0c52-4103-8ba0-141af2e3b280 | pore-provably-robust-recommender-systems | 2303.14601 | null | https://arxiv.org/abs/2303.14601v1 | https://arxiv.org/pdf/2303.14601v1.pdf | PORE: Provably Robust Recommender Systems against Data Poisoning Attacks | Data poisoning attacks spoof a recommender system to make arbitrary, attacker-desired recommendations via injecting fake users with carefully crafted rating scores into the recommender system. We envision a cat-and-mouse game for such data poisoning attacks and their defenses, i.e., new defenses are designed to defend ... | ['Neil Zhenqiang Gong', 'Yuepeng Hu', 'Yupei Liu', 'Jinyuan Jia'] | 2023-03-26 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-2.30068058e-01 -1.41567066e-01 -1.25225559e-01 3.93480062e-02
-5.02613485e-01 -1.41667557e+00 2.15408191e-01 -1.71120778e-01
-3.59834254e-01 2.46733040e-01 -9.03524160e-02 -7.60323763e-01
-2.19880804e-01 -1.36021090e+00 -7.96311021e-01 -7.36186564e-01
-1.33214250e-01 4.21709180e-01 4.25711691e-01 -6.26581430... | [5.81056547164917, 7.342576026916504] |
43e879ec-1366-4f94-a9d2-eb1cb2afefe3 | replica-enhanced-feature-pyramid-network-by | 2111.11546 | null | https://arxiv.org/abs/2111.11546v3 | https://arxiv.org/pdf/2111.11546v3.pdf | Lightweight Transformer Backbone for Medical Object Detection | Lesion detection in digital breast tomosynthesis (DBT) is an important and a challenging problem characterized by a low prevalence of images containing tumors. Due to the label scarcity problem, large deep learning models and computationally intensive algorithms are likely to fail when applied to this task. In this pap... | ['Nicholas Konz', 'Maciej A. Mazurowski', 'Hanxue Gu', 'Haoyu Dong', 'Yifan Zhang'] | 2021-11-22 | null | null | null | null | ['medical-object-detection'] | ['computer-vision'] | [ 4.08387870e-01 4.52205837e-01 -4.22495395e-01 1.12848356e-01
-1.10393083e+00 -1.26208544e-01 2.06190005e-01 1.82990715e-01
-1.14138220e-02 2.41080537e-01 -7.28895664e-02 -5.79009473e-01
3.11884910e-01 -6.51547492e-01 -7.59927332e-01 -7.06787348e-01
2.19796211e-01 3.52555782e-01 6.92077637e-01 -5.64575978... | [15.191291809082031, -2.4718027114868164] |
79ecd76e-c5fe-4e2c-bbb0-6d5a83422212 | accelerating-code-search-with-deep-hashing | 2203.15287 | null | https://arxiv.org/abs/2203.15287v2 | https://arxiv.org/pdf/2203.15287v2.pdf | Accelerating Code Search with Deep Hashing and Code Classification | Code search is to search reusable code snippets from source code corpus based on natural languages queries. Deep learning-based methods of code search have shown promising results. However, previous methods focus on retrieval accuracy but lacked attention to the efficiency of the retrieval process. We propose a novel m... | ['Michael R. Lyu', 'Dongmei Zhang', 'Shi Han', 'Hongyu Zhang', 'Lun Du', 'Yanlin Wang', 'Wenchao Gu'] | 2022-03-29 | null | https://aclanthology.org/2022.acl-long.181 | https://aclanthology.org/2022.acl-long.181.pdf | acl-2022-5 | ['code-classification', 'code-search', 'code-search'] | ['computer-code', 'computer-code', 'computer-vision'] | [-5.92060447e-01 -6.41181052e-01 -5.46336949e-01 -2.73644123e-02
-1.15836406e+00 -5.26566327e-01 3.72937083e-01 5.31680942e-01
-4.62160140e-01 4.06605899e-02 1.89488288e-02 -6.31645441e-01
-9.47866887e-02 -7.70601273e-01 -4.75195736e-01 -1.38335973e-01
-1.90258726e-01 -8.09867866e-03 4.03964996e-01 -4.40589078... | [7.49930477142334, 8.083356857299805] |
fdd8aad1-0f7e-4eee-a765-1ddf62424856 | learning-to-refine-human-pose-estimation | 1804.07909 | null | http://arxiv.org/abs/1804.07909v1 | http://arxiv.org/pdf/1804.07909v1.pdf | Learning to Refine Human Pose Estimation | Multi-person pose estimation in images and videos is an important yet
challenging task with many applications. Despite the large improvements in
human pose estimation enabled by the development of convolutional neural
networks, there still exist a lot of difficult cases where even the
state-of-the-art models fail to co... | ['Anna Khoreva', 'Mihai Fieraru', 'Leonid Pishchulin', 'Bernt Schiele'] | 2018-04-21 | null | null | null | null | ['multi-person-pose-estimation-and-tracking'] | ['computer-vision'] | [ 1.24732383e-01 3.31658840e-01 5.83120249e-02 -3.47707868e-01
-7.89665043e-01 -2.73815751e-01 4.68161970e-01 -1.18727818e-01
-5.98555326e-01 7.73973167e-01 3.60114902e-01 5.08811414e-01
1.35114580e-01 -3.33022594e-01 -9.83445287e-01 -3.05177033e-01
-2.62497761e-03 1.04287326e+00 5.26516795e-01 -3.94558072... | [7.025851249694824, -0.8275352120399475] |
5fe4bf47-6cd5-4bf0-bdc7-d329f4fa53c6 | xtab-cross-table-pretraining-for-tabular | 2305.06090 | null | https://arxiv.org/abs/2305.06090v1 | https://arxiv.org/pdf/2305.06090v1.pdf | XTab: Cross-table Pretraining for Tabular Transformers | The success of self-supervised learning in computer vision and natural language processing has motivated pretraining methods on tabular data. However, most existing tabular self-supervised learning models fail to leverage information across multiple data tables and cannot generalize to new tables. In this work, we intr... | ['Mahsa Shoaran', 'George Karypis', 'Mu Li', 'Nick Erickson', 'Xingjian Shi', 'Bingzhao Zhu'] | 2023-05-10 | null | null | null | null | ['automl'] | ['methodology'] | [-1.07875086e-01 1.94447055e-01 -7.10223556e-01 -8.62524569e-01
-1.10719919e+00 -8.12047601e-01 4.05008048e-01 5.05202353e-01
4.36360657e-01 1.17065573e+00 7.16752782e-02 -6.60009623e-01
-3.71136032e-02 -8.94155324e-01 -1.33126247e+00 -2.61826277e-01
5.99112324e-02 1.24891889e+00 -1.47091672e-01 -1.23976022... | [9.641417503356934, 7.828150749206543] |
aad2a89b-33dc-4383-9501-849a7b8bc022 | g-rank-unsupervised-continuous-learn-to-rank | 2301.12530 | null | https://arxiv.org/abs/2301.12530v1 | https://arxiv.org/pdf/2301.12530v1.pdf | G-Rank: Unsupervised Continuous Learn-to-Rank for Edge Devices in a P2P Network | Ranking algorithms in traditional search engines are powered by enormous training data sets that are meticulously engineered and curated by a centralized entity. Decentralized peer-to-peer (p2p) networks such as torrenting applications and Web3 protocols deliberately eschew centralized databases and computational archi... | ['Johan Pouwelse', 'Andrew Gold'] | 2023-01-29 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-3.10869604e-01 6.77335486e-02 -7.97255039e-01 -4.47952718e-01
-6.73244953e-01 -8.13341677e-01 6.21426404e-01 1.24222413e-01
-1.28870070e-01 7.06017911e-01 1.60125718e-02 -4.99357462e-01
-8.77447844e-01 -1.03038979e+00 -2.95112133e-01 -4.64684516e-01
-6.31576300e-01 1.23846292e+00 6.36548698e-01 -2.00625405... | [6.042801380157471, 6.202227592468262] |
ea8ab3b3-3816-4fe0-8b75-954324663368 | analysis-of-the-influence-of-final-resolution | 2307.00388 | null | https://arxiv.org/abs/2307.00388v1 | https://arxiv.org/pdf/2307.00388v1.pdf | Analysis of the influence of final resolution on ADC accuracy | This work is devoted to the study of the influence of quantization noise on the spectral characteristics of a digital signal and the assessment of spectrum measurement errors that arise due to the quantization noise of an analog-to-digital converter. To achieve more accurate and reliable measurements of the spectrum, a... | ['Anzhelika Stakhova'] | 2023-07-01 | null | null | null | null | ['quantization'] | ['methodology'] | [ 5.85587144e-01 -5.62998474e-01 1.49179429e-01 -9.82672814e-03
-5.21902621e-01 -1.10467657e-01 1.27450258e-01 6.76625371e-01
-3.02724242e-01 3.74400944e-01 1.07495993e-01 -1.79066375e-01
-2.37246841e-01 -9.93228555e-01 -2.81275958e-01 -4.90740836e-01
8.14496428e-02 7.63312355e-02 3.31576109e-01 1.18648447... | [15.05114459991455, 5.64572811126709] |
2208ab8c-282e-42d0-b467-a18221121ac5 | multi-agent-embodied-question-answering-in | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2013_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580647.pdf | Multi-Agent Embodied Question Answering in Interactive Environments | We investigate a new AI task --- Multi-Agent Interactive Question Answering --- where several agents explore the scene jointly in interactive environments to answer a question. To cooperate efficiently and answer accurately, agents must be well-organized to have balanced work division and share knowledge about the obje... | ['Huaping Liu', 'Di Guo', 'Weilai Xiang', 'Sinan Tan', 'Fuchun Sun'] | null | null | null | null | eccv-2020-8 | ['embodied-question-answering'] | ['computer-vision'] | [-3.11628759e-01 3.95111620e-01 3.46750289e-01 -5.32482028e-01
-8.45475793e-01 -7.39721000e-01 5.49160779e-01 4.34242368e-01
-4.55301642e-01 5.40723741e-01 2.83720791e-01 7.51992166e-02
-2.06766173e-01 -1.06847739e+00 -9.65026259e-01 -1.85309246e-01
-2.13011086e-01 1.67590964e+00 7.86357403e-01 -3.99596751... | [4.3974151611328125, 0.5948408842086792] |
5c9602d0-8020-40aa-b27d-2a57033159e2 | on-high-dimensional-poisson-models-with | 2301.00139 | null | https://arxiv.org/abs/2301.00139v1 | https://arxiv.org/pdf/2301.00139v1.pdf | On High dimensional Poisson models with measurement error: hypothesis testing for nonlinear nonconvex optimization | We study estimation and testing in the Poisson regression model with noisy high dimensional covariates, which has wide applications in analyzing noisy big data. Correcting for the estimation bias due to the covariate noise leads to a non-convex target function to minimize. Treating the high dimensional issue further le... | ['Yanyuan Ma', 'Jianxuan Liu', 'Yeqing Zhou', 'Fei Jiang'] | 2022-12-31 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 7.62296394e-02 8.84072036e-02 -2.49551814e-02 -5.98396897e-01
-1.04017651e+00 -7.83883035e-02 -5.14889583e-02 7.35785440e-02
-6.40213609e-01 1.18223989e+00 6.09535053e-02 -9.49112102e-02
-3.83833140e-01 -6.55968964e-01 -6.84272349e-01 -9.14718568e-01
-2.42044687e-01 4.93393898e-01 -2.04923004e-01 4.00182217... | [7.591695308685303, 4.6826491355896] |
246bf292-f92c-4b27-bbfe-8ade9fb0a523 | fast-online-video-super-resolution-with | 2202.01731 | null | https://arxiv.org/abs/2202.01731v2 | https://arxiv.org/pdf/2202.01731v2.pdf | Fast Online Video Super-Resolution with Deformable Attention Pyramid | Video super-resolution (VSR) has many applications that pose strict causal, real-time, and latency constraints, including video streaming and TV. We address the VSR problem under these settings, which poses additional important challenges since information from future frames is unavailable. Importantly, designing effic... | ['Luc van Gool', 'Radu Timofte', 'Martin Danelljan', 'Dario Fuoli'] | 2022-02-03 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 2.94149965e-01 -3.20014656e-01 -1.94591835e-01 -1.10533334e-01
-8.08015883e-01 -1.87920436e-01 2.29198769e-01 -2.63621420e-01
-3.30385149e-01 5.93346238e-01 3.17387253e-01 -3.07212770e-02
2.53467690e-02 -5.99856913e-01 -5.78522742e-01 -4.13315684e-01
1.42634306e-02 -6.54950365e-02 8.57567310e-01 -4.31896538... | [11.082282066345215, -1.8759123086929321] |
23415d94-c8a3-44b2-8e6e-423781e5e2da | shrimp-sparser-random-feature-models-via | 2112.04002 | null | https://arxiv.org/abs/2112.04002v1 | https://arxiv.org/pdf/2112.04002v1.pdf | SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning | Sparse shrunk additive models and sparse random feature models have been developed separately as methods to learn low-order functions, where there are few interactions between variables, but neither offers computational efficiency. On the other hand, $\ell_2$-based shrunk additive models are efficient but do not offer ... | ['Rachel Ward', 'Hayden Schaeffer', 'Bobby Shi', 'Yuege Xie'] | 2021-12-07 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 2.02307656e-01 9.57551673e-02 -1.49874553e-01 -2.12770551e-01
-3.74005973e-01 -1.30402416e-01 8.59703869e-02 -2.89418370e-01
-9.65079442e-02 8.45625281e-01 -9.28631425e-02 1.36164635e-01
-9.09581244e-01 -7.42114961e-01 -9.66057718e-01 -9.91154850e-01
-6.05818450e-01 3.24773997e-01 -8.06666911e-02 -1.45852745... | [8.264572143554688, 3.739588737487793] |
58597401-6466-47ce-a031-015406caab72 | cross-domain-few-shot-segmentation-with | 2211.14745 | null | https://arxiv.org/abs/2211.14745v1 | https://arxiv.org/pdf/2211.14745v1.pdf | Cross-domain Few-shot Segmentation with Transductive Fine-tuning | Few-shot segmentation (FSS) expects models trained on base classes to work on novel classes with the help of a few support images. However, when there exists a domain gap between the base and novel classes, the state-of-the-art FSS methods may even fail to segment simple objects. To improve their performance on unseen ... | ['Song Wang', 'Zhenyao Wu', 'Xinyi Wu', 'Yuhang Lu'] | 2022-11-27 | null | null | null | null | ['cross-domain-few-shot'] | ['computer-vision'] | [ 5.90269089e-01 3.96097898e-01 -3.57480705e-01 -8.67683113e-01
-9.93521094e-01 -5.58828056e-01 4.61996436e-01 -2.51411181e-02
-2.92783082e-01 6.13766789e-01 -3.06417197e-01 1.76707417e-01
-1.08513877e-01 -8.19716752e-01 -7.95648515e-01 -6.62407100e-01
3.55778635e-01 6.85188711e-01 6.87325895e-01 -2.23698840... | [9.638983726501465, 1.4004461765289307] |
8e70cfb8-37d2-45d3-b558-286efe48302a | through-wall-human-pose-estimation-using | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhao_Through-Wall_Human_Pose_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhao_Through-Wall_Human_Pose_CVPR_2018_paper.pdf | Through-Wall Human Pose Estimation Using Radio Signals | This paper demonstrates accurate human pose estimation through walls and occlusions. We leverage the fact that wireless signals in the WiFi frequencies traverse walls and reflect off the human body. We introduce a deep neural network approach that parses such radio signals to estimate 2D poses. Since humans cannot anno... | ['Ming-Min Zhao', 'Tianhong Li', 'Hang Zhao', 'Antonio Torralba', 'Dina Katabi', 'Yonglong Tian', 'Mohammad Abu Alsheikh'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['rf-based-pose-estimation'] | ['computer-vision'] | [ 1.58222064e-01 2.91386694e-01 -7.31001571e-02 -3.07611138e-01
-7.93807387e-01 -6.53574944e-01 7.04394802e-02 -3.31988245e-01
-5.31474829e-01 4.84765381e-01 1.86346292e-01 -1.24745257e-01
3.07650894e-01 -6.48998022e-01 -1.18188500e+00 -1.33810148e-01
-2.77141899e-01 4.95906353e-01 -1.28682390e-01 -5.34407310... | [6.839484214782715, 0.26518136262893677] |
5fee27fc-1276-4b20-8cc2-ed416173a080 | interpretable-reinforcement-learning-via-1 | 2303.10382 | null | https://arxiv.org/abs/2303.10382v2 | https://arxiv.org/pdf/2303.10382v2.pdf | Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management | The COVID-19 pandemic has highlighted the importance of supply chains and the role of digital management to react to dynamic changes in the environment. In this work, we focus on developing dynamic inventory ordering policies for a multi-echelon, i.e. multi-stage, supply chain. Traditional inventory optimization method... | ['Johannes S. Otterbach', 'Sebastian Schulze', 'Maximilian Schambach', 'Julien Siems'] | 2023-03-18 | null | null | null | null | ['additive-models'] | ['methodology'] | [-1.43270582e-01 1.28799781e-01 -1.31177127e-01 -1.23016220e-02
-8.32914375e-03 -9.51839805e-01 3.88379186e-01 3.81454647e-01
-3.09920937e-01 8.28520179e-01 2.84244239e-01 -4.97237921e-01
-6.11150146e-01 -8.36799204e-01 -8.94774079e-01 -5.92304409e-01
-3.27360779e-01 1.18978500e+00 -1.71606302e-01 -6.44287765... | [4.3895697593688965, 2.429342031478882] |
5e8a3482-bd9f-4da4-97bf-1ce3460cf0da | proofver-natural-logic-theorem-proving-for | 2108.11357 | null | https://arxiv.org/abs/2108.11357v2 | https://arxiv.org/pdf/2108.11357v2.pdf | ProoFVer: Natural Logic Theorem Proving for Fact Verification | Fact verification systems typically rely on neural network classifiers for veracity prediction which lack explainability. This paper proposes ProoFVer, which uses a seq2seq model to generate natural logic-based inferences as proofs. These proofs consist of lexical mutations between spans in the claim and the evidence r... | ['Andreas Vlachos', 'Sebastian Riedel', 'Amrith Krishna'] | 2021-08-25 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 6.54959157e-02 9.05026615e-01 -8.52834404e-01 -2.22336166e-02
-8.12643826e-01 -6.00580692e-01 9.23755348e-01 1.98605210e-01
2.35393152e-01 1.25273538e+00 5.37146986e-01 -9.54119503e-01
-2.21732557e-01 -7.00925708e-01 -1.11256695e+00 -2.74171941e-02
1.14001654e-01 5.13212323e-01 -1.27798855e-01 -1.39795765... | [9.852505683898926, 7.818010330200195] |
4c12b986-3bf7-4235-928c-ab07aee9840d | a-study-on-the-invariance-in-security | 2302.11527 | null | https://arxiv.org/abs/2302.11527v1 | https://arxiv.org/pdf/2302.11527v1.pdf | A study on the invariance in security whatever the dimension of images for the steganalysis by deep-learning | In this paper, we study the performance invariance of convolutional neural networks when confronted with variable image sizes in the context of a more "wild steganalysis". First, we propose two algorithms and definitions for a fine experimental protocol with datasets owning "similar difficulty" and "similar security". ... | ['Frédéric Comby', 'Marc Chaumont', 'Kévin Planolles'] | 2023-02-22 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 4.58962083e-01 -6.25283942e-02 2.90645808e-01 -1.09378599e-01
1.79095700e-01 -5.79779088e-01 7.52259433e-01 -1.71445414e-01
-8.04343641e-01 5.20947397e-01 -2.99849778e-01 -5.94206631e-01
-7.04497322e-02 -9.96298552e-01 -8.84690523e-01 -8.42060626e-01
-2.64653653e-01 -1.95436433e-01 3.69350612e-01 -6.52065039... | [4.400973320007324, 8.0281400680542] |
17d111f5-c35e-4545-8795-a9563b852e2f | naiverole-author-contribution-extraction-and | 1912.10170 | null | https://arxiv.org/abs/1912.10170v1 | https://arxiv.org/pdf/1912.10170v1.pdf | NaïveRole: Author-Contribution Extraction and Parsing from Biomedical Manuscripts | Information about the contributions of individual authors to scientific publications is important for assessing authors' achievements. Some biomedical publications have a short section that describes authors' roles and contributions. It is usually written in natural language and hence author contributions cannot be tri... | ['Dominika Tkaczyk', 'Joeran Beel', 'Andrew Collins'] | 2019-12-15 | null | null | null | null | ['open-information-extraction'] | ['natural-language-processing'] | [ 2.52792567e-01 4.73829329e-01 -3.94787997e-01 5.38042709e-02
-8.49145651e-01 -7.80909359e-01 5.56474984e-01 8.85879815e-01
-5.18676758e-01 1.33127594e+00 5.97946942e-01 -3.35861564e-01
-4.10136223e-01 -5.15385330e-01 -6.19780600e-01 -5.84951282e-01
1.58822790e-01 7.48371780e-01 1.52697876e-01 1.83536336... | [8.804450035095215, 8.619308471679688] |
f60b2aa6-a50e-4a80-a569-806be714a675 | a-large-dataset-for-improving-patch-matching | 1801.01466 | null | http://arxiv.org/abs/1801.01466v3 | http://arxiv.org/pdf/1801.01466v3.pdf | A Large Dataset for Improving Patch Matching | We propose a new dataset for learning local image descriptors which can be
used for significantly improved patch matching. Our proposed dataset consists
of an order of magnitude more number of scenes, images, and positive and
negative correspondences compared to the currently available Multi-View Stereo
(MVS) dataset f... | ['Sharat Chandran', 'Rahul Mitra', 'Arjun Jain', 'Utkarsh Gautam', 'Shuaib Ahmed', 'Sanath Narayan', 'Nehal Doiphode'] | 2018-01-04 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 1.07658960e-01 -5.81799805e-01 -1.03630058e-01 -5.25499642e-01
-1.09147811e+00 -5.90351403e-01 6.97379529e-01 8.54404643e-02
-2.10561112e-01 2.46002063e-01 1.05281048e-01 3.86673152e-01
-9.58804488e-02 -7.50067949e-01 -9.10960019e-01 -7.73380756e-01
1.27908438e-01 3.48314643e-01 5.30266941e-01 -4.71376121... | [8.13115406036377, -2.0152735710144043] |
480f05a0-fdd6-4ab9-8ed0-255ceb86559d | head-pose-estimation-of-occluded-faces-using | 1602.00997 | null | http://arxiv.org/abs/1602.00997v1 | http://arxiv.org/pdf/1602.00997v1.pdf | Head Pose Estimation of Occluded Faces using Regularized Regression | This paper presents regression methods for estimation of head pose from
occluded 2-D face images. The process primarily involves reconstructing a face
from its occluded image, followed by classification. Typical methods for
reconstruction assume that the pixel errors of the occluded regions are
independent. However, su... | ['Rishabh Bindal', 'Michael Rotkowitz', 'Soumya Indela', 'Amit Kumar'] | 2016-02-02 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-4.92982604e-02 1.61487967e-01 -1.51342511e-01 -6.69849098e-01
-8.12753081e-01 -4.27981187e-03 4.71274592e-02 -3.52041900e-01
-2.39274800e-01 9.26542222e-01 4.16034162e-01 1.69163689e-01
3.92375961e-02 -4.33718503e-01 -7.71735966e-01 -9.27745759e-01
2.72590593e-02 3.15075785e-01 -5.56652606e-01 2.65182555... | [13.219351768493652, 0.3282988965511322] |
6397298f-aefd-4f9c-bb42-1a36f5146016 | benchmark-for-license-plate-character | 1607.02937 | null | http://arxiv.org/abs/1607.02937v2 | http://arxiv.org/pdf/1607.02937v2.pdf | Benchmark for License Plate Character Segmentation | Automatic License Plate Recognition (ALPR) has been the focus of many
researches in the past years. In general, ALPR is divided into the following
problems: detection of on-track vehicles, license plates detection, segmention
of license plate characters and optical character recognition (OCR). Even
though commercial so... | ['William Robson Schwartz', 'Gabriel Resende Gonçalves', 'David Menotti', 'Sirlene Pio Gomes da Silva'] | 2016-07-11 | null | null | null | null | ['license-plate-recognition', 'license-plate-detection'] | ['computer-vision', 'computer-vision'] | [ 1.12612717e-01 -5.18354177e-01 1.16712764e-01 -1.11193389e-01
-8.03096950e-01 -8.44686031e-01 4.20410901e-01 1.21624790e-01
-7.10375428e-01 5.66258609e-01 -5.83738744e-01 -2.19232008e-01
-6.25233948e-02 -7.84744620e-01 -7.09421694e-01 -5.35874426e-01
3.57581258e-01 7.42075980e-01 5.85231364e-01 -8.24989229... | [9.81925106048584, -4.9673614501953125] |
b9cbfb5c-ccdd-4dea-b3a5-13f19b32ddaa | view-consistency-aware-holistic-triangulation | 2302.11301 | null | https://arxiv.org/abs/2302.11301v2 | https://arxiv.org/pdf/2302.11301v2.pdf | View Consistency Aware Holistic Triangulation for 3D Human Pose Estimation | The rapid development of multi-view 3D human pose estimation (HPE) is attributed to the maturation of monocular 2D HPE and the geometry of 3D reconstruction. However, 2D detection outliers in occluded views due to neglect of view consistency, and 3D implausible poses due to lack of pose coherence, remain challenges. To... | ['Xu Zhao', 'Zhuo Chen', 'Xiaoyue Wan'] | 2023-02-22 | null | null | null | null | ['3d-human-pose-estimation', 'anatomy'] | ['computer-vision', 'miscellaneous'] | [-1.81414187e-01 2.37571850e-01 -3.35130394e-02 -2.30038762e-01
-7.14093864e-01 -4.76337314e-01 3.21756303e-01 -4.13343340e-01
-4.62390818e-02 4.51718658e-01 5.76351643e-01 3.68369907e-01
-1.90014765e-01 -4.36840534e-01 -6.04291141e-01 -2.99442440e-01
1.46141142e-01 4.11327988e-01 2.61708975e-01 -1.57437995... | [6.9997878074646, -0.9658544063568115] |
62e3a2e8-c69e-40d1-b55e-3a2f7c551c2e | parting-with-misconceptions-about-learning | 2306.07962 | null | https://arxiv.org/abs/2306.07962v1 | https://arxiv.org/pdf/2306.07962v1.pdf | Parting with Misconceptions about Learning-based Vehicle Motion Planning | The release of nuPlan marks a new era in vehicle motion planning research, offering the first large-scale real-world dataset and evaluation schemes requiring both precise short-term planning and long-horizon ego-forecasting. Existing systems struggle to simultaneously meet both requirements. Indeed, we find that these ... | ['Kashyap Chitta', 'Andreas Geiger', 'Marcel Hallgarten', 'Daniel Dauner'] | 2023-06-13 | null | null | null | null | ['misconceptions', 'motion-planning'] | ['miscellaneous', 'robots'] | [-4.25773486e-02 4.60632682e-01 -4.92788136e-01 -2.50029266e-01
-7.53974140e-01 -7.80780911e-01 1.16024327e+00 5.41358665e-02
-4.83151615e-01 8.80468786e-01 6.01025462e-01 -6.02855504e-01
-1.44862220e-01 -7.28839517e-01 -6.58462346e-01 -4.87811774e-01
-5.43954968e-01 9.99148846e-01 6.72023773e-01 -5.88859200... | [5.638774871826172, 0.8445616364479065] |
368b51bb-63cd-44b9-a388-b42cb481b7c9 | chemformer-a-pre-trained-transformer-for | null | null | https://iopscience.iop.org/article/10.1088/2632-2153/ac3ffb | https://iopscience.iop.org/article/10.1088/2632-2153/ac3ffb/pdf | Chemformer: a pre-trained transformer for computational chemistry | Transformer models coupled with a simplified molecular line entry system (SMILES) have recently proven to be a powerful combination for solving challenges in cheminformatics. These models, however, are often developed specifically for a single application and can be very resource-intensive to train. In this work we pre... | ['Esben Jannik Bjerrum', 'Jiazhen He', 'Spyridon Dimitriadis', 'Ross Irwin'] | 2022-01-31 | null | null | null | machine-learning-science-and-technology-2022 | ['retrosynthesis'] | ['medical'] | [ 6.76618814e-01 -2.81309575e-01 -3.28656495e-01 -3.94241780e-01
-1.09636056e+00 -1.11762059e+00 6.93242371e-01 6.05043709e-01
-2.96805888e-01 1.19076991e+00 -2.19842270e-01 -6.73142433e-01
-1.60804853e-01 -1.43340498e-01 -9.46021795e-01 -1.01115453e+00
-1.03673518e-01 8.29896748e-01 1.52144179e-01 -4.72265661... | [4.694423198699951, 5.921141624450684] |
c6c10c0a-b070-45d4-b09c-63149ec51ca8 | sparse-depth-completion-with-semantic-mesh | 2112.05498 | null | https://arxiv.org/abs/2112.05498v1 | https://arxiv.org/pdf/2112.05498v1.pdf | Sparse Depth Completion with Semantic Mesh Deformation Optimization | Sparse depth measurements are widely available in many applications such as augmented reality, visual inertial odometry and robots equipped with low cost depth sensors. Although such sparse depth samples work well for certain applications like motion tracking, a complete depth map is usually preferred for broader appli... | ['Sinem Guven', 'Matias Aiskovich', 'Bing Zhou'] | 2021-12-10 | null | null | null | null | ['3d-object-recognition', 'depth-completion'] | ['computer-vision', 'computer-vision'] | [ 2.41611466e-01 4.44397964e-02 -4.83699054e-01 -6.03383124e-01
-5.40979564e-01 1.17849052e-01 2.51751333e-01 -1.17253512e-01
-4.58948404e-01 5.59441507e-01 1.28153875e-01 -2.07315966e-01
8.92401487e-02 -1.05271316e+00 -1.04747510e+00 -2.62943149e-01
-1.32700419e-02 5.82302034e-01 2.37428948e-01 -2.10918307... | [8.520731925964355, -2.520075559616089] |
e38244e2-0b76-46f6-a746-bda43c0c5853 | deduction-under-perturbed-evidence-probing | 2305.14507 | null | https://arxiv.org/abs/2305.14507v1 | https://arxiv.org/pdf/2305.14507v1.pdf | Deduction under Perturbed Evidence: Probing Student Simulation Capabilities of Large Language Models | We explore whether Large Language Models (LLMs) are capable of logical reasoning with distorted facts, which we call Deduction under Perturbed Evidence (DUPE). DUPE presents a unique challenge to LLMs since they typically rely on their parameters, which encode mostly accurate information, to reason and make inferences.... | ['Richard G. Baraniuk', 'Shashank Sonkar'] | 2023-05-23 | null | null | null | null | ['logical-reasoning', 'strategyqa'] | ['reasoning', 'reasoning'] | [-1.74343754e-02 6.16609871e-01 4.04378958e-02 -9.88329053e-02
-9.18081343e-01 -9.59543049e-01 4.14791882e-01 4.61202592e-01
-2.43375093e-01 9.23086286e-01 9.64360684e-02 -1.04087698e+00
-3.87080818e-01 -1.03979659e+00 -1.13183105e+00 -4.12306227e-02
3.08112621e-01 5.46344995e-01 4.33544189e-01 -4.67765093... | [9.501642227172852, 7.31986665725708] |
029822de-e264-4809-bb3b-167defc880e7 | posecnn-a-convolutional-neural-network-for-6d | 1711.00199 | null | http://arxiv.org/abs/1711.00199v3 | http://arxiv.org/pdf/1711.00199v3.pdf | PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes | Estimating the 6D pose of known objects is important for robots to interact
with the real world. The problem is challenging due to the variety of objects
as well as the complexity of a scene caused by clutter and occlusions between
objects. In this work, we introduce PoseCNN, a new Convolutional Neural Network
for 6D o... | ['Yu Xiang', 'Venkatraman Narayanan', 'Dieter Fox', 'Tanner Schmidt'] | 2017-11-01 | null | null | null | null | ['6d-pose-estimation-using-rgbd'] | ['computer-vision'] | [-2.04823360e-01 -4.89761084e-01 -8.98576453e-02 -3.82364213e-01
-5.82840800e-01 -6.99521005e-01 2.22253859e-01 -3.21733654e-01
-5.02132595e-01 2.52275020e-01 -9.34173465e-02 1.30159527e-01
2.48320714e-01 -3.22840154e-01 -1.30881572e+00 -4.62846488e-01
-4.04502004e-01 8.76847029e-01 3.43192488e-01 1.21129053... | [7.314334869384766, -2.472534656524658] |
7bc52841-e5ab-409b-bdf2-914dc66439d9 | batgpt-a-bidirectional-autoregessive-talker | 2307.00360 | null | https://arxiv.org/abs/2307.00360v1 | https://arxiv.org/pdf/2307.00360v1.pdf | BatGPT: A Bidirectional Autoregessive Talker from Generative Pre-trained Transformer | BatGPT is a large-scale language model designed and trained jointly by Wuhan University and Shanghai Jiao Tong University. It is capable of generating highly natural and fluent text in response to various types of input, including text prompts, images, and audio. In the modeling level, we employ a bidirectional autoreg... | ['Dongjie Yang', 'Yifei Yang', 'Hai Zhao', 'Shitou Zhang', 'Zuchao Li'] | 2023-07-01 | null | null | null | null | ['text-generation', 'question-answering'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.54217882e-02 -3.80133055e-02 5.86788077e-03 -2.54168481e-01
-5.48860252e-01 -2.45317072e-01 6.39976740e-01 -1.23476535e-01
-1.65992349e-01 3.84775579e-01 5.96143603e-01 -4.11415011e-01
4.21307296e-01 -8.89027119e-01 -5.59086919e-01 -3.42435271e-01
4.11514759e-01 5.47920763e-01 3.56089175e-02 -5.37932396... | [12.402861595153809, 8.556004524230957] |
8ffdd5a0-726a-4729-b7fe-cfe960ab92c4 | data-needs-for-integrated-economic | 2209.01487 | null | https://arxiv.org/abs/2209.01487v1 | https://arxiv.org/pdf/2209.01487v1.pdf | Data needs for integrated economic-epidemiological models of pandemic mitigation policies | The COVID-19 pandemic and the mitigation policies implemented in response to it have resulted in economic losses worldwide. Attempts to understand the relationship between economics and epidemiology has lead to a new generation of integrated mathematical models. The data needs for these models transcend those of the in... | ['Katharina D. Hauck', 'Peter C. Smith', 'Patrick Doohan', 'Robert Johnson', 'Giovanni Forchini', 'Christian Morgenstern', 'David J. Haw'] | 2022-09-03 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 1.61562100e-01 -5.30797653e-02 -1.42438054e-01 1.94136858e-01
1.92161471e-01 -3.54732484e-01 8.55763912e-01 7.38580585e-01
-8.53287220e-01 6.69969201e-01 5.59823811e-01 -8.27289462e-01
-7.41471469e-01 -8.52587342e-01 -2.17200086e-01 -4.46000487e-01
-6.38862193e-01 6.16447151e-01 -7.96248913e-02 -4.69561279... | [5.960150241851807, 4.3710713386535645] |
c591906a-0916-4b7d-bd63-e72ddeff6598 | modeep-a-deep-learning-framework-using-motion | 1409.7963 | null | http://arxiv.org/abs/1409.7963v1 | http://arxiv.org/pdf/1409.7963v1.pdf | MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation | In this work, we propose a novel and efficient method for articulated human
pose estimation in videos using a convolutional network architecture, which
incorporates both color and motion features. We propose a new human body pose
dataset, FLIC-motion, that extends the FLIC dataset with additional motion
features. We ap... | ['Yann Lecun', 'Jonathan Tompson', 'Arjun Jain', 'Christoph Bregler'] | 2014-09-28 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-1.27163798e-01 -3.48051816e-01 -7.41912723e-02 -2.36183301e-01
-4.01905060e-01 -2.87958324e-01 3.29511493e-01 -7.15736866e-01
-9.12533462e-01 5.69922864e-01 5.45240700e-01 5.77229917e-01
4.85673308e-01 -2.04555273e-01 -5.40272593e-01 -1.12481326e-01
-4.16409463e-01 6.12154543e-01 4.83190179e-01 -4.19832468... | [7.028767108917236, -0.8089232444763184] |
e57154f8-1381-416f-b116-69febf1bdf07 | progressive-and-aligned-pose-attention | 2103.11622 | null | https://arxiv.org/abs/2103.11622v1 | https://arxiv.org/pdf/2103.11622v1.pdf | Progressive and Aligned Pose Attention Transfer for Person Image Generation | This paper proposes a new generative adversarial network for pose transfer, i.e., transferring the pose of a given person to a target pose. We design a progressive generator which comprises a sequence of transfer blocks. Each block performs an intermediate transfer step by modeling the relationship between the conditio... | ['Xiang Bai', 'Wenqing Cheng', 'Baoguang Shi', 'Mengde Xu', 'Tengteng Huang', 'Zhen Zhu'] | 2021-03-22 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [-5.03930449e-02 -4.21579108e-02 2.85958320e-01 -4.57671970e-01
-4.21936214e-01 -5.84564507e-01 6.13407433e-01 -5.14145792e-01
-2.73910642e-01 7.39685476e-01 2.77861953e-01 2.57208377e-01
3.49872291e-01 -8.06519866e-01 -8.14098358e-01 -5.71650803e-01
2.46373862e-01 5.14552534e-01 4.50440273e-02 -2.51621604... | [12.036809921264648, -0.800792932510376] |
89c8f10c-93fc-4d16-99b4-51e3210562fe | physics-constrained-backdoor-attacks-on-power | 2211.04445 | null | https://arxiv.org/abs/2211.04445v1 | https://arxiv.org/pdf/2211.04445v1.pdf | Physics-Constrained Backdoor Attacks on Power System Fault Localization | The advances in deep learning (DL) techniques have the potential to deliver transformative technological breakthroughs to numerous complex tasks in modern power systems that suffer from increasing uncertainty and nonlinearity. However, the vulnerability of DL has yet to be thoroughly explored in power system tasks unde... | ['Zuyi Li', 'Ren Wang', 'Jianing Bai'] | 2022-11-07 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [-1.59115165e-01 -2.11351782e-01 -2.05939129e-01 1.70557454e-01
-5.42676032e-01 -9.23798978e-01 5.32516479e-01 1.11665703e-01
4.28169996e-01 7.78757870e-01 -8.01084518e-01 -7.35870421e-01
-4.17723864e-01 -7.55578518e-01 -8.12958837e-01 -1.08462870e+00
-8.65204632e-01 5.80390096e-02 2.07741871e-01 -1.70492753... | [5.434837818145752, 7.427256107330322] |
fc39cece-c6c4-4cb4-86fd-7ca002e0d00b | human-gender-prediction-based-on-deep | 2205.09850 | null | https://arxiv.org/abs/2205.09850v3 | https://arxiv.org/pdf/2205.09850v3.pdf | Human Gender Prediction Based on Deep Transfer Learning from Panoramic Radiograph Images | Panoramic Dental Radiography (PDR) image processing is one of the most extensively used manual methods for gender determination in forensic medicine. With the assistance of the PDR images, a person's biological gender determination can be performed through analyzing skeletal structures expressing sexual dimorphism. Man... | ['I. Atas'] | 2022-05-19 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [-9.47395631e-04 1.07666127e-01 4.76858318e-02 -4.47216660e-01
-3.42107832e-01 1.98469654e-01 3.44550163e-01 2.03900516e-01
-8.94422770e-01 5.42099774e-01 -1.92969754e-01 -9.06630978e-02
-1.15310125e-01 -1.15077448e+00 -3.71409029e-01 -8.83406103e-01
1.50186926e-01 7.96510518e-01 3.48705910e-02 -2.03100398... | [14.344772338867188, -2.0359063148498535] |
306a75cd-5f50-4cd5-86ab-47828610bab6 | zeroeggs-zero-shot-example-based-gesture | 2209.07556 | null | https://arxiv.org/abs/2209.07556v2 | https://arxiv.org/pdf/2209.07556v2.pdf | ZeroEGGS: Zero-shot Example-based Gesture Generation from Speech | We present ZeroEGGS, a neural network framework for speech-driven gesture generation with zero-shot style control by example. This means style can be controlled via only a short example motion clip, even for motion styles unseen during training. Our model uses a Variational framework to learn a style embedding, making ... | ['Marc-André Carbonneau', 'Nikolaus F. Troje', 'Daniel Holden', 'Ylva Ferstl', 'Saeed Ghorbani'] | 2022-09-15 | null | null | null | null | ['gesture-generation'] | ['robots'] | [ 3.45231712e-01 1.41438887e-01 -1.81358293e-01 -5.10225832e-01
-5.42480290e-01 -8.75468552e-01 1.00188255e+00 -9.15961027e-01
-3.03273708e-01 4.47041512e-01 8.21898580e-01 1.52183741e-01
1.84570327e-01 -6.02118194e-01 -5.83956182e-01 -5.66382766e-01
8.16509351e-02 4.41082537e-01 1.43010691e-01 -4.08911198... | [5.653951644897461, -0.11354607343673706] |
d6bbaccd-f0cf-4557-9d27-e01b77b1c052 | sequence-to-point-learning-based-on | 2006.00250 | null | https://arxiv.org/abs/2006.00250v1 | https://arxiv.org/pdf/2006.00250v1.pdf | Sequence to Point Learning Based on Bidirectional Dilated Residual Network for Non Intrusive Load Monitoring | Non Intrusive Load Monitoring (NILM) or Energy Disaggregation (ED), seeks to save energy by decomposing corresponding appliances power reading from an aggregate power reading of the whole house. It is a single channel blind source separation problem (SCBSS) and difficult prediction problem because it is unidentifiable.... | ['Zhenrong Zhang', 'Ziyue Jia', 'Linfeng Yang', 'Hui Liu', 'Fannie Kong'] | 2020-05-30 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 8.47708061e-02 -2.97909707e-01 -3.28082502e-01 -2.42987871e-01
-7.37668693e-01 -6.28940701e-01 4.30948257e-01 -4.44178253e-01
2.20367327e-01 7.75061548e-01 4.38555211e-01 -3.30305636e-01
-2.58094907e-01 -5.71355283e-01 -6.15121961e-01 -9.18847442e-01
-6.27551377e-02 1.51593253e-01 -4.21052039e-01 -1.13102600... | [16.065776824951172, 7.579491138458252] |
114739a5-a9f4-4cf7-a86a-63823d087d02 | a-physically-informed-deep-learning-approach | 2208.04938 | null | https://arxiv.org/abs/2208.04938v1 | https://arxiv.org/pdf/2208.04938v1.pdf | A physically-informed Deep-Learning approach for locating sources in a waveguide | Inverse source problems are central to many applications in acoustics, geophysics, non-destructive testing, and more. Traditional imaging methods suffer from the resolution limit, preventing distinction of sources separated by less than the emitted wavelength. In this work we propose a method based on physically-inform... | ['Dmitry Batenkov', 'Eli Turkel', 'Symeon Papadimitropoulos', 'Adar Kahana'] | 2022-08-07 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [ 4.78206098e-01 -4.60841507e-02 8.13578725e-01 -2.91271538e-01
-5.14833987e-01 -2.25514069e-01 2.16480255e-01 -2.21274704e-01
-7.23312199e-01 7.28968203e-01 6.65558800e-02 -8.68762508e-02
-8.62484872e-01 -7.45307267e-01 -4.08908695e-01 -1.03031826e+00
-3.65320683e-01 6.01880491e-01 8.91641304e-02 -5.12688085... | [12.46377182006836, -2.5769975185394287] |
4d2244c1-067b-4f81-bd36-246fadb0c452 | depth-estimation-using-modified-cost-function | 1703.00919 | null | http://arxiv.org/abs/1703.00919v2 | http://arxiv.org/pdf/1703.00919v2.pdf | Depth Estimation using Modified Cost Function for Occlusion Handling | The paper presents a novel approach to occlusion handling problem in depth
estimation using three views. A solution based on modification of similarity
cost function is proposed. During the depth estimation via optimization
algorithms like Graph Cut similarity metric is constantly updated so that only
non-occluded frag... | ['Olgierd Stankiewicz', 'Marek Domanski', 'Krzysztof Wegner'] | 2017-03-02 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 3.03943127e-01 2.97610432e-01 1.73462063e-01 -3.52098703e-01
-3.79275233e-01 -3.39494437e-01 2.01792464e-01 2.52844006e-01
-3.02856982e-01 7.54557550e-01 2.28592753e-01 1.67365864e-01
-1.48144722e-01 -1.06565595e+00 -2.92527556e-01 -4.76131290e-01
-5.78019954e-02 5.50369501e-01 7.49494016e-01 1.13683632... | [9.302372932434082, -2.5382800102233887] |
8d27698f-263b-4093-8786-d0f7edada3af | unsupervised-behavior-change-detection-in | 1908.05103 | null | https://arxiv.org/abs/1908.05103v1 | https://arxiv.org/pdf/1908.05103v1.pdf | Unsupervised Behavior Change Detection in Multidimensional Data Streams for Maritime Traffic Monitoring | The worldwide growth of maritime traffic and the development of the Automatic Identification System (AIS) has led to advances in monitoring systems for preventing vessel accidents and detecting illegal activities. In this work, we describe research gaps and challenges in machine learning for vessel behavior change and ... | ['Stan Matwin', 'Vania Bogorny', 'Lucas May Petry', 'Amilcar Soares'] | 2019-08-14 | null | null | null | null | ['semi-supervised-change-detection'] | ['computer-vision'] | [ 3.15737396e-01 -1.58797041e-01 -2.09898099e-01 -5.03589571e-01
-3.49134594e-01 -7.05156207e-01 6.17675900e-01 8.51059258e-01
-5.66053152e-01 6.53790712e-01 2.20460147e-01 -1.62432745e-01
-5.04032850e-01 -8.60350311e-01 -7.18311742e-02 -5.99095404e-01
-5.43993473e-01 1.73108771e-01 3.26443881e-01 -1.66156471... | [7.187201976776123, 2.7496418952941895] |
4a576248-f03f-4e46-b3f3-8c9ca1e012bc | blending-target-domain-adaptation-by-1 | 1907.03389 | null | https://arxiv.org/abs/1907.03389v1 | https://arxiv.org/pdf/1907.03389v1.pdf | Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks | (Unsupervised) Domain Adaptation (DA) seeks for classifying target instances when solely provided with source labeled and target unlabeled examples for training. Learning domain-invariant features helps to achieve this goal, whereas it underpins unlabeled samples drawn from a single or multiple explicit target domains ... | ['Liang Lin', 'Xiaodan Liang', 'Ziliang Chen', 'Jingyu Zhuang'] | 2019-07-08 | blending-target-domain-adaptation-by | http://openaccess.thecvf.com/content_CVPR_2019/html/Chen_Blending-Target_Domain_Adaptation_by_Adversarial_Meta-Adaptation_Networks_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Blending-Target_Domain_Adaptation_by_Adversarial_Meta-Adaptation_Networks_CVPR_2019_paper.pdf | cvpr-2019-6 | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 3.75948578e-01 3.17610912e-02 -4.07648832e-01 -3.90386671e-01
-9.51561034e-01 -9.22357798e-01 7.18944430e-01 -1.34142131e-01
-2.44430497e-01 7.99580276e-01 1.09042712e-02 -1.99689120e-01
2.89122045e-01 -8.33592534e-01 -8.73251200e-01 -8.14278603e-01
1.89380795e-01 7.30590343e-01 2.62822151e-01 -3.34524184... | [10.335018157958984, 3.0913710594177246] |
11c912f4-689e-499e-940f-a950a82559fb | push-pull-characterizing-the-adversarial | 2210.00753 | null | https://arxiv.org/abs/2210.00753v1 | https://arxiv.org/pdf/2210.00753v1.pdf | Push-Pull: Characterizing the Adversarial Robustness for Audio-Visual Active Speaker Detection | Audio-visual active speaker detection (AVASD) is well-developed, and now is an indispensable front-end for several multi-modal applications. However, to the best of our knowledge, the adversarial robustness of AVASD models hasn't been investigated, not to mention the effective defense against such attacks. In this pape... | ['Jyh-Shing Roger Jang', 'Hung-Yi Lee', 'Helen Meng', 'Haibin Wu', 'Xuanjun Chen'] | 2022-10-03 | null | null | null | null | ['audio-visual-active-speaker-detection'] | ['computer-vision'] | [-8.92238542e-02 3.13125789e-01 1.42578259e-01 -2.44254693e-02
-1.27318418e+00 -1.04762495e+00 5.92504621e-01 3.93672995e-02
-2.47150198e-01 1.58644661e-01 2.32400328e-01 -5.36647260e-01
3.57887931e-02 -2.73001343e-01 -5.53199410e-01 -8.57892871e-01
-4.57896173e-01 1.52544007e-01 4.05690104e-01 -9.22425389... | [14.011585235595703, 5.8440656661987305] |
8b1142a0-3612-49c8-a49e-25e8e07238d7 | cnn-based-patch-matching-for-optical-flow | 1607.08064 | null | https://arxiv.org/abs/1607.08064v4 | https://arxiv.org/pdf/1607.08064v4.pdf | CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss | Learning based approaches have not yet achieved their full potential in optical flow estimation, where their performance still trails heuristic approaches. In this paper, we present a CNN based patch matching approach for optical flow estimation. An important contribution of our approach is a novel thresholded loss for... | ['Kiran varanasi', 'Christian Bailer', 'Didier Stricker'] | 2016-07-27 | cnn-based-patch-matching-for-optical-flow-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Bailer_CNN-Based_Patch_Matching_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Bailer_CNN-Based_Patch_Matching_CVPR_2017_paper.pdf | cvpr-2017-7 | ['patch-matching'] | ['computer-vision'] | [-3.54176790e-01 -4.39800054e-01 4.22327556e-02 -1.53318802e-02
-5.34676909e-01 -4.98041809e-01 6.45623803e-01 -6.53284788e-02
-6.62351727e-01 9.18716133e-01 1.86822549e-01 -3.38307954e-02
-2.19381437e-01 -7.52239168e-01 -6.41839325e-01 -4.65320379e-01
-5.16935766e-01 1.56092778e-01 5.70291817e-01 -2.60276377... | [8.809144020080566, -1.8217999935150146] |
e9c5b3be-8da8-4850-ba01-816e2764aa90 | one-time-of-interaction-may-not-be-enough-go | null | null | https://aclanthology.org/P19-1001 | https://aclanthology.org/P19-1001.pdf | One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues | Currently, researchers have paid great attention to retrieval-based dialogues in open-domain. In particular, people study the problem by investigating context-response matching for multi-turn response selection based on publicly recognized benchmark data sets. State-of-the-art methods require a response to interact wit... | ['Chongyang Tao', 'Wenpeng Hu', 'Wei Wu', 'Rui Yan', 'Dongyan Zhao', 'Can Xu'] | 2019-07-01 | null | null | null | acl-2019-7 | ['conversational-response-selection'] | ['natural-language-processing'] | [ 5.16775489e-01 2.33216375e-01 -8.13521668e-02 -7.39480615e-01
-1.08742702e+00 -4.53746796e-01 6.89137518e-01 8.94531906e-02
-4.32780057e-01 3.25209022e-01 7.97412455e-01 -2.71398425e-01
-3.77028994e-02 -4.43354666e-01 -1.40134931e-01 -3.73070121e-01
3.11477661e-01 6.17677450e-01 1.27766997e-01 -6.86044335... | [12.471622467041016, 7.839714527130127] |
dfaa8915-dea8-4d5a-9803-4519b5a12a87 | multicqa-zero-shot-transfer-of-self | 2010.00980 | null | https://arxiv.org/abs/2010.00980v1 | https://arxiv.org/pdf/2010.00980v1.pdf | MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale | We study the zero-shot transfer capabilities of text matching models on a massive scale, by self-supervised training on 140 source domains from community question answering forums in English. We investigate the model performances on nine benchmarks of answer selection and question similarity tasks, and show that all 14... | ['Iryna Gurevych', 'Jonas Pfeiffer', 'Andreas Rücklé'] | 2020-10-02 | null | https://aclanthology.org/2020.emnlp-main.194 | https://aclanthology.org/2020.emnlp-main.194.pdf | emnlp-2020-11 | ['question-similarity', 'answer-selection'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.10251166e-01 1.45198433e-02 -2.66204149e-01 -2.80195981e-01
-1.64297760e+00 -6.33283496e-01 9.21579778e-01 2.30598912e-01
-6.13290787e-01 7.48579621e-01 5.96898317e-01 -1.89473227e-01
-3.10936570e-01 -7.28686750e-01 -7.43673921e-01 9.16626900e-02
4.31338251e-01 1.04777098e+00 8.44836652e-01 -8.92966866... | [11.378213882446289, 7.947145938873291] |
e47b30e0-89a9-46a8-b181-71f47b233d80 | offline-reinforcement-learning-with-7 | 2307.02752 | null | https://arxiv.org/abs/2307.02752v1 | https://arxiv.org/pdf/2307.02752v1.pdf | Offline Reinforcement Learning with Imbalanced Datasets | The prevalent use of benchmarks in current offline reinforcement learning (RL) research has led to a neglect of the imbalance of real-world dataset distributions in the development of models. The real-world offline RL dataset is often imbalanced over the state space due to the challenge of exploration or safety conside... | ['Zhao Ding', 'Wai Kin Chan', 'Haoran Xu', 'JieLin Qiu', 'Sijie Chen', 'Li Jiang'] | 2023-07-06 | null | null | null | null | ['q-learning', 'reinforcement-learning-1', 'retrieval', 'offline-rl', 'd4rl'] | ['methodology', 'methodology', 'methodology', 'playing-games', 'robots'] | [-9.72018987e-02 5.43829938e-03 -6.06803536e-01 -1.89193726e-01
-8.04715216e-01 -6.15697563e-01 3.25223029e-01 4.46121275e-01
-5.65721989e-01 1.01689279e+00 3.08777809e-01 -4.38621491e-01
-3.44047159e-01 -6.51585162e-01 -7.76092768e-01 -4.04181600e-01
-2.42843136e-01 6.61470354e-01 -2.37997353e-01 -3.04795802... | [4.081406593322754, 2.278317928314209] |
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