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9c5bd677-8a2a-48b6-82b1-9d3e1c16e45f | deepring-learning-roto-translation-invariant | 2210.11029 | null | https://arxiv.org/abs/2210.11029v1 | https://arxiv.org/pdf/2210.11029v1.pdf | DeepRING: Learning Roto-translation Invariant Representation for LiDAR based Place Recognition | LiDAR based place recognition is popular for loop closure detection and re-localization. In recent years, deep learning brings improvements to place recognition by learnable feature extraction. However, these methods degenerate when the robot re-visits previous places with large perspective difference. To address the c... | ['Yue Wang', 'Rong Xiong', 'Li Tang', 'Xuecheng Xu', 'Sha Lu'] | 2022-10-20 | null | null | null | null | ['loop-closure-detection', 'one-shot-learning'] | ['computer-vision', 'methodology'] | [-3.16408984e-02 -3.72694552e-01 -4.09305930e-01 -5.11694252e-01
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-1.93835303e-01 3.27122301e-01 6.55768216e-02 -1.82657897... | [7.551284313201904, -2.133492946624756] |
7a1cb48d-0553-4287-b9a1-c3f0d5701aa7 | compressed-video-quality-assessment-for-super | 2305.04844 | null | https://arxiv.org/abs/2305.04844v1 | https://arxiv.org/pdf/2305.04844v1.pdf | Compressed Video Quality Assessment for Super-Resolution: a Benchmark and a Quality Metric | We developed a super-resolution (SR) benchmark to analyze SR's capacity to upscale compressed videos. Our dataset employed video codecs based on five compression standards: H.264, H.265, H.266, AV1, and AVS3. We assessed 17 state-ofthe-art SR models using our benchmark and evaluated their ability to preserve scene cont... | ['Dmitriy Vatolin', 'Ivan Molodetskikh', 'Evgeney Bogatyrev'] | 2023-05-08 | null | null | null | null | ['video-quality-assessment', 'video-quality-assessment'] | ['computer-vision', 'time-series'] | [ 2.10907146e-01 -4.86987591e-01 -2.51953751e-01 -2.47959048e-01
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-4.69970345e-01 -3.02056521e-01 6.64063752e-01 -3.25534999... | [11.503279685974121, -1.8006539344787598] |
3409fe84-e697-45d5-acb3-3495de3aa357 | taxonomy-completion-via-triplet-matching | 2101.01896 | null | https://arxiv.org/abs/2101.01896v3 | https://arxiv.org/pdf/2101.01896v3.pdf | Taxonomy Completion via Triplet Matching Network | Automatically constructing taxonomy finds many applications in e-commerce and web search. One critical challenge is as data and business scope grow in real applications, new concepts are emerging and needed to be added to the existing taxonomy. Previous approaches focus on the taxonomy expansion, i.e. finding an approp... | ['Lei LI', 'Yuning Mao', 'Jiaming Shen', 'Jiaze Chen', 'Ying Zeng', 'Xiangchen Song', 'Jieyu Zhang'] | 2021-01-06 | null | null | null | null | ['taxonomy-expansion'] | ['natural-language-processing'] | [ 2.49250978e-01 -3.03104281e-01 -5.20940840e-01 -6.83493793e-01
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7.66972601e-02 7.86450267e-01 1.39942184e-01 -5.13969898... | [9.224791526794434, 8.004592895507812] |
27214a65-b7dc-4074-a81f-877f39479206 | sfcw-gpr-tree-roots-detection-enhancement-by | 2204.02594 | null | https://arxiv.org/abs/2204.02594v1 | https://arxiv.org/pdf/2204.02594v1.pdf | SFCW GPR tree roots detection enhancement by time frequency analysis in tropical areas | Accurate monitoring of tree roots using ground penetrating radar (GPR) is very useful in assessing the trees health. In high moisture tropical areas such as Singapore, tree fall due to root rot can cause loss of lives and properties. The tropical complex soil characteristics due to the high moisture content tends to af... | ['Mohamed Lokman Mohd Yusof', 'Genevieve Ow', 'Abdulkadir C. Yucel', 'Yee Hui Lee', 'Wenhao Luo'] | 2022-04-06 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 6.43328547e-01 -3.23016077e-01 2.48103544e-01 1.74360290e-01
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-4.41751450e-01 -2.63093174e-01 3.11648130e-01 -1.17017843... | [6.831613540649414, 1.392909049987793] |
bf8323e1-2f14-4561-9052-1a0e16672234 | neuralpci-spatio-temporal-neural-field-for-3d | 2303.15126 | null | https://arxiv.org/abs/2303.15126v1 | https://arxiv.org/pdf/2303.15126v1.pdf | NeuralPCI: Spatio-temporal Neural Field for 3D Point Cloud Multi-frame Non-linear Interpolation | In recent years, there has been a significant increase in focus on the interpolation task of computer vision. Despite the tremendous advancement of video interpolation, point cloud interpolation remains insufficiently explored. Meanwhile, the existence of numerous nonlinear large motions in real-world scenarios makes t... | ['Changjun Jiang', 'Guang Chen', 'Fan Lu', 'Ruisi Lu', 'Danni Wu', 'Zehan Zheng'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zheng_NeuralPCI_Spatio-Temporal_Neural_Field_for_3D_Point_Cloud_Multi-Frame_Non-Linear_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zheng_NeuralPCI_Spatio-Temporal_Neural_Field_for_3D_Point_Cloud_Multi-Frame_Non-Linear_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-point-cloud-interpolation'] | ['computer-vision'] | [-1.40467107e-01 -3.89964074e-01 -1.65602267e-01 -3.84916633e-01
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22c06c68-c00b-438e-9baf-42ce196c7d77 | neural-gas-network-image-features-and | 2301.12176 | null | https://arxiv.org/abs/2301.12176v1 | https://arxiv.org/pdf/2301.12176v1.pdf | Neural Gas Network Image Features and Segmentation for Brain Tumor Detection Using Magnetic Resonance Imaging Data | Accurate detection of brain tumors could save lots of lives and increasing the accuracy of this binary classification even as much as a few percent has high importance. Neural Gas Networks (NGN) is a fast, unsupervised algorithm that could be used in data clustering, image pattern recognition, and image segmentation. I... | ['S. Muhammad Hossein Mousavi'] | 2023-01-28 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 2.44302735e-01 -1.84456795e-01 -5.89101240e-02 -1.37280554e-01
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-1.61639795e-01 8.30207467e-01 1.79695576e-01 4.37608138... | [14.753894805908203, -2.553692579269409] |
f2d6b59b-d4cd-4a62-9368-ef656c730d8d | applying-information-extraction-to-storybook | null | null | https://aclanthology.org/2022.rocling-1.36 | https://aclanthology.org/2022.rocling-1.36.pdf | Applying Information Extraction to Storybook Question and Answer Generation | For educators, how to generate high quality question-answer pairs from story text is a time-consuming and labor-intensive task. The purpose is not to make students unable to answer, but to ensure that students understand the story text through the generated question-answer pairs. In this paper, we improve the FairyTale... | ['Chia-Hui Chang', 'Kai-Yen Kao'] | null | null | null | null | rocling-2022-11 | ['question-generation', 'answer-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.24635555e-02 3.87739658e-01 3.32629621e-01 -4.32909161e-01
-9.99087393e-01 -9.69426036e-01 4.03028846e-01 5.25384367e-01
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8.12699676e-01 3.81985039e-01 7.96195924e-01 -7.09607244... | [11.48165225982666, 8.040923118591309] |
d0550c61-220a-4e60-87b8-f23d7f9e1443 | skellam-rank-fair-learning-to-rank-algorithm | 2306.06607 | null | https://arxiv.org/abs/2306.06607v1 | https://arxiv.org/pdf/2306.06607v1.pdf | Skellam Rank: Fair Learning to Rank Algorithm Based on Poisson Process and Skellam Distribution for Recommender Systems | Recommender system is a widely adopted technology in a diversified class of product lines. Modern day recommender system approaches include matrix factorization, learning to rank and deep learning paradigms, etc. Unlike many other approaches, learning to rank builds recommendation results based on maximization of the p... | ['Hao Wang'] | 2023-06-11 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [-5.05434692e-01 -5.02598166e-01 -4.40150917e-01 -4.47659433e-01
-1.43143296e-01 -4.83547688e-01 6.05149329e-01 1.27818882e-01
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-2.58969218e-01 8.97702694e-01 -5.14762476e-02 -6.98831141... | [9.955678939819336, 5.709502696990967] |
a50ae757-4931-479e-9697-00711daabe1a | grassmann-pooling-as-compact-homogeneous | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Xing_Wei_Grassmann_Pooling_for_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Xing_Wei_Grassmann_Pooling_for_ECCV_2018_paper.pdf | Grassmann Pooling as Compact Homogeneous Bilinear Pooling for Fine-Grained Visual Classification | Designing discriminative and invariant features is the key to visual recognition. Recently, the bilinear pooled feature matrix of Convolutional Neural Network (CNN) has shown to achieve state-of-the-art performance on a range of fine-grained visual recognition tasks. The bilinear feature matrix collects second-order st... | ['Yue Zhang', 'Xing Wei', 'Yihong Gong', 'Nanning Zheng', 'Jiawei Zhang'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['fine-grained-visual-recognition'] | ['computer-vision'] | [-2.70843208e-01 -5.14569700e-01 -6.49563447e-02 -3.35288703e-01
-2.68084973e-01 -6.68695807e-01 7.82863081e-01 -2.09611356e-01
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-5.17786980e-01 -6.03274345e-01 -5.67054570e-01 -1.08692682e+00
-6.13601543e-02 -3.38425428e-01 1.03508785e-01 -1.17846683... | [8.999458312988281, 2.137913465499878] |
1f962528-6c6a-4792-b6b6-a4c3e7cd1df5 | copula-density-neural-estimation | 2211.15353 | null | https://arxiv.org/abs/2211.15353v1 | https://arxiv.org/pdf/2211.15353v1.pdf | Copula Density Neural Estimation | Probability density estimation from observed data constitutes a central task in statistics. Recent advancements in machine learning offer new tools but also pose new challenges. The big data era demands analysis of long-range spatial and long-term temporal dependencies in large collections of raw data, rendering neural... | ['Andrea M. Tonello', 'Nunzio A. Letizia'] | 2022-11-25 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [-2.83508599e-01 -2.25938976e-01 -1.38952807e-01 -5.06263912e-01
-5.34550250e-01 -2.13514328e-01 6.45201683e-01 2.55362570e-01
-4.03281897e-01 1.19125509e+00 6.23698309e-02 -1.32285640e-01
-3.75861645e-01 -1.10835922e+00 -9.71246123e-01 -8.64861369e-01
-4.84213769e-01 9.04405355e-01 -2.02109218e-01 2.83486843... | [7.241539001464844, 3.9494123458862305] |
cd003731-c2ae-434d-ae8c-d7d858f46769 | learning-an-optimizer-for-image-deconvolution | 1804.03368 | null | https://arxiv.org/abs/1804.03368v2 | https://arxiv.org/pdf/1804.03368v2.pdf | Learning Deep Gradient Descent Optimization for Image Deconvolution | As an integral component of blind image deblurring, non-blind deconvolution removes image blur with a given blur kernel, which is essential but difficult due to the ill-posed nature of the inverse problem. The predominant approach is based on optimization subject to regularization functions that are either manually des... | ['Yanning Zhang', 'Chunhua Shen', 'Anton Van Den Hengel', 'Zhen Zhang', 'Dong Gong', 'Qinfeng Shi'] | 2018-04-10 | null | null | null | null | ['blind-image-deblurring', 'image-deconvolution'] | ['computer-vision', 'computer-vision'] | [ 1.35033980e-01 -4.52225149e-01 -2.70284489e-02 -3.09923321e-01
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1.72512338e-01 1.31851032e-01 -5.90874851e-02 -9.03407186... | [11.621603965759277, -2.655325412750244] |
0464581d-a27a-48e0-8f33-f7dfc1842d82 | assessment-of-a-cost-effective-headphone | 2207.12899 | null | https://arxiv.org/abs/2207.12899v1 | https://arxiv.org/pdf/2207.12899v1.pdf | Assessment of a cost-effective headphone calibration procedure for soundscape evaluations | To increase the availability and adoption of the soundscape standard, a low-cost calibration procedure for reproduction of audio stimuli over headphones was proposed as part of the global ``Soundscape Attributes Translation Project'' (SATP) for validating ISO/TS~12913-2:2018 perceived affective quality (PAQ) attribute ... | ['Woon-Seng Gan', 'Trevor Wong', 'Karn N. Watcharasupat', 'Zhen-Ting Ong', 'Kenneth Ooi', 'Bhan Lam'] | 2022-07-24 | null | null | null | null | ['soundscape-evaluation'] | ['audio'] | [ 2.17765257e-01 -2.34918118e-01 6.87969804e-01 -3.15627605e-01
-1.04455876e+00 -4.61646199e-01 5.30775711e-02 7.49028325e-01
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-5.38243651e-01 -4.20174450e-01 -5.70177436e-01 -4.39828336e-01
-1.86955556e-01 -2.51579583e-01 2.25405186e-01 1.65830180... | [15.139763832092285, 5.62797737121582] |
9d41bbfc-606e-49fd-82eb-73964e294df6 | automatic-quantification-of-settlement-damage | 2010.05512 | null | https://arxiv.org/abs/2010.05512v1 | https://arxiv.org/pdf/2010.05512v1.pdf | Automatic Quantification of Settlement Damage using Deep Learning of Satellite Images | Humanitarian disasters and political violence cause significant damage to our living space. The reparation cost to homes, infrastructure, and the ecosystem is often difficult to quantify in real-time. Real-time quantification is critical to both informing relief operations, but also planning ahead for rebuilding. Here,... | ['Weisi Guo', 'Lili Lu'] | 2020-10-12 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 2.07569331e-01 2.87996829e-01 2.98764795e-01 -7.89202452e-02
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-3.91014636e-01 2.89686769e-01 -2.70573944e-01 -6.88731849... | [9.50593376159668, -1.2932251691818237] |
14e01fa6-4dae-4da0-9e5d-cca641e5f806 | distributional-instance-segmentation-modeling | 2305.01910 | null | https://arxiv.org/abs/2305.01910v1 | https://arxiv.org/pdf/2305.01910v1.pdf | Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN | Object recognition and instance segmentation are fundamental skills in any robotic or autonomous system. Existing state-of-the-art methods are often unable to capture meaningful uncertainty in challenging or ambiguous scenes, and as such can cause critical errors in high-performance applications. In this paper, we expl... | ['Xi Chen', 'Pieter Abbeel', 'Nikhil Mishra', 'Yuxuan Liu'] | 2023-05-03 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 5.03346801e-01 1.70401365e-01 -1.45146132e-01 -5.89251578e-01
-1.14676058e+00 -7.67967880e-01 1.70061246e-01 -6.49244636e-02
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5.09691760e-02 1.09702325e+00 4.18395847e-01 2.72306621... | [6.800088405609131, -1.066758394241333] |
6afad2f6-4852-431e-afa4-213388fbb5b7 | leveraging-declarative-knowledge-in-text-and | 2004.14201 | null | https://arxiv.org/abs/2004.14201v2 | https://arxiv.org/pdf/2004.14201v2.pdf | Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection | We study the detection of propagandistic text fragments in news articles. Instead of merely learning from input-output datapoints in training data, we introduce an approach to inject declarative knowledge of fine-grained propaganda techniques. Specifically, we leverage the declarative knowledge expressed in both first-... | ['Daxin Jiang', 'Ruize Wang', 'Xuanjing Huang', 'Nan Duan', 'Ming Zhou', 'Wanjun Zhong', 'Duyu Tang', 'Zhongyu Wei'] | 2020-04-29 | null | https://aclanthology.org/2020.emnlp-main.320 | https://aclanthology.org/2020.emnlp-main.320.pdf | emnlp-2020-11 | ['propaganda-detection'] | ['natural-language-processing'] | [ 1.92343503e-01 2.24280462e-01 -8.63229215e-01 -5.30782759e-01
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-1.61928162e-01 3.40783268e-01 5.07190451e-02 -2.75125533... | [9.740107536315918, 7.992658615112305] |
51154fc9-8cb1-48f2-9a43-28f66a60cf24 | 190600575 | 1906.00575 | null | https://arxiv.org/abs/1906.00575v3 | https://arxiv.org/pdf/1906.00575v3.pdf | Jointly Learning Semantic Parser and Natural Language Generator via Dual Information Maximization | Semantic parsing aims to transform natural language (NL) utterances into formal meaning representations (MRs), whereas an NL generator achieves the reverse: producing a NL description for some given MRs. Despite this intrinsic connection, the two tasks are often studied separately in prior work. In this paper, we model... | ['Hai Ye', 'Wenjie Li', 'Lu Wang'] | 2019-06-03 | jointly-learning-semantic-parser-and-natural | https://aclanthology.org/P19-1201 | https://aclanthology.org/P19-1201.pdf | acl-2019-7 | ['dialogue-management'] | ['natural-language-processing'] | [ 4.42734241e-01 1.04469442e+00 -2.38314256e-01 -5.64384878e-01
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3.04280102e-01 5.51745296e-01 -3.06095660e-01 3.22569944... | [10.672913551330566, 9.103737831115723] |
98a1a6d7-b9d3-4395-95f2-4f7cba5768e0 | better-cmos-produces-clearer-images-learning | 2304.03542 | null | https://arxiv.org/abs/2304.03542v1 | https://arxiv.org/pdf/2304.03542v1.pdf | Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution | Most of the existing blind image Super-Resolution (SR) methods assume that the blur kernels are space-invariant. However, the blur involved in real applications are usually space-variant due to object motion, out-of-focus, etc., resulting in severe performance drop of the advanced SR methods. To address this problem, w... | ['Yong liu', 'Chengjie Wang', 'Yabiao Wang', 'Chao Xu', 'Jiangning Zhang', 'Xuhai Chen'] | 2023-04-07 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_Better_CMOS_Produces_Clearer_Images_Learning_Space-Variant_Blur_Estimation_for_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_Better_CMOS_Produces_Clearer_Images_Learning_Space-Variant_Blur_Estimation_for_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-super-resolution'] | ['computer-vision'] | [-6.29969835e-02 -9.16311383e-01 2.96715558e-01 -2.72287875e-01
-4.07995224e-01 -2.31256843e-01 2.77282834e-01 -6.22053981e-01
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-4.01977330e-01 -4.17169154e-01 -3.21878225e-01 -8.07654858e-01
1.41266927e-01 -6.07022047e-01 4.40352619e-01 -2.61442989... | [11.449225425720215, -2.60996675491333] |
109ea7a1-4e84-4b6d-a70f-000699897110 | thifly-research-at-semeval-2023-task-7-a | 2306.01245 | null | https://arxiv.org/abs/2306.01245v1 | https://arxiv.org/pdf/2306.01245v1.pdf | THiFLY Research at SemEval-2023 Task 7: A Multi-granularity System for CTR-based Textual Entailment and Evidence Retrieval | The NLI4CT task aims to entail hypotheses based on Clinical Trial Reports (CTRs) and retrieve the corresponding evidence supporting the justification. This task poses a significant challenge, as verifying hypotheses in the NLI4CT task requires the integration of multiple pieces of evidence from one or two CTR(s) and th... | ['Ji Wu', 'Xinxin You', 'Xien Liu', 'Miao Li', 'Meiwei Li', 'Ziyu Jin', 'Yuxuan Zhou'] | 2023-06-02 | null | null | null | null | ['natural-language-inference'] | ['natural-language-processing'] | [ 2.45180979e-01 1.58883244e-01 -6.93681538e-01 -3.64786476e-01
-1.32158923e+00 -4.10817742e-01 4.36665714e-01 7.45973110e-01
-4.81305420e-01 9.09613967e-01 2.25547329e-01 -6.77333772e-01
-4.79839623e-01 -6.47714019e-01 -7.51694441e-01 -8.67526233e-02
1.36423573e-01 3.67830187e-01 2.05870140e-02 1.05121575... | [8.56010627746582, 8.669601440429688] |
d43a4a13-f432-4f99-8bd7-c9e9c2a01b89 | seamless-copy-move-manipulation-in-digital | 2110.05747 | null | https://arxiv.org/abs/2110.05747v1 | https://arxiv.org/pdf/2110.05747v1.pdf | Seamless Copy Move Manipulation in Digital Images | The importance and relevance of digital image forensics has attracted researchers to establish different techniques for creating as well as detecting forgeries. The core category in passive image forgery is copy-move image forgery that affects the originality of image by applying a different transformation. In this pap... | ['Khizar Hayat', 'Mushtaq Ali', 'Tanzila Qazi'] | 2021-10-12 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 6.92491531e-01 -3.72085840e-01 2.61617541e-01 2.49548435e-01
-4.11396474e-01 -5.82967639e-01 1.44160256e-01 -1.73526213e-01
-2.09210664e-01 4.29932445e-01 9.80101712e-03 -2.72217721e-01
-2.00686201e-01 -9.05425370e-01 -4.34119821e-01 -8.56000066e-01
-2.18111109e-02 -6.62134171e-01 3.33418518e-01 -2.56979495... | [12.354524612426758, 0.9405251145362854] |
b1c241a2-58a2-4e76-add1-ddaa8c818f86 | reduced-gate-convolutional-lstm-design-using | null | null | https://openreview.net/forum?id=rJEyrjRqYX | https://openreview.net/pdf?id=rJEyrjRqYX | Reduced-Gate Convolutional LSTM Design Using Predictive Coding for Next-Frame Video Prediction | Spatiotemporal sequence prediction is an important problem in deep learning. We
study next-frame video prediction using a deep-learning-based predictive coding
framework that uses convolutional, long short-term memory (convLSTM) modules.
We introduce a novel reduced-gate convolutional LSTM architecture. Our
reduced-gat... | ['Magdy Bayoumi', 'Anthony S. Maida', 'Nelly Elsayed'] | 2018-09-27 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 2.36311316e-01 -7.92458355e-02 -5.81576049e-01 -5.57097673e-01
-4.60477382e-01 1.92149192e-01 3.23663831e-01 -4.23144937e-01
-3.92553747e-01 6.48717940e-01 1.58951044e-01 -5.14006317e-01
4.85445321e-01 -6.65578246e-01 -1.14521408e+00 -4.96269315e-01
-2.81256646e-01 -1.13011554e-01 7.69241333e-01 8.27033371... | [8.950385093688965, 0.38918495178222656] |
937c8cd0-777f-44d5-be3f-c336311266d3 | combining-word-embeddings-and-n-grams-for | 2004.14119 | null | https://arxiv.org/abs/2004.14119v1 | https://arxiv.org/pdf/2004.14119v1.pdf | Combining Word Embeddings and N-grams for Unsupervised Document Summarization | Graph-based extractive document summarization relies on the quality of the sentence similarity graph. Bag-of-words or tf-idf based sentence similarity uses exact word matching, but fails to measure the semantic similarity between individual words or to consider the semantic structure of sentences. In order to improve t... | ['Sanjay Krishna', 'Richard Schwartz', 'Manaj Srivastava', 'David Akodes', 'Zhuolin Jiang'] | 2020-04-25 | null | null | null | null | ['sentence-compression', 'extractive-document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.73873466e-01 1.51664108e-01 -3.13294619e-01 -4.32504117e-01
-8.54458451e-01 -3.95305306e-01 5.64910114e-01 1.03278267e+00
-3.88236195e-01 5.52287996e-01 1.24176347e+00 1.09497838e-01
-3.56493622e-01 -9.01870906e-01 -4.84223455e-01 -3.67840827e-01
-8.86112079e-02 4.11936700e-01 -1.19870208e-01 -4.40599889... | [12.523585319519043, 9.566071510314941] |
9889fbff-2c46-464b-8850-781f16c99559 | panocontext-former-panoramic-total-scene | 2305.12497 | null | https://arxiv.org/abs/2305.12497v2 | https://arxiv.org/pdf/2305.12497v2.pdf | PanoContext-Former: Panoramic Total Scene Understanding with a Transformer | Panoramic image enables deeper understanding and more holistic perception of $360^\circ$ surrounding environment, which can naturally encode enriched scene context information compared to standard perspective image. Previous work has made lots of effort to solve the scene understanding task in a bottom-up form, thus ea... | ['Zilong Dong', 'Ping Tan', 'Liefeng Bo', 'Chuan Fang', 'Yuan Dong'] | 2023-05-21 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [ 6.28560126e-01 -1.99099436e-01 5.03534377e-01 -7.64853656e-01
-3.47004592e-01 -5.49410284e-01 4.61505979e-01 5.73143363e-02
2.63720214e-01 3.03485900e-01 4.75524694e-01 -1.89671829e-01
-1.91956192e-01 -9.50368583e-01 -8.67632627e-01 -4.92747873e-01
2.35797301e-01 2.01874942e-01 1.58807799e-01 -2.53393143... | [8.647789001464844, -2.8411810398101807] |
c3cd2401-b550-4330-bf96-f276b4eea7d6 | bayesian-learning-of-effective-chemical | 2205.06268 | null | https://arxiv.org/abs/2205.06268v1 | https://arxiv.org/pdf/2205.06268v1.pdf | Bayesian learning of effective chemical master equations in crowded intracellular conditions | Biochemical reactions inside living cells often occur in the presence of crowders -- molecules that do not participate in the reactions but influence the reaction rates through excluded volume effects. However the standard approach to modelling stochastic intracellular reaction kinetics is based on the chemical master ... | ['Guido Sanguinetti', 'Ramon Grima', 'Svitlana Braichenko'] | 2022-05-11 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 4.44430113e-01 9.67669189e-02 3.75189722e-01 1.59301847e-01
-1.89667210e-01 -4.72957075e-01 9.92349088e-01 5.64144850e-01
-9.17300463e-01 1.41022670e+00 -2.73851641e-02 -1.36953026e-01
2.72625778e-02 -7.39096642e-01 -6.26882732e-01 -1.52485383e+00
-5.81200160e-02 8.79305840e-01 5.30859590e-01 -1.58542424... | [6.059707164764404, 4.260529041290283] |
82beab7b-e9c2-4c86-99ea-483ae0f848df | dd-tig-at-constraint-acl2022-multimodal | null | null | https://aclanthology.org/2022.constraint-1.2 | https://aclanthology.org/2022.constraint-1.2.pdf | DD-TIG at Constraint@ACL2022: Multimodal Understanding and Reasoning for Role Labeling of Entities in Hateful Memes | The memes serve as an important tool in online communication, whereas some hateful memes endanger cyberspace by attacking certain people or subjects. Recent studies address hateful memes detection while further understanding of relationships of entities in memes remains unexplored. This paper presents our work at the C... | ['Xiaolong Liu', 'Jun Gao', 'Jingjing Dong', 'Han Zhao', 'Ziming Zhou'] | null | null | null | null | constraint-acl-2022-5 | ['continual-pretraining'] | ['methodology'] | [ 3.30966525e-02 1.30853936e-01 1.42968353e-02 -3.54844965e-02
-4.69265848e-01 -9.45856631e-01 1.00349104e+00 4.39502627e-01
-7.78063118e-01 9.17059064e-01 5.86382985e-01 1.83958299e-02
1.47291213e-01 -7.22527802e-01 -4.30878460e-01 -4.42619324e-01
1.31815612e-01 4.05824393e-01 1.56042710e-01 -5.39802730... | [8.538400650024414, 10.66923713684082] |
2eee24a9-3a5e-41bc-ba33-61f5f9fed89c | human-action-generation-with-generative | 1805.10416 | null | http://arxiv.org/abs/1805.10416v1 | http://arxiv.org/pdf/1805.10416v1.pdf | Human Action Generation with Generative Adversarial Networks | Inspired by the recent advances in generative models, we introduce a human
action generation model in order to generate a consecutive sequence of human
motions to formulate novel actions. We propose a framework of an autoencoder
and a generative adversarial network (GAN) to produce multiple and consecutive
human action... | ['Mohammad Ahangar Kiasari', 'Minho Lee', 'Dennis Singh Moirangthem'] | 2018-05-26 | null | null | null | null | ['human-action-generation', 'action-generation'] | ['computer-vision', 'computer-vision'] | [ 5.09699523e-01 3.43835264e-01 7.90750757e-02 -3.54988463e-02
-3.19457442e-01 -4.44670290e-01 8.90408874e-01 -9.63706434e-01
-6.84663327e-03 8.56934547e-01 3.57872933e-01 -2.13192049e-02
4.24209893e-01 -9.47067738e-01 -7.63774157e-01 -8.20560098e-01
4.56640899e-01 3.97811472e-01 3.48427258e-02 -5.35846986... | [7.320693492889404, -0.11622694879770279] |
252e942c-f6b7-42f1-983c-d9985e722ed1 | kullback-leibler-maillard-sampling-for-multi | 2304.14989 | null | https://arxiv.org/abs/2304.14989v1 | https://arxiv.org/pdf/2304.14989v1.pdf | Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewards | We study $K$-armed bandit problems where the reward distributions of the arms are all supported on the $[0,1]$ interval. It has been a challenge to design regret-efficient randomized exploration algorithms in this setting. Maillard sampling~\cite{maillard13apprentissage}, an attractive alternative to Thompson sampling,... | ['Chicheng Zhang', 'Kwang-Sung Jun', 'Hao Qin'] | 2023-04-28 | null | null | null | null | ['thompson-sampling', 'multi-armed-bandits'] | ['methodology', 'miscellaneous'] | [-1.64593056e-01 1.54562980e-01 -8.29576075e-01 -3.42193276e-01
-1.51290107e+00 -8.15432668e-01 -1.48157114e-02 2.37576049e-02
-8.35572898e-01 1.20408237e+00 -6.73218106e-04 -9.78982806e-01
-9.36369002e-01 -7.52024889e-01 -9.51787174e-01 -7.44506538e-01
-5.33300698e-01 5.33414841e-01 -3.30713034e-01 2.07461938... | [4.534137725830078, 3.308532238006592] |
26633a50-c4f6-4eab-ad46-c49f11e7a284 | a-graph-based-text-similarity-measure-that | null | null | https://aclanthology.org/R17-1098 | https://aclanthology.org/R17-1098.pdf | A Graph-based Text Similarity Measure That Employs Named Entity Information | Text comparison is an interesting though hard task, with many applications in Natural Language Processing. This work introduces a new text-similarity measure, which employs named-entities{'} information extracted from the texts and the n-gram graphs{'} model for representing documents. Using OpenCalais as a named-entit... | ['Iraklis Varlamis', 'Leonidas Tsekouras', 'George Giannakopoulos'] | 2017-09-01 | null | null | null | ranlp-2017-9 | ['text-clustering'] | ['natural-language-processing'] | [-1.26941308e-01 2.06446514e-01 1.90779686e-01 -3.45880240e-01
-4.38948274e-01 -7.93147981e-01 7.40779877e-01 1.00402677e+00
-6.49736822e-01 1.66438401e-01 4.37973708e-01 -5.33127427e-01
-5.29618025e-01 -9.38692451e-01 1.43641725e-01 -4.65058684e-01
-1.97815791e-01 7.56599665e-01 4.05022323e-01 -1.76462337... | [9.979362487792969, 8.745014190673828] |
486e0887-4aaf-47bf-ab44-873b70c9fbd3 | an-improved-regret-analysis-for-ucb-n-and-ts | 2305.04093 | null | https://arxiv.org/abs/2305.04093v1 | https://arxiv.org/pdf/2305.04093v1.pdf | An improved regret analysis for UCB-N and TS-N | In the setting of stochastic online learning with undirected feedback graphs, Lykouris et al. (2020) previously analyzed the pseudo-regret of the upper confidence bound-based algorithm UCB-N and the Thompson Sampling-based algorithm TS-N. In this note, we show how to improve their pseudo-regret analysis. Our improvemen... | ['Nishant A. Mehta'] | 2023-05-06 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [-1.27983943e-01 6.51973426e-01 -4.13785398e-01 -3.04779023e-01
-8.61362696e-01 -6.02284014e-01 -3.57210368e-01 3.70089740e-01
-6.25238776e-01 1.23558986e+00 -2.46999145e-01 -7.05131948e-01
-5.85443139e-01 -8.04440916e-01 -1.04598629e+00 -6.34639680e-01
-7.75224388e-01 4.12428349e-01 3.06000024e-01 -5.45691177... | [4.6529927253723145, 3.4188485145568848] |
7162aa23-3c42-46e8-97c1-6a7b60b0c31d | early-myocardial-infarction-detection-over | 2111.05790 | null | https://arxiv.org/abs/2111.05790v3 | https://arxiv.org/pdf/2111.05790v3.pdf | Early Myocardial Infarction Detection over Multi-view Echocardiography | Myocardial infarction (MI) is the leading cause of mortality in the world that occurs due to a blockage of the coronary arteries feeding the myocardium. An early diagnosis of MI and its localization can mitigate the extent of myocardial damage by facilitating early therapeutic interventions. Following the blockage of a... | ['Moncef Gabbouj', 'Rashid Mazhar', 'Tahir Hamid', 'Serkan Kiranyaz', 'Aysen Degerli'] | 2021-11-09 | null | null | null | null | ['myocardial-infarction-detection'] | ['medical'] | [ 1.67554975e-01 -2.51116186e-01 -1.32080942e-01 2.02927947e-01
-5.35683036e-01 -9.28806663e-01 -3.27742621e-02 -2.81583816e-02
-4.17102575e-02 4.39433783e-01 -2.13929992e-02 -4.85550433e-01
-9.50785726e-02 -5.51437795e-01 -1.36013687e-01 -9.12230968e-01
-3.24151278e-01 2.92587519e-01 2.48184070e-01 4.56813127... | [14.247176170349121, -2.3763816356658936] |
94623665-4cd2-4c48-a46b-7061ea8c9801 | implicit-and-explicit-aspect-extraction-in | null | null | https://aclanthology.org/W18-3108 | https://aclanthology.org/W18-3108.pdf | Implicit and Explicit Aspect Extraction in Financial Microblogs | This paper focuses on aspect extraction which is a sub-task of Aspect-based Sentiment Analysis. The goal is to report an extraction method of financial aspects in microblog messages. Our approach uses a stock-investment taxonomy for the identification of explicit and implicit aspects. We compare supervised and unsuperv... | ['Gopal Sridhar', 'Thomas Gaillat', 'Manel Zarrouk', 'Bernardo Stearns', 'Ross McDermott', 'Brian Davis'] | 2018-07-01 | null | null | null | ws-2018-7 | ['aspect-extraction'] | ['natural-language-processing'] | [-3.35015029e-01 5.89223087e-01 -5.03702700e-01 -6.19626284e-01
-2.92149276e-01 -7.85714984e-01 1.14643764e+00 6.38804138e-01
-5.01883686e-01 6.18421793e-01 6.80297077e-01 -4.61831957e-01
2.50839710e-01 -1.21953118e+00 -9.45362379e-04 -1.81419417e-01
-1.11038581e-01 5.74529409e-01 2.41915107e-01 -4.58772689... | [11.235633850097656, 6.833914756774902] |
efec6229-ff79-468b-be8f-105894e5e8d3 | high-accuracy-android-malware-detection-using | 1608.00835 | null | http://arxiv.org/abs/1608.00835v1 | http://arxiv.org/pdf/1608.00835v1.pdf | High Accuracy Android Malware Detection Using Ensemble Learning | With over 50 billion downloads and more than 1.3 million apps in the Google
official market, Android has continued to gain popularity amongst smartphone
users worldwide. At the same time there has been a rise in malware targeting
the platform, with more recent strains employing highly sophisticated detection
avoidance ... | ['Suleiman Y. Yerima', 'Sakir Sezer', 'Igor Muttik'] | 2016-08-02 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 2.02883050e-01 -2.91375041e-01 -5.20035684e-01 8.94866884e-02
-5.47501206e-01 -7.64758229e-01 7.40548730e-01 -9.62215383e-03
-4.51576896e-02 7.02456772e-01 -3.70568454e-01 -8.07951689e-01
3.15799676e-02 -4.70732003e-01 -2.31696665e-01 -3.84486258e-01
-3.68525803e-01 5.07203974e-02 4.04012889e-01 -1.87417269... | [14.42809009552002, 9.683245658874512] |
99bd8e84-6a61-4ab2-9c45-a748f845bd4f | deepotsu-document-enhancement-and | 1901.06081 | null | http://arxiv.org/abs/1901.06081v1 | http://arxiv.org/pdf/1901.06081v1.pdf | DeepOtsu: Document Enhancement and Binarization using Iterative Deep Learning | This paper presents a novel iterative deep learning framework and apply it
for document enhancement and binarization. Unlike the traditional methods which
predict the binary label of each pixel on the input image, we train the neural
network to learn the degradations in document images and produce the uniform
images of... | ['Lambert Schomaker', 'Sheng He'] | 2019-01-18 | null | null | null | null | ['document-enhancement'] | ['computer-vision'] | [ 8.41652036e-01 -1.83095708e-01 1.32482961e-01 -4.47302133e-01
-3.75868648e-01 -2.77058154e-01 4.13388431e-01 -1.49096102e-01
-3.41158897e-01 6.42872214e-01 3.08029771e-01 -9.59713310e-02
-2.23416254e-01 -8.04351032e-01 -5.87540329e-01 -1.17286682e+00
1.07335329e-01 -1.16925575e-02 2.17085615e-01 -2.25516021... | [11.35031509399414, -2.080423593521118] |
49ac2b18-8a4c-45ae-92d1-272a7f7ab221 | multi-staged-cross-lingual-acoustic-model | null | null | https://aclanthology.org/2020.lrec-1.780 | https://aclanthology.org/2020.lrec-1.780.pdf | Multi-Staged Cross-Lingual Acoustic Model Adaption for Robust Speech Recognition in Real-World Applications - A Case Study on German Oral History Interviews | While recent automatic speech recognition systems achieve remarkable performance when large amounts of adequate, high quality annotated speech data is used for training, the same systems often only achieve an unsatisfactory result for tasks in domains that greatly deviate from the conditions represented by the training... | ['Joachim K{\\"o}hler', 'Oliver Walter', 'Sven Behnke', 'Christoph Schmidt', 'Michael Gref'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['robust-speech-recognition'] | ['speech'] | [ 4.49449986e-01 2.32177272e-01 2.26331830e-01 -7.26383448e-01
-1.57080495e+00 -5.22483468e-01 6.26763761e-01 1.64124608e-01
-7.54800439e-01 6.31294191e-01 1.87866241e-01 -2.78290153e-01
2.59183913e-01 -1.90951154e-01 -5.65632761e-01 -5.63673973e-01
2.78459817e-01 8.79266322e-01 4.34788644e-01 -3.25289845... | [14.418939590454102, 6.665928840637207] |
86a5623f-7ded-42d8-b429-5a6361fcee1c | streamlined-dense-video-captioning | 1904.03870 | null | http://arxiv.org/abs/1904.03870v1 | http://arxiv.org/pdf/1904.03870v1.pdf | Streamlined Dense Video Captioning | Dense video captioning is an extremely challenging task since accurate and
coherent description of events in a video requires holistic understanding of
video contents as well as contextual reasoning of individual events. Most
existing approaches handle this problem by first detecting event proposals from
a video and th... | ['Ning Xu', 'Jonghwan Mun', 'Zhou Ren', 'Linjie Yang', 'Bohyung Han'] | 2019-04-08 | streamlined-dense-video-captioning-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Mun_Streamlined_Dense_Video_Captioning_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Mun_Streamlined_Dense_Video_Captioning_CVPR_2019_paper.pdf | cvpr-2019-6 | ['dense-video-captioning'] | ['computer-vision'] | [ 3.97503614e-01 -2.51098555e-02 -2.78232455e-01 -5.33743680e-01
-8.18157017e-01 -5.49834847e-01 8.13890398e-01 3.11823606e-01
-2.94566542e-01 7.89958000e-01 7.58688092e-01 1.31294772e-01
2.78077006e-01 -5.85286856e-01 -1.00108039e+00 -3.36822003e-01
-9.76257473e-02 3.12436372e-01 4.97494817e-01 1.45083806... | [10.39478874206543, 0.6132733225822449] |
7dcc04c3-b0ed-437e-967d-cbe82c9d6532 | object-categorization-in-finer-levels | 1703.09990 | null | http://arxiv.org/abs/1703.09990v1 | http://arxiv.org/pdf/1703.09990v1.pdf | Object categorization in finer levels requires higher spatial frequencies, and therefore takes longer | The human visual system contains a hierarchical sequence of modules that take
part in visual perception at different levels of abstraction, i.e.,
superordinate, basic, and subordinate levels. One important question is to
identify the "entry" level at which the visual representation is commenced in
the process of object... | ['Mohammad Ganjtabesh', 'Timothée Masquelier', 'Matin N. Ashtiani', 'Saeed Reza Kheradpisheh'] | 2017-03-29 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 8.65874365e-02 -4.78803545e-01 3.62522930e-01 -2.29309544e-01
1.39344379e-01 -7.90688872e-01 4.36102927e-01 6.69346035e-01
-7.37728894e-01 3.15375000e-01 -2.55053081e-02 -4.27358299e-01
-2.53979534e-01 -6.82304204e-01 -4.43934977e-01 -7.31074095e-01
1.03488036e-01 -1.87019691e-01 7.65842497e-01 -2.03639925... | [10.107155799865723, 2.1731743812561035] |
6eb3ffef-53df-4b96-aaf9-2236cd7a268f | multi-talker-mvdr-beamforming-based-on | 1910.07753 | null | http://arxiv.org/abs/1910.07753v1 | http://arxiv.org/pdf/1910.07753v1.pdf | Multi-Talker MVDR Beamforming Based on Extended Complex Gaussian Mixture Model | In this letter, we present a novel multi-talker minimum variance
distortionless response (MVDR) beamforming as the front-end of an automatic
speech recognition (ASR) system in a dinner party scenario. The CHiME-5 dataset
is selected to evaluate our proposal for overlapping multi-talker scenario with
severe noise. A det... | [] | 2019-10-17 | null | null | null | null | ['target-speaker-extraction'] | ['audio'] | [ 1.50191978e-01 -3.02198599e-03 6.53970659e-01 -5.83245635e-01
-1.36989951e+00 -4.21161890e-01 5.38864672e-01 -3.81800860e-01
-4.67501104e-01 1.89653620e-01 6.44557059e-01 -4.25923616e-01
-6.24444187e-02 -1.87318847e-01 -3.06812584e-01 -1.09107077e+00
3.27330440e-01 5.19635417e-02 4.47969176e-02 -3.04327428... | [14.835280418395996, 5.961994647979736] |
d2608d98-d97f-4f14-98c1-c9afe8912862 | a-metaheuristic-multi-objective-interaction | 2211.05423 | null | https://arxiv.org/abs/2211.05423v1 | https://arxiv.org/pdf/2211.05423v1.pdf | A metaheuristic multi-objective interaction-aware feature selection method | Multi-objective feature selection is one of the most significant issues in the field of pattern recognition. It is challenging because it maximizes the classification performance and, at the same time, minimizes the number of selected features, and the mentioned two objectives are usually conflicting. To achieve a bett... | ['Mostafa Sabzekar', 'Modjtaba Rouhani', 'Motahare Namakin'] | 2022-11-10 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [ 2.59052664e-01 -6.74580753e-01 1.52985200e-01 1.24652851e-02
-1.10646345e-01 4.27736305e-02 9.93724167e-03 3.56378287e-01
-5.22537589e-01 9.61871207e-01 -3.53890419e-01 4.11086559e-01
-8.93119454e-01 -1.10449541e+00 -4.09596376e-02 -1.10979176e+00
-2.40589846e-02 4.63338763e-01 2.81289726e-01 -3.48536730... | [5.749634265899658, 3.485461950302124] |
3ad92053-e4bc-4b84-be68-f2d4c22b6554 | probability-map-guided-bi-directional | 1903.00923 | null | https://arxiv.org/abs/1903.00923v6 | https://arxiv.org/pdf/1903.00923v6.pdf | Pancreas segmentation with probabilistic map guided bi-directional recurrent UNet | Pancreas segmentation in medical imaging data is of great significance for clinical pancreas diagnostics and treatment. However, the large population variations in the pancreas shape and volume cause enormous segmentation difficulties, even for state-of-the-art algorithms utilizing fully-convolutional neural networks (... | ['Xiaozhu Lin', 'Xiaohua Qian', 'Jun Li', 'Hui Che', 'Hao Li'] | 2019-03-03 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [-2.60021221e-02 5.88663481e-02 -1.12061255e-01 -2.56304353e-01
-9.01462078e-01 -3.41941416e-01 9.10990406e-03 2.31447458e-01
-3.95346522e-01 5.23204029e-01 2.44750783e-01 -2.18277857e-01
-3.37805957e-01 -8.57331991e-01 -7.29627609e-01 -1.09664738e+00
-3.09448957e-01 5.07724822e-01 5.49364090e-01 3.64316434... | [14.5484037399292, -2.724985361099243] |
d5948d03-ab2c-4e5e-9fea-c06b2a9f0793 | multiscale-analysis-for-improving-texture | 2204.09841 | null | https://arxiv.org/abs/2204.09841v1 | https://arxiv.org/pdf/2204.09841v1.pdf | Multiscale Analysis for Improving Texture Classification | Information from an image occurs over multiple and distinct spatial scales. Image pyramid multiresolution representations are a useful data structure for image analysis and manipulation over a spectrum of spatial scales. This paper employs the Gaussian-Laplacian pyramid to treat different spatial frequency bands of a t... | ['Alessandro L. Koerich', 'Alceu S. Britto Jr.', 'Jonathan de Matos', 'Diego Saqui', 'Steve T. M. Ataky'] | 2022-04-21 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 6.34025156e-01 -4.33284074e-01 -1.66305363e-01 2.56243232e-03
-9.68984067e-01 -3.20482731e-01 2.98030972e-01 6.27525091e-01
-4.09764349e-01 5.44192135e-01 1.42543316e-01 2.85625994e-01
-5.93254328e-01 -9.47307706e-01 -4.23480533e-02 -1.18865311e+00
-3.05949479e-01 -1.64370328e-01 5.98159611e-01 -6.82245642... | [10.431540489196777, -0.3835970163345337] |
d29af8b2-0253-4626-848c-c357184a488b | hiding-image-in-image-by-five-modulus-method | 1304.1571 | null | http://arxiv.org/abs/1304.1571v1 | http://arxiv.org/pdf/1304.1571v1.pdf | Hiding Image in Image by Five Modulus Method for Image Steganography | This paper is to create a practical steganographic implementation to hide
color image (stego) inside another color image (cover). The proposed technique
uses Five Modulus Method to convert the whole pixels within both the cover and
the stego images into multiples of five. Since each pixels inside the stego
image is div... | ['Firas A. Jassim'] | 2013-04-04 | null | null | null | null | ['image-steganography'] | ['computer-vision'] | [ 7.62420356e-01 9.16947871e-02 1.97115317e-01 1.83330551e-01
-1.82479158e-01 -6.69552624e-01 -1.15852594e-01 -3.56329173e-01
-3.28764647e-01 6.64734006e-01 -4.55240548e-01 -6.94929540e-01
4.19323325e-01 -1.10101604e+00 -4.55269486e-01 -1.05264640e+00
-9.21783522e-02 -2.70686537e-01 5.64006150e-01 -3.69837970... | [4.300374507904053, 8.05219554901123] |
2660fc86-37cc-4234-8ebe-51bedd7d19d8 | a-symbolic-framework-for-systematic | 2305.12563 | null | https://arxiv.org/abs/2305.12563v1 | https://arxiv.org/pdf/2305.12563v1.pdf | A Symbolic Framework for Systematic Evaluation of Mathematical Reasoning with Transformers | Whether Transformers can learn to apply symbolic rules and generalise to out-of-distribution examples is an open research question. In this paper, we devise a data generation method for producing intricate mathematical derivations, and systematically perturb them with respect to syntax, structure, and semantics. Our ta... | ['Andre Freitas', 'Damien Teney', 'Marco Valentino', 'Jordan Meadows'] | 2023-05-21 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 4.28973347e-01 4.61389542e-01 3.67637463e-02 -3.75458628e-01
-4.84211832e-01 -8.87280822e-01 5.93086600e-01 2.31579412e-02
1.79096445e-01 6.08228207e-01 6.99211583e-02 -1.09845734e+00
-1.46121532e-01 -8.36826861e-01 -1.06217885e+00 1.23681709e-01
-2.01428160e-01 3.01476359e-01 1.28019840e-01 -5.25284290... | [9.336137771606445, 7.243639945983887] |
dd8f903a-2e39-4265-bdf0-6de49c401915 | low-rank-optimization-for-efficient-deep | 2303.13635 | null | https://arxiv.org/abs/2303.13635v1 | https://arxiv.org/pdf/2303.13635v1.pdf | Low Rank Optimization for Efficient Deep Learning: Making A Balance between Compact Architecture and Fast Training | Deep neural networks have achieved great success in many data processing applications. However, the high computational complexity and storage cost makes deep learning hard to be used on resource-constrained devices, and it is not environmental-friendly with much power cost. In this paper, we focus on low-rank optimizat... | ['Yipeng Liu', 'Ce Zhu', 'Zhangxin Chen', 'Xinwei Ou'] | 2023-03-22 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 1.58497900e-01 -1.43903628e-01 -4.32320058e-01 -3.71294826e-01
-1.86182842e-01 1.02403481e-02 1.95643172e-01 -6.33418187e-02
-5.42457819e-01 5.58375478e-01 1.84229597e-01 -1.61182228e-02
-5.86815238e-01 -8.04078996e-01 -7.37117469e-01 -9.37421679e-01
-1.13477679e-02 1.74307436e-01 7.77169243e-02 8.75685364... | [8.485584259033203, 3.0683698654174805] |
1b405a27-bf66-4c8e-9fe5-abd39026c768 | conmae-contour-guided-mae-for-unsupervised | 2302.05673 | null | https://arxiv.org/abs/2302.05673v1 | https://arxiv.org/pdf/2302.05673v1.pdf | ConMAE: Contour Guided MAE for Unsupervised Vehicle Re-Identification | Vehicle re-identification is a cross-view search task by matching the same target vehicle from different perspectives. It serves an important role in road-vehicle collaboration and intelligent road control. With the large-scale and dynamic road environment, the paradigm of supervised vehicle re-identification shows lim... | ['Hongke Xu', 'Jianwu Fang', 'Jing Yang'] | 2023-02-11 | null | null | null | null | ['vehicle-re-identification'] | ['computer-vision'] | [-9.12627578e-02 -2.75363535e-01 -3.27964693e-01 -4.26191121e-01
-4.77704853e-01 -2.85504371e-01 6.85173631e-01 -1.60623372e-01
-3.48728031e-01 2.96648145e-01 -1.95847563e-02 -2.05433160e-01
-1.40098843e-03 -7.94346631e-01 -5.85515797e-01 -9.26470935e-01
1.92372695e-01 3.79337370e-01 3.68238777e-01 -9.20358375... | [8.144396781921387, -0.9949756860733032] |
2b912f4a-1d62-4eb0-921f-6bf25a601c94 | sapi-surroundings-aware-vehicle-trajectory | 2306.01812 | null | https://arxiv.org/abs/2306.01812v1 | https://arxiv.org/pdf/2306.01812v1.pdf | SAPI: Surroundings-Aware Vehicle Trajectory Prediction at Intersections | In this work we propose a deep learning model, i.e., SAPI, to predict vehicle trajectories at intersections. SAPI uses an abstract way to represent and encode surrounding environment by utilizing information from real-time map, right-of-way, and surrounding traffic. The proposed model consists of two convolutional netw... | ['Lei Wang', 'Yiqian Gan', 'Hao Xiao', 'Ethan Zhang'] | 2023-06-02 | null | null | null | null | ['trajectory-prediction', 'autonomous-vehicles'] | ['computer-vision', 'computer-vision'] | [-1.88308984e-01 1.61286891e-02 -1.58728153e-01 -6.91768825e-01
-6.53444886e-01 -1.20286651e-01 6.11190736e-01 -6.46345643e-03
-4.19814259e-01 6.91533327e-01 3.07129711e-01 -7.48951375e-01
1.34172529e-01 -1.17570627e+00 -1.14637995e+00 -4.09508139e-01
-4.58760470e-01 2.25550979e-01 5.00895739e-01 -3.01491946... | [6.0944976806640625, 1.2894493341445923] |
e3313ec2-9da4-40d5-a14a-ff5ada42feac | the-ethics-of-ai-in-games | 2305.07392 | null | https://arxiv.org/abs/2305.07392v1 | https://arxiv.org/pdf/2305.07392v1.pdf | The Ethics of AI in Games | Video games are one of the richest and most popular forms of human-computer interaction and, hence, their role is critical for our understanding of human behaviour and affect at a large scale. As artificial intelligence (AI) tools are gradually adopted by the game industry a series of ethical concerns arise. Such conce... | ['Georgios N. Yannakakis', 'Christoffer Holmgård', 'Benedikte Mikkelsen', 'Julian Togelius', 'David Melhart'] | 2023-05-12 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [ 3.22869211e-01 6.60552382e-01 4.41170335e-01 1.62763931e-02
6.41854405e-02 -7.73963094e-01 4.62827951e-01 -3.42928953e-02
-5.23935258e-01 5.27866721e-01 1.76040217e-01 -3.11622322e-01
-4.79116850e-02 -5.92826962e-01 -2.77234256e-01 -3.13839883e-01
2.02928185e-01 -9.92813855e-02 1.19957618e-01 -5.68218946... | [9.135711669921875, 6.445724010467529] |
1e0cfd09-1e61-4842-b564-afe367b20619 | self-explaining-neural-network-with-plausible | 2110.04598 | null | https://arxiv.org/abs/2110.04598v3 | https://arxiv.org/pdf/2110.04598v3.pdf | Self-explaining Neural Network with Concept-based Explanations for ICU Mortality Prediction | Complex deep learning models show high prediction tasks in various clinical prediction tasks but their inherent complexity makes it more challenging to explain model predictions for clinicians and healthcare providers. Existing research on explainability of deep learning models in healthcare have two major limitations:... | ['Andrew Michelson', 'Zachary Abrams', 'Thomas Kannampallil', 'Philip R. O. Payne', 'Sean C. Yu', 'Sayantan Kumar'] | 2021-10-09 | null | null | null | null | ['explainable-models', 'icu-mortality'] | ['computer-vision', 'medical'] | [-1.35057494e-02 9.94319022e-01 -9.26627144e-02 -6.54895902e-01
-1.70448110e-01 -2.78899577e-02 8.51191133e-02 4.97109920e-01
2.03973383e-01 7.62506843e-01 5.41718900e-01 -7.53618717e-01
-5.46565592e-01 -5.08952200e-01 -6.21873617e-01 -2.14076906e-01
-1.53980508e-01 7.87548840e-01 -3.94614458e-01 2.12386344... | [8.341941833496094, 5.987630844116211] |
3377db2d-34b1-4347-bf4a-9f0dbfa77087 | the-past-and-the-present-of-the-color-checker | 1903.04473 | null | http://arxiv.org/abs/1903.04473v1 | http://arxiv.org/pdf/1903.04473v1.pdf | The Past and the Present of the Color Checker Dataset Misuse | The pipelines of digital cameras contain a part for computational color
constancy, which aims to remove the influence of the illumination on the scene
colors. One of the best known and most widely used benchmark datasets for this
problem is the Color Checker dataset. However, due to the improper handling of
the black l... | ['Sven Lon{č}arić', 'Marko Subašić', 'Karlo Koš{č}ević', 'Nikola Banić'] | 2019-03-11 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 5.43972179e-02 -4.11008239e-01 3.62681836e-01 -1.64261848e-01
1.49219513e-01 -6.50737941e-01 6.17714167e-01 -1.24512672e-01
-3.86221647e-01 7.20906496e-01 -2.10422993e-01 -2.93947995e-01
-5.45045696e-02 -5.06800175e-01 -5.45013905e-01 -9.36846912e-01
2.46059895e-01 -5.85906878e-02 6.41147196e-01 -1.50417298... | [10.398168563842773, -2.5140202045440674] |
1f3ec439-6587-4d62-8b35-473cc864ca39 | convolutional-recurrent-neural-networks-for-2 | 1703.05390 | null | http://arxiv.org/abs/1703.05390v3 | http://arxiv.org/pdf/1703.05390v3.pdf | Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting | Keyword spotting (KWS) constitutes a major component of human-technology
interfaces. Maximizing the detection accuracy at a low false alarm (FA) rate,
while minimizing the footprint size, latency and complexity are the goals for
KWS. Towards achieving them, we study Convolutional Recurrent Neural Networks
(CRNNs). Insp... | ['Sercan O. Arik', 'Markus Kliegl', 'Rewon Child', 'Adam Coates', 'Andrew Gibiansky', 'Ryan Prenger', 'Joel Hestness', 'Chris Fougner'] | 2017-03-15 | null | null | null | null | ['small-footprint-keyword-spotting'] | ['speech'] | [ 6.87203333e-02 -2.43731529e-01 -2.78616279e-01 -2.80651003e-01
-7.76672244e-01 -1.94570005e-01 1.75641298e-01 -8.29265043e-02
-5.37155509e-01 3.27730924e-01 1.21701941e-01 -9.93844748e-01
-4.73989733e-03 -3.84160191e-01 -3.93711865e-01 -2.88635433e-01
-1.14750996e-01 -3.90030354e-01 4.87292528e-01 -1.21783182... | [14.354581832885742, 6.223809719085693] |
979ee4ef-278a-4e59-9244-a15590898bd7 | lider-an-efficient-high-dimensional-learned | 2205.00970 | null | https://arxiv.org/abs/2205.00970v3 | https://arxiv.org/pdf/2205.00970v3.pdf | LIDER: An Efficient High-dimensional Learned Index for Large-scale Dense Passage Retrieval | Many recent approaches of passage retrieval are using dense embeddings generated from deep neural models, called "dense passage retrieval". The state-of-the-art end-to-end dense passage retrieval systems normally deploy a deep neural model followed by an approximate nearest neighbor (ANN) search module. The model gener... | ['Daisy Zhe Wang', 'Haodi Ma', 'Yifan Wang'] | 2022-05-02 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-8.90344679e-01 -7.55570710e-01 -3.34721893e-01 1.26381069e-01
-1.22179031e+00 -5.10928452e-01 5.62969387e-01 3.84490043e-01
-7.98149586e-01 3.78173202e-01 6.59706533e-01 -3.75684798e-02
-5.22480428e-01 -1.19976234e+00 -5.06233454e-01 -5.16093373e-01
-8.33107680e-02 1.10479069e+00 4.27056998e-01 -3.79319578... | [11.415264129638672, 7.6404500007629395] |
bbd54393-bedb-48ec-bffc-7d36cdae797a | fba-net-foreground-and-background-aware | 2306.15189 | null | https://arxiv.org/abs/2306.15189v1 | https://arxiv.org/pdf/2306.15189v1.pdf | FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation | Medical image segmentation of gadolinium enhancement magnetic resonance imaging (GE MRI) is an important task in clinical applications. However, manual annotation is time-consuming and requires specialized expertise. Semi-supervised segmentation methods that leverage both labeled and unlabeled data have shown promise, ... | ['Jihun Hamm', 'Nassir Marrouche', 'Chao Huang', 'Chanho Lim', 'Yunsung Chung'] | 2023-06-27 | null | null | null | null | ['contrastive-learning', 'medical-image-segmentation', 'contrastive-learning'] | ['computer-vision', 'medical', 'methodology'] | [ 4.96464759e-01 4.24010932e-01 -2.81149536e-01 -6.19482458e-01
-1.21907926e+00 -4.74354774e-01 2.08886206e-01 2.05434784e-01
-6.45512760e-01 5.01694083e-01 -1.08688198e-01 -4.59190428e-01
1.87428251e-01 -2.66037583e-01 -2.65525669e-01 -9.56125200e-01
-2.39253968e-01 7.99548447e-01 2.11400703e-01 2.41335541... | [14.614500045776367, -2.249267101287842] |
77545e2b-c969-4c55-9f5b-f1c4a7c72f61 | trove-ontology-driven-weak-supervision-for | 2008.01972 | null | https://arxiv.org/abs/2008.01972v2 | https://arxiv.org/pdf/2008.01972v2.pdf | Ontology-driven weak supervision for clinical entity classification in electronic health records | In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e.g. the order of an event relative to a time index) can inform many important analyses. However, creating training data for clinical entity tasks is time consuming and sharing labeled data is challenging... | ['Saelig Khattar', 'Scott L. Fleming', 'Jose Posada', 'Jason A. Fries', 'Ethan Steinberg', 'Nigam H. Shah', 'Alison Callahan'] | 2020-08-05 | null | null | null | null | ['temporal-information-extraction'] | ['natural-language-processing'] | [-6.55195788e-02 3.41444999e-01 -5.39343476e-01 -5.27915776e-01
-7.77640522e-01 -8.46877694e-01 5.14771529e-02 1.28723609e+00
-6.24334753e-01 9.08149242e-01 5.29282510e-01 -6.18981361e-01
-6.61975324e-01 -5.67323029e-01 -4.53238934e-01 -2.02116862e-01
-6.35573447e-01 9.95061636e-01 -1.75742298e-01 2.52425849... | [8.409740447998047, 8.563154220581055] |
e90869d8-00d8-4981-ab49-fca2014acc4c | explan-explaining-black-box-classifiers-using | null | null | https://ieeexplore.ieee.org/document/9206710 | https://ieeexplore.ieee.org/document/9206710 | EXPLAN: Explaining Black-box Classifiers using Adaptive Neighborhood Generation | Defining a representative locality is an urgent challenge in perturbation-based explanation methods, which influences the fidelity and soundness of explanations. We address this issue by proposing a robust and intuitive approach for EXPLaining black-box classifiers using Adaptive Neighborhood generation (EXPLAN). EXPLA... | ['Ingrid Chieh Yu', 'Peyman Rasouli'] | 2020-07-19 | null | null | null | 2020-international-joint-conference-on-neural-1 | ['explainable-models', 'explanation-fidelity-evaluation'] | ['computer-vision', 'methodology'] | [ 3.99766937e-02 5.60753226e-01 -4.07269686e-01 -6.54498637e-01
-2.38430440e-01 -1.63659588e-01 6.80627465e-01 5.58351994e-01
3.65174919e-01 6.88435435e-01 5.21484613e-01 -5.91134250e-01
-8.28479230e-01 -7.05518305e-01 -6.21188223e-01 -5.37785590e-01
6.40200749e-02 4.82892185e-01 -1.04857482e-01 -2.03862697... | [8.76427173614502, 5.6784796714782715] |
6a299f9a-15ba-46c6-a0f0-3085ef747cdc | fusing-motion-patterns-and-key-visual | 2007.06288 | null | https://arxiv.org/abs/2007.06288v1 | https://arxiv.org/pdf/2007.06288v1.pdf | Fusing Motion Patterns and Key Visual Information for Semantic Event Recognition in Basketball Videos | Many semantic events in team sport activities e.g. basketball often involve both group activities and the outcome (score or not). Motion patterns can be an effective means to identify different activities. Global and local motions have their respective emphasis on different activities, which are difficult to capture fr... | ['Qi. Wang', 'Junchi Yan', 'Lifang Wu', 'Zhou Yang', 'Chang Wen Chen', 'Boxuan Zhao', 'Meng Jian'] | 2020-07-13 | null | null | null | null | ['group-activity-recognition'] | ['computer-vision'] | [-1.46201625e-01 -9.10232604e-01 -3.46746266e-01 -6.84438646e-02
-4.19412106e-01 -4.50420618e-01 4.28507298e-01 2.76019778e-02
-4.60807413e-01 3.69911373e-01 2.97654837e-01 4.12215084e-01
-3.33907336e-01 -7.75957584e-01 -3.92920375e-01 -1.03126729e+00
-1.65144980e-01 -9.25834775e-02 3.85586739e-01 2.90546869... | [8.17525577545166, 0.4657060503959656] |
ce41ab97-ba15-4d58-ae07-89cccdc557d7 | automated-fake-news-detection-using-cross | 2201.00083 | null | https://arxiv.org/abs/2201.00083v1 | https://arxiv.org/pdf/2201.00083v1.pdf | Automated Fake News Detection using cross-checking with reliable sources | Over the past decade, fake news and misinformation have turned into a major problem that has impacted different aspects of our lives, including politics and public health. Inspired by natural human behavior, we present an approach that automates the detection of fake news. Natural human behavior is to cross-check new i... | ['Sadegh Raeisi', 'Fakhteh Ghanbarnejad', 'Milad Ranjbar', 'Zahra Ghadiri'] | 2022-01-01 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [-6.79740310e-02 3.85668397e-01 -5.34451485e-01 -3.48232001e-01
-7.53283620e-01 -6.22599661e-01 1.17656016e+00 9.23475266e-01
-3.74253511e-01 8.76538634e-01 2.07801253e-01 -5.47442079e-01
5.83167732e-01 -1.25911856e+00 -8.33481848e-01 -2.76900202e-01
3.39008600e-01 5.75071275e-01 7.08072662e-01 -5.06020784... | [8.16787338256836, 10.234122276306152] |
5a04e966-9f14-49f5-a40a-882d73d6fdfd | gowfed-a-novel-federated-network-intrusion | 2210.16441 | null | https://arxiv.org/abs/2210.16441v2 | https://arxiv.org/pdf/2210.16441v2.pdf | GowFed -- A novel Federated Network Intrusion Detection System | Network intrusion detection systems are evolving into intelligent systems that perform data analysis while searching for anomalies in their environment. Indeed, the development of deep learning techniques paved the way to build more complex and effective threat detection models. However, training those models may be co... | ['Javier Navaridas', 'Jose A. Pascual', 'Aitor Belenguer'] | 2022-10-28 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [-1.43881336e-01 -4.67581004e-02 -4.16209586e-02 -1.62505403e-01
-2.14404181e-01 -7.20463991e-01 7.99119651e-01 3.05502325e-01
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-6.48115277e-01 4.86939818e-01 1.42038718e-01 -1.58213779... | [5.362105369567871, 7.116565227508545] |
a939ab6c-62dc-4170-9389-91f3ca257b80 | snowman-a-million-scale-chinese-commonsense | 2306.10241 | null | https://arxiv.org/abs/2306.10241v1 | https://arxiv.org/pdf/2306.10241v1.pdf | Snowman: A Million-scale Chinese Commonsense Knowledge Graph Distilled from Foundation Model | Constructing commonsense knowledge graphs (CKGs) has attracted wide research attention due to its significant importance in cognitive intelligence. Nevertheless, existing CKGs are typically oriented to English, limiting the research in non-English languages. Meanwhile, the emergence of foundation models like ChatGPT an... | ['Xin Zheng', 'Guanfeng Liu', 'An Liu', 'Zhixu Li', 'Yunlong Liang', 'Jianfeng Qu', 'Jiaan Wang'] | 2023-06-17 | null | null | null | null | ['knowledge-graphs'] | ['knowledge-base'] | [-6.22360855e-02 2.06876859e-01 -2.31368601e-01 -1.34931847e-01
1.44736394e-01 -2.48248458e-01 2.87086397e-01 5.72933070e-02
-4.85175788e-01 8.54817748e-01 2.74048388e-01 -2.90512025e-01
-2.49204621e-01 -1.11988831e+00 -3.82984966e-01 -2.03806728e-01
3.33919019e-01 3.15540254e-01 4.23137486e-01 -7.34257281... | [9.999024391174316, 8.053938865661621] |
da12f079-9725-4730-88b7-2d858483c3cf | three-dimensional-bin-packing-and-mixed-case | null | null | https://pubsonline.informs.org/doi/10.1287/ijoo.2019.0013 | https://pubsonline.informs.org/doi/pdf/10.1287/ijoo.2019.0013 | Three-Dimensional Bin Packing and Mixed-Case Palletization | Despite its wide range of applications, the three-dimensional bin-packing problem is still one of the most difficult optimization problems to solve. Currently, medium- to large-size instances are only solved heuristically and remain out of reach of exact methods. This is particularly true for its practical variant, the... | ['Burak Yildiz', 'Fatma Gzara', 'Samir Elhedhli'] | 2019-07-08 | null | null | null | informs-2019-7 | ['3d-bin-packing'] | ['miscellaneous'] | [ 2.90132291e-03 1.92067574e-03 -6.34057820e-01 -1.60027221e-01
-3.67269307e-01 -5.63762546e-01 -3.80741626e-01 5.36206663e-01
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-8.78465295e-01 -8.94815028e-01 -8.19292724e-01 -6.54822946e-01
-5.26109695e-01 1.06822181e+00 1.32583991e-01 -1.98755026... | [5.091279029846191, 2.81113338470459] |
2062f839-fbcd-4395-9ac2-d1f0ca46ab20 | enhancing-the-spatial-resolution-of-stereo | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Jeon_Enhancing_the_Spatial_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Jeon_Enhancing_the_Spatial_CVPR_2018_paper.pdf | Enhancing the Spatial Resolution of Stereo Images Using a Parallax Prior | We present a novel method that can enhance the spatial resolution of stereo images using a parallax prior. While traditional stereo imaging has focused on estimating depth from stereo images, our method utilizes stereo images to enhance spatial resolution instead of estimating disparity. The critical challenge for enha... | ['Seung-Hwan Baek', 'Min H. Kim', 'Inchang Choi', 'Daniel S. Jeon'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['stereo-image-super-resolution'] | ['computer-vision'] | [ 9.34372246e-01 -3.68201323e-02 -1.19354212e-04 -4.10835505e-01
-7.92108417e-01 -1.76868558e-01 1.97264016e-01 -5.74904442e-01
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3.89249027e-01 -1.94674045e-01 6.43192530e-01 -2.37238668... | [10.008146286010742, -2.398576498031616] |
b31ceddc-8fff-4d67-a159-5861c08e5c74 | learning-normal-form-autoencoders-for-data | 2106.05102 | null | https://arxiv.org/abs/2106.05102v1 | https://arxiv.org/pdf/2106.05102v1.pdf | Learning normal form autoencoders for data-driven discovery of universal,parameter-dependent governing equations | Complex systems manifest a small number of instabilities and bifurcations that are canonical in nature, resulting in universal pattern forming characteristics as a function of some parametric dependence. Such parametric instabilities are mathematically characterized by their universal un-foldings, or normal form dynami... | ['J. Nathan Kutz', 'Christoph Brune', 'Hil G. E. Meijer', 'Steven L. Brunton', 'Manu Kalia'] | 2021-06-09 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [-4.48745996e-01 6.28315564e-03 -3.39096576e-01 6.07203096e-02
1.23272121e-01 -9.23465490e-01 1.02813387e+00 -2.43472531e-01
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-4.11454409e-01 -4.67241108e-01 -7.52214849e-01 -1.08121407e+00
-5.84475040e-01 7.28122711e-01 -2.07177460e-01 -7.40313709... | [6.585069179534912, 3.6192548274993896] |
70e48a75-bfff-4be0-adcd-beba8d793777 | why-does-my-medical-ai-look-at-pictures-of | 2306.17555 | null | https://arxiv.org/abs/2306.17555v1 | https://arxiv.org/pdf/2306.17555v1.pdf | Why does my medical AI look at pictures of birds? Exploring the efficacy of transfer learning across domain boundaries | It is an open secret that ImageNet is treated as the panacea of pretraining. Particularly in medical machine learning, models not trained from scratch are often finetuned based on ImageNet-pretrained models. We posit that pretraining on data from the domain of the downstream task should almost always be preferred inste... | ['Jens Kleesiek', 'Jan Egger', 'Constantin Seibold', 'Michael Kamp', 'Felix Nensa', 'René Hosch', 'Johannes Haubold', 'Janis Evers', 'Enrico Nasca', 'Moon Kim', 'Frederic Jonske'] | 2023-06-30 | null | null | null | null | ['computed-tomography-ct', 'transfer-learning'] | ['methodology', 'miscellaneous'] | [ 4.91807699e-01 4.89374548e-01 -3.62927884e-01 -5.68927526e-01
-1.06116462e+00 -6.63137674e-01 6.52741075e-01 2.13665783e-01
-9.30080175e-01 7.19541311e-01 4.58196253e-01 -3.84156585e-01
-1.03490464e-01 -5.32664895e-01 -6.53899968e-01 -4.62653935e-01
-1.86190769e-01 6.25994325e-01 3.31134260e-01 -2.11365670... | [14.851841926574707, -2.327594757080078] |
1a06373e-f073-4b58-a68c-a31d8d9c9553 | deepkspd-learning-kernel-matrix-based-spd | 1711.04047 | null | http://arxiv.org/abs/1711.04047v1 | http://arxiv.org/pdf/1711.04047v1.pdf | DeepKSPD: Learning Kernel-matrix-based SPD Representation for Fine-grained Image Recognition | Being symmetric positive-definite (SPD), covariance matrix has traditionally
been used to represent a set of local descriptors in visual recognition. Recent
study shows that kernel matrix can give considerably better representation by
modelling the nonlinearity in the local descriptor set. Nevertheless, neither
the des... | ['Lei Wang', 'Xinwang Liu', 'Melih Engin', 'Luping Zhou'] | 2017-11-11 | deepkspd-learning-kernel-matrix-based-spd-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Melih_Engin_DeepKSPD_Learning_Kernel-matrix-based_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Melih_Engin_DeepKSPD_Learning_Kernel-matrix-based_ECCV_2018_paper.pdf | eccv-2018-9 | ['fine-grained-image-recognition'] | ['computer-vision'] | [-1.20658010e-01 -3.41235965e-01 -1.31594375e-01 -5.45159757e-01
-5.70268035e-01 -4.30505604e-01 7.31671035e-01 -2.14953303e-01
-3.37972581e-01 2.36707464e-01 3.82502750e-02 -1.40421987e-01
-7.30779231e-01 -5.67649662e-01 -5.75410903e-01 -1.06440353e+00
-2.87344486e-01 -5.76722734e-02 -2.27126092e-01 -1.05235070... | [8.942140579223633, 2.1675868034362793] |
f56f1ecd-18d1-4e02-ba85-f67358867113 | a-semi-trailer-truck-right-hook-turn-blind | 2303.11223 | null | https://arxiv.org/abs/2303.11223v1 | https://arxiv.org/pdf/2303.11223v1.pdf | A semi-trailer truck right-hook turn blind spot alert system for detecting vulnerable road users using transfer learning | Cycling is an increasingly popular method of transportation for sustainability and health benefits. However, cyclists face growing risks, especially when encountering semi-trailer trucks. This study aims to reduce the number of truck-cyclist collisions, which are often caused by semi-trailer trucks making right-hook tu... | ['Charles Tang'] | 2023-01-16 | null | null | null | null | ['2d-cyclist-detection'] | ['computer-vision'] | [-1.16684824e-01 -2.75862962e-01 -1.52282789e-01 -9.28712860e-02
-6.71577811e-01 -4.99748975e-01 2.99917042e-01 -1.87491789e-01
-7.07368314e-01 1.34406298e-01 -1.36979565e-01 -9.41225469e-01
2.09613871e-02 -5.62328219e-01 -6.62102580e-01 -3.46139014e-01
1.36546522e-01 3.57358605e-01 7.69073784e-01 -2.93271214... | [7.918096542358398, -0.9056409001350403] |
beecf4e9-7000-4cde-a3c7-bcc71b74d946 | interpretable-anomaly-detection-in-cellular | 2306.15938 | null | https://arxiv.org/abs/2306.15938v1 | https://arxiv.org/pdf/2306.15938v1.pdf | Interpretable Anomaly Detection in Cellular Networks by Learning Concepts in Variational Autoencoders | This paper addresses the challenges of detecting anomalies in cellular networks in an interpretable way and proposes a new approach using variational autoencoders (VAEs) that learn interpretable representations of the latent space for each Key Performance Indicator (KPI) in the dataset. This enables the detection of an... | ['Markus Lange-Hegermann', 'Michael Weber', 'Amandeep Singh'] | 2023-06-28 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [-1.12265734e-04 3.48366201e-01 3.03540409e-01 -1.38389692e-01
-6.76046789e-01 -4.29234385e-01 6.48833752e-01 3.83087814e-01
1.09288126e-01 5.70457458e-01 2.84199268e-01 -1.86339572e-01
-8.82432342e-01 -7.96146154e-01 -5.25609910e-01 -1.15147853e+00
-3.59273851e-01 8.62164557e-01 -2.62941808e-01 -2.65253056... | [7.6081743240356445, 2.446704149246216] |
ad5ea8cd-8988-49c3-9ff6-d2ead7d95df7 | joint-texture-and-geometry-optimization-for | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Fu_Joint_Texture_and_Geometry_Optimization_for_RGB-D_Reconstruction_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Fu_Joint_Texture_and_Geometry_Optimization_for_RGB-D_Reconstruction_CVPR_2020_paper.pdf | Joint Texture and Geometry Optimization for RGB-D Reconstruction | Due to inevitable noises and quantization error, the reconstructed 3D models via RGB-D sensors always accompany geometric error and camera drifting, which consequently lead to blurring and unnatural texture mapping results. Most of the 3D reconstruction methods focus on either geometry refinement or texture improvement... | [' Chunxia Xiao', ' Jie Liao', ' Qingan Yan', 'Yanping Fu'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['rgb-d-reconstruction'] | ['computer-vision'] | [ 2.33949184e-01 -2.67600983e-01 3.85545880e-01 -2.88251191e-01
-6.40108049e-01 -3.78405809e-01 2.63806611e-01 -2.03839064e-01
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5.25238924e-02 -8.31888318e-01 -5.85333109e-01 -8.68494987e-01
7.16905415e-01 2.80611575e-01 4.73231912e-01 8.06234106... | [9.299057960510254, -2.8902955055236816] |
daf9e9af-8301-4c3d-9bac-5d4a48132c04 | libris2s-a-german-english-speech-to-speech | 2204.10593 | null | https://arxiv.org/abs/2204.10593v1 | https://arxiv.org/pdf/2204.10593v1.pdf | LibriS2S: A German-English Speech-to-Speech Translation Corpus | Recently, we have seen an increasing interest in the area of speech-to-text translation. This has led to astonishing improvements in this area. In contrast, the activities in the area of speech-to-speech translation is still limited, although it is essential to overcome the language barrier. We believe that one of the ... | ['Jan Niehues', 'Pedro Jeuris'] | 2022-04-22 | null | https://aclanthology.org/2022.lrec-1.98 | https://aclanthology.org/2022.lrec-1.98.pdf | lrec-2022-6 | ['speech-to-text-translation', 'speech-to-speech-translation'] | ['natural-language-processing', 'speech'] | [ 3.37709427e-01 3.98096412e-01 1.05555296e-01 -3.82392555e-01
-1.21900582e+00 -4.81074005e-01 8.36255014e-01 1.28534392e-01
-3.68509024e-01 7.36600697e-01 5.10935426e-01 -4.66204077e-01
3.28312576e-01 -4.81191337e-01 -6.81063771e-01 -4.54749852e-01
3.68519992e-01 5.40673077e-01 2.30814472e-01 -3.74399453... | [14.520392417907715, 6.984955787658691] |
4d5da945-eb18-4c90-adc8-f3290b266827 | extended-u-net-for-speaker-verification-in | 2206.13044 | null | https://arxiv.org/abs/2206.13044v1 | https://arxiv.org/pdf/2206.13044v1.pdf | Extended U-Net for Speaker Verification in Noisy Environments | Background noise is a well-known factor that deteriorates the accuracy and reliability of speaker verification (SV) systems by blurring speech intelligibility. Various studies have used separate pretrained enhancement models as the front-end module of the SV system in noisy environments, and these methods effectively r... | ['Ha-Jin Yu', 'Hye-jin Shim', 'Jungwoo Heo', 'Ju-ho Kim'] | 2022-06-27 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 1.06800549e-01 3.42652127e-02 3.93052965e-01 -5.88362932e-01
-9.99565065e-01 -2.27591693e-01 2.10938588e-01 -6.39037132e-01
-4.54965025e-01 4.74077195e-01 5.52316606e-01 -2.89511621e-01
1.60518289e-01 -1.37133300e-01 -4.15761083e-01 -1.00152743e+00
3.19428325e-01 -3.75206470e-01 -1.35492399e-01 -2.85205543... | [14.794355392456055, 5.976571083068848] |
cfdbe0e0-4ed1-4e4b-9a51-6543864e3ed4 | 2020-cataracts-semantic-segmentation | 2110.10965 | null | https://arxiv.org/abs/2110.10965v2 | https://arxiv.org/pdf/2110.10965v2.pdf | 2020 CATARACTS Semantic Segmentation Challenge | Surgical scene segmentation is essential for anatomy and instrument localization which can be further used to assess tissue-instrument interactions during a surgical procedure. In 2017, the Challenge on Automatic Tool Annotation for cataRACT Surgery (CATARACTS) released 50 cataract surgery videos accompanied by instrum... | ['Da-Han Wang', 'Feihong Huang', 'Joan M. Nunez Do Rio', 'Jeremy Birch', 'Martin Huber', 'Claudio Ravasio', 'Danail Stoyanov', 'Haili Ye', 'Jianyuan Hong', 'Uddhav Vaghela', 'Sophia Bano', 'Pal Halvorsen', 'Michael A. Riegler', 'Debesh Jha', 'Nikhil KumarTomar', 'Fucang Jia', 'Hongyu Chen', 'Christos Bergeles', 'Lyndon... | 2021-10-21 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 1.53028744e-03 3.02981377e-01 -6.58231080e-02 3.58868651e-02
-1.03992951e+00 -1.12254202e+00 1.08472966e-01 -1.59924671e-01
-5.33526897e-01 3.70600760e-01 6.45813942e-01 -5.05162597e-01
1.57292292e-01 1.23277649e-01 -6.76929832e-01 -3.27858299e-01
-3.37092608e-01 2.85443515e-01 1.08820565e-01 2.41321102... | [14.016230583190918, -3.263078212738037] |
98375d3c-b944-4747-a346-0f7a7e861ff2 | enhanced-direct-speech-to-speech-translation | 2204.02967 | null | https://arxiv.org/abs/2204.02967v3 | https://arxiv.org/pdf/2204.02967v3.pdf | Enhanced Direct Speech-to-Speech Translation Using Self-supervised Pre-training and Data Augmentation | Direct speech-to-speech translation (S2ST) models suffer from data scarcity issues as there exists little parallel S2ST data, compared to the amount of data available for conventional cascaded systems that consist of automatic speech recognition (ASR), machine translation (MT), and text-to-speech (TTS) synthesis. In th... | ['Ann Lee', 'Wei-Ning Hsu', 'Jiatao Gu', 'Yossi Adi', 'Juan Pino', 'Changhan Wang', 'Peng-Jen Chen', 'Sravya Popuri'] | 2022-04-06 | null | null | null | null | ['speech-to-text-translation', 'speech-to-speech-translation'] | ['natural-language-processing', 'speech'] | [ 5.85997999e-01 3.43665630e-01 -5.54341972e-01 -4.42404389e-01
-1.63411415e+00 -4.62089658e-01 7.74598837e-01 -3.63130242e-01
-1.26404300e-01 8.47379446e-01 5.78747869e-01 -9.96288121e-01
7.32360721e-01 -2.41488934e-01 -9.00091171e-01 -4.14015085e-01
6.09915912e-01 7.75373936e-01 -2.00663898e-02 -5.00066459... | [14.531233787536621, 7.127433776855469] |
db84d439-64ee-418e-b75e-d75e84eefef3 | prompting-for-multimodal-hateful-meme | 2302.04156 | null | https://arxiv.org/abs/2302.04156v1 | https://arxiv.org/pdf/2302.04156v1.pdf | Prompting for Multimodal Hateful Meme Classification | Hateful meme classification is a challenging multimodal task that requires complex reasoning and contextual background knowledge. Ideally, we could leverage an explicit external knowledge base to supplement contextual and cultural information in hateful memes. However, there is no known explicit external knowledge base... | ['Jing Jiang', 'Wen-Haw Chong', 'Roy Ka-Wei Lee', 'Rui Cao'] | 2023-02-08 | null | null | null | null | ['meme-classification'] | ['natural-language-processing'] | [-2.19672516e-01 -3.94290239e-01 -2.28140861e-01 -8.54982883e-02
-6.07970595e-01 -8.85985792e-01 9.23053324e-01 1.55633271e-01
-4.34506506e-01 6.34780705e-01 6.15822196e-01 -2.49688141e-03
4.04002339e-01 -4.61061835e-01 -2.20598489e-01 -4.54708219e-01
2.41540045e-01 -7.50001241e-03 6.76215887e-02 -5.13778806... | [8.533036231994629, 10.648653984069824] |
eb7d89e1-80f7-4899-8ae7-30c15c321e5b | enabling-data-diversity-efficient-automatic | 2103.16493 | null | https://arxiv.org/abs/2103.16493v1 | https://arxiv.org/pdf/2103.16493v1.pdf | Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training | Data augmentation has proved extremely useful by increasing training data variance to alleviate overfitting and improve deep neural networks' generalization performance. In medical image analysis, a well-designed augmentation policy usually requires much expert knowledge and is difficult to generalize to multiple tasks... | ['Dimitris Metaxas', 'Mu Zhou', 'Zhiqiang Tang', 'Yunhe Gao'] | 2021-03-30 | null | null | null | null | ['skin-cancer-classification'] | ['medical'] | [ 5.23641229e-01 2.94051558e-01 -2.32404664e-01 -4.21151996e-01
-8.52696121e-01 -4.30554628e-01 3.97797853e-01 2.40239903e-01
-5.98665416e-01 5.95661223e-01 -2.54636139e-01 -4.17801827e-01
3.70767325e-01 -5.89168429e-01 -6.52096272e-01 -8.26643646e-01
6.76342174e-02 5.12394369e-01 3.37800756e-02 -1.21899486... | [14.494277000427246, -2.104868173599243] |
8d48e482-675c-4e04-bac5-cd9d80589ea2 | vibertgrid-a-jointly-trained-multi-modal-2d | 2105.11672 | null | https://arxiv.org/abs/2105.11672v1 | https://arxiv.org/pdf/2105.11672v1.pdf | ViBERTgrid: A Jointly Trained Multi-Modal 2D Document Representation for Key Information Extraction from Documents | Recent grid-based document representations like BERTgrid allow the simultaneous encoding of the textual and layout information of a document in a 2D feature map so that state-of-the-art image segmentation and/or object detection models can be straightforwardly leveraged to extract key information from documents. Howeve... | ['Qiang Huo', 'Qin Ren', 'Kai Hu', 'Zhuoyao Zhong', 'Lei Sun', 'Qifang Gao', 'WeiHong Lin'] | 2021-05-25 | null | null | null | null | ['key-information-extraction'] | ['natural-language-processing'] | [ 8.24154448e-03 1.08171873e-01 -1.79003075e-01 -1.68797538e-01
-8.71969342e-01 -8.36967945e-01 9.22205031e-01 4.13954526e-01
-3.18278730e-01 1.63294062e-01 1.35651350e-01 -2.69751191e-01
5.00412937e-03 -1.06976461e+00 -6.55561090e-01 -5.42970657e-01
6.80139363e-02 7.92786717e-01 3.18356961e-01 -5.49828000... | [11.61681079864502, 2.4540212154388428] |
6a5b752a-ef99-4970-9309-68c8bddec54f | blinkflow-a-dataset-to-push-the-limits-of | 2303.07716 | null | https://arxiv.org/abs/2303.07716v1 | https://arxiv.org/pdf/2303.07716v1.pdf | BlinkFlow: A Dataset to Push the Limits of Event-based Optical Flow Estimation | Event cameras provide high temporal precision, low data rates, and high dynamic range visual perception, which are well-suited for optical flow estimation. While data-driven optical flow estimation has obtained great success in RGB cameras, its generalization performance is seriously hindered in event cameras mainly du... | ['Guofeng Zhang', 'Zhaopeng Cui', 'Hujun Bao', 'Hongsheng Li', 'Xiaoyu Shi', 'Shuo Chen', 'Zhaoyang Huang', 'Yijin Li'] | 2023-03-14 | null | null | null | null | ['event-based-optical-flow'] | ['computer-vision'] | [-2.95069814e-01 -7.75935650e-01 1.11523516e-01 -1.10724971e-01
-1.35975331e-01 -5.16308129e-01 5.57203233e-01 -1.15853332e-01
-3.94655406e-01 5.65705419e-01 2.62511194e-01 -4.54758108e-02
-3.67818307e-03 -7.83041120e-01 -4.60857421e-01 -4.53859627e-01
-1.90034062e-01 -4.82828403e-03 5.97028911e-01 -3.93238887... | [8.641474723815918, -1.4310989379882812] |
a0db111c-acf3-4c9d-a277-c85da8b117cb | a-novel-end-to-end-framework-for-occluded | 2304.07721 | null | https://arxiv.org/abs/2304.07721v1 | https://arxiv.org/pdf/2304.07721v1.pdf | A Novel end-to-end Framework for Occluded Pixel Reconstruction with Spatio-temporal Features for Improved Person Re-identification | Person re-identification is vital for monitoring and tracking crowd movement to enhance public security. However, re-identification in the presence of occlusion substantially reduces the performance of existing systems and is a challenging area. In this work, we propose a plausible solution to this problem by developin... | ['Santosh Kumar', 'Satyanarayana Vollala', 'Praneeth Nemani', 'Ghanta Sai Krishna', 'Prathistith Raj Medi'] | 2023-04-16 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 2.75750339e-01 -1.35852158e-01 2.37739339e-01 -2.77707428e-01
-6.23229802e-01 -1.94506109e-01 4.66127783e-01 -2.61180252e-01
-7.25543320e-01 8.55021775e-01 2.18610808e-01 7.54381567e-02
2.35566616e-01 -7.37306237e-01 -8.94965291e-01 -8.93554509e-01
1.51712760e-01 3.25369716e-01 9.06927288e-02 -1.21578053... | [14.636724472045898, 0.9541665315628052] |
86285f61-fcd6-484f-bbe5-344749637c32 | learning-discriminative-data-fitting | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Pan_Learning_Discriminative_Data_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Pan_Learning_Discriminative_Data_ICCV_2017_paper.pdf | Learning Discriminative Data Fitting Functions for Blind Image Deblurring | Solving blind image deblurring usually requires defining a data fitting function and image priors. While existing algorithms mainly focus on developing image priors for blur kernel estimation and non-blind deconvolution, only a few methods consider the effect of data fitting functions. In contrast to the state-of-the-a... | ['Ming-Hsuan Yang', 'Yu-Wing Tai', 'Zhixun Su', 'Jinshan Pan', 'Jiangxin Dong'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['blind-image-deblurring'] | ['computer-vision'] | [ 1.38693765e-01 -5.92303932e-01 1.02435008e-01 -3.54157925e-01
-6.36520863e-01 -5.23844242e-01 4.45358872e-01 -8.13500762e-01
-2.41823316e-01 6.86396897e-01 6.85433149e-01 -3.06485556e-02
-4.53210741e-01 -6.57563210e-02 -5.91138363e-01 -7.90634334e-01
1.53530568e-01 2.76767649e-02 -6.74340129e-02 3.76565546... | [11.622532844543457, -2.7307403087615967] |
571020d3-c52d-4cd5-ae62-701dad1dde40 | attention-based-transformation-from-latent | 2112.05324 | null | https://arxiv.org/abs/2112.05324v1 | https://arxiv.org/pdf/2112.05324v1.pdf | Attention-based Transformation from Latent Features to Point Clouds | In point cloud generation and completion, previous methods for transforming latent features to point clouds are generally based on fully connected layers (FC-based) or folding operations (Folding-based). However, point clouds generated by FC-based methods are usually troubled by outliers and rough surfaces. For folding... | ['Cheng Jin', 'Yuan Wu', 'Ximing Yang', 'Kaiyi Zhang'] | 2021-12-10 | null | null | null | null | ['point-cloud-completion', 'unsupervised-semantic-segmentation', 'point-cloud-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.40224123e-01 -1.64614208e-02 1.33447990e-01 -1.63947240e-01
-2.76893824e-01 -2.99592346e-01 4.28824425e-01 -3.58401276e-02
6.81068108e-04 3.20957989e-01 -3.24476570e-01 6.21451549e-02
-1.54172778e-01 -1.19101095e+00 -8.28201294e-01 -6.52589917e-01
2.17074960e-01 7.03910232e-01 6.10131860e-01 5.16959233... | [8.293785095214844, -3.4513468742370605] |
763efde5-f2a0-4b5a-b8cb-1be4d815df25 | deep-relational-metric-learning | 2108.10026 | null | https://arxiv.org/abs/2108.10026v1 | https://arxiv.org/pdf/2108.10026v1.pdf | Deep Relational Metric Learning | This paper presents a deep relational metric learning (DRML) framework for image clustering and retrieval. Most existing deep metric learning methods learn an embedding space with a general objective of increasing interclass distances and decreasing intraclass distances. However, the conventional losses of metric learn... | ['Jie zhou', 'Jiwen Lu', 'Borui Zhang', 'Wenzhao Zheng'] | 2021-08-23 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zheng_Deep_Relational_Metric_Learning_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zheng_Deep_Relational_Metric_Learning_ICCV_2021_paper.pdf | iccv-2021-1 | ['image-clustering'] | ['computer-vision'] | [-2.12135881e-01 -3.87038946e-01 -4.22669500e-01 -1.05716765e+00
-6.90946043e-01 -4.70644534e-01 3.95288259e-01 2.17822969e-01
-1.33718729e-01 2.66442090e-01 7.83559680e-03 2.50511747e-02
-6.63043976e-01 -1.05167675e+00 -4.66412485e-01 -7.20257223e-01
2.28030272e-02 6.51896894e-01 -5.92123950e-03 1.10805854... | [9.42182731628418, 3.1103851795196533] |
5ca0d601-9528-4e95-a4bd-f5b9c941e851 | impact-of-the-reference-choice-on-scalp-eeg | 1812.00794 | null | http://arxiv.org/abs/1812.00794v2 | http://arxiv.org/pdf/1812.00794v2.pdf | Impact of the reference choice on scalp EEG connectivity estimation | Several scalp EEG functional connectivity studies, mostly clinical, seem to
overlook the reference electrode impact. The subsequent interpretation of brain
connectivity is thus often biased by the choice a non-neutral reference. This
study aims at systematically investigating these effects. As EEG reference, we
examine... | [] | 2019-01-24 | null | null | null | null | ['connectivity-estimation'] | ['graphs'] | [ 2.38905451e-03 -1.24735892e-01 4.71472770e-01 1.58665646e-02
3.08454782e-02 -6.09542012e-01 5.65337300e-01 4.66894001e-01
-5.65004826e-01 7.98227727e-01 1.73533559e-01 -2.47928560e-01
-7.27714956e-01 -6.47841990e-01 -4.71672386e-01 -9.15556431e-01
-4.34198946e-01 1.49420723e-01 1.78195223e-01 -8.45176727... | [12.982353210449219, 3.3796401023864746] |
5cee1e63-dd38-4ec8-97b5-650a653f19c5 | efficient-temporal-sequence-comparison-and | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Zhang_Efficient_Temporal_Sequence_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhang_Efficient_Temporal_Sequence_CVPR_2016_paper.pdf | Efficient Temporal Sequence Comparison and Classification Using Gram Matrix Embeddings on a Riemannian Manifold | In this paper we propose a new framework to compare and classify temporal sequences. The proposed approach captures the underlying dynamics of the data while avoiding expensive estimation procedures, making it suitable to process large numbers of sequences. The main idea is to first embed the sequences into a Riemann... | ['Yin Wang', 'Mario Sznaier', 'Xikang Zhang', 'Octavia Camps', 'Mengran Gou'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['3d-human-action-recognition'] | ['computer-vision'] | [-3.35283652e-02 -1.14941582e-01 -4.83720563e-02 -4.92014289e-02
-1.70271203e-01 -5.49299479e-01 9.12060261e-01 -4.25544567e-02
-5.65388799e-01 3.14106405e-01 2.50299305e-01 1.49779141e-01
-4.10641611e-01 -3.09695929e-01 -4.46167320e-01 -8.90492558e-01
-6.38168991e-01 3.70272309e-01 3.75972629e-01 -3.18599969... | [7.747391223907471, 3.9204232692718506] |
1d8d30e2-67e7-473d-b88a-0ec9f85a95a6 | image-demoireing-with-learnable-bandpass | 2004.00406 | null | https://arxiv.org/abs/2004.00406v1 | https://arxiv.org/pdf/2004.00406v1.pdf | Image Demoireing with Learnable Bandpass Filters | Image demoireing is a multi-faceted image restoration task involving both texture and color restoration. In this paper, we propose a novel multiscale bandpass convolutional neural network (MBCNN) to address this problem. As an end-to-end solution, MBCNN respectively solves the two sub-problems. For texture restoration,... | ['Ales Leonardis', 'Shanxin Yuan', 'Gregory Slabaugh', 'Bolun Zheng'] | 2020-04-01 | image-demoireing-with-learnable-bandpass-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Zheng_Image_Demoireing_with_Learnable_Bandpass_Filters_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zheng_Image_Demoireing_with_Learnable_Bandpass_Filters_CVPR_2020_paper.pdf | cvpr-2020-6 | ['tone-mapping'] | ['computer-vision'] | [ 5.17260134e-01 -4.93037909e-01 2.03277215e-01 -6.53310418e-02
-1.00510228e+00 -2.57633269e-01 2.49250188e-01 -5.36834657e-01
-2.43365765e-01 6.26793265e-01 2.78174281e-01 -2.46220231e-01
3.49575393e-02 -8.68531168e-01 -9.87482488e-01 -9.44653988e-01
3.99353474e-01 -4.60734427e-01 3.30115169e-01 -3.28007579... | [11.07185173034668, -2.182785987854004] |
c6b6654e-aacd-4036-b430-1d47a18d339d | enhance-nerf-multiple-performance-evaluation | 2306.05303 | null | https://arxiv.org/abs/2306.05303v1 | https://arxiv.org/pdf/2306.05303v1.pdf | Enhance-NeRF: Multiple Performance Evaluation for Neural Radiance Fields | The quality of three-dimensional reconstruction is a key factor affecting the effectiveness of its application in areas such as virtual reality (VR) and augmented reality (AR) technologies. Neural Radiance Fields (NeRF) can generate realistic images from any viewpoint. It simultaneously reconstructs the shape, lighting... | ['Baohua Zhang', 'Shuwan Yu', 'Yinling Xie', 'Tao Liu', 'Qianqiu Tan'] | 2023-06-08 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [ 1.58345670e-01 -3.36285442e-01 5.95298231e-01 -1.30078867e-01
-5.18776238e-01 -3.98476720e-01 3.00104827e-01 -5.22411108e-01
-1.55484691e-01 6.14691198e-01 -1.51277140e-01 -4.27615404e-01
3.10346838e-02 -8.92395973e-01 -8.33104908e-01 -6.37434542e-01
1.80283532e-01 -2.62998223e-01 3.12887788e-01 -4.89692122... | [9.738831520080566, -2.9175660610198975] |
215dcfb8-b558-4c82-bd69-12acef4ec972 | when-can-i-speak-predicting-initiation-points | 2208.03812 | null | https://arxiv.org/abs/2208.03812v1 | https://arxiv.org/pdf/2208.03812v1.pdf | When can I Speak? Predicting initiation points for spoken dialogue agents | Current spoken dialogue systems initiate their turns after a long period of silence (700-1000ms), which leads to little real-time feedback, sluggish responses, and an overall stilted conversational flow. Humans typically respond within 200ms and successfully predicting initiation points in advance would allow spoken di... | ['Christopher D. Manning', 'Ashwin Paranjape', 'Siyan Li'] | 2022-08-07 | null | https://aclanthology.org/2022.sigdial-1.22 | https://aclanthology.org/2022.sigdial-1.22.pdf | sigdial-acl-2022-9 | ['spoken-dialogue-systems'] | ['speech'] | [ 1.64871722e-01 5.44915557e-01 1.51209101e-01 -9.22038734e-01
-9.37680304e-01 -8.54403079e-01 8.44036818e-01 9.30278599e-02
-5.81962645e-01 8.52572262e-01 8.35332096e-01 -4.36149299e-01
4.47929889e-01 -2.16286212e-01 1.17341399e-01 -1.68206632e-01
-1.58647284e-01 6.89238369e-01 1.21855512e-01 -7.39287496... | [12.865425109863281, 7.886548042297363] |
194e3a0b-18b8-405f-a008-802637eecc0b | adaptive-convolutional-dictionary-network-for | 2205.07471 | null | https://arxiv.org/abs/2205.07471v2 | https://arxiv.org/pdf/2205.07471v2.pdf | Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction | Inspired by the great success of deep neural networks, learning-based methods have gained promising performances for metal artifact reduction (MAR) in computed tomography (CT) images. However, most of the existing approaches put less emphasis on modelling and embedding the intrinsic prior knowledge underlying this spec... | ['Yefeng Zheng', 'Deyu Meng', 'Yuexiang Li', 'Hong Wang'] | 2022-05-16 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 1.72639966e-01 8.65204334e-02 -2.60055307e-02 -3.15760404e-01
-5.82856476e-01 -5.75217232e-02 2.35176131e-01 1.27301842e-01
-1.61257029e-01 4.39300716e-01 8.60431045e-02 -3.56685936e-01
-4.18919951e-01 -7.62528718e-01 -6.35840535e-01 -7.52564788e-01
8.92800838e-02 1.05887271e-01 2.52161175e-01 -9.83090401... | [13.50600814819336, -2.546527862548828] |
873689a9-fec0-4244-ac57-cb9bf17bbb12 | effective-connectivity-from-single-trial-fmri | 1803.05840 | null | http://arxiv.org/abs/1803.05840v1 | http://arxiv.org/pdf/1803.05840v1.pdf | Effective Connectivity from Single Trial fMRI Data by Sampling Biologically Plausible Models | The estimation of causal network architectures in the brain is fundamental
for understanding cognitive information processes. However, access to the
dynamic processes underlying cognition is limited to indirect measurements of
the hidden neuronal activity, for instance through fMRI data. Thus, estimating
the network st... | [] | 2018-03-15 | null | null | null | null | ['connectivity-estimation'] | ['graphs'] | [ 2.87497818e-01 1.14406459e-01 2.61302620e-01 -1.74353212e-01
2.30015308e-01 -4.20201957e-01 6.08079314e-01 2.16824368e-01
-4.53675419e-01 8.58615279e-01 3.85199040e-01 -3.02452922e-01
-5.96417367e-01 -6.67675436e-01 -6.09230280e-01 -7.42550671e-01
-6.06461525e-01 3.16667497e-01 -2.07595546e-02 6.30372316... | [12.34194564819336, 3.4416794776916504] |
f2dfed4b-f43e-4aaf-b779-023b5c99646a | towards-high-quality-and-efficient-video | 2303.08331 | null | https://arxiv.org/abs/2303.08331v2 | https://arxiv.org/pdf/2303.08331v2.pdf | Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting | As deep convolutional neural networks (DNNs) are widely used in various fields of computer vision, leveraging the overfitting ability of the DNN to achieve video resolution upscaling has become a new trend in the modern video delivery system. By dividing videos into chunks and overfitting each chunk with a super-resolu... | ['Xiaolong Ma', 'Linke Guo', 'Fatemeh Afghah', 'Bin Ren', 'Wei Niu', 'Minghai Qin', 'Jie Ji', 'Gen Li'] | 2023-03-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Towards_High-Quality_and_Efficient_Video_Super-Resolution_via_Spatial-Temporal_Data_Overfitting_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Towards_High-Quality_and_Efficient_Video_Super-Resolution_via_Spatial-Temporal_Data_Overfitting_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-super-resolution'] | ['computer-vision'] | [-6.21424317e-02 -1.58524171e-01 -3.43322545e-01 -2.60136753e-01
-7.35032082e-01 -2.17651546e-01 -1.20558769e-01 -4.74218220e-01
-4.28125888e-01 5.70401013e-01 2.10878581e-01 -2.38275439e-01
2.08510369e-01 -8.81606758e-01 -1.09680188e+00 -5.52962005e-01
-1.43743306e-01 -1.41638964e-01 5.66120505e-01 6.32768199... | [11.04907512664795, -1.7668216228485107] |
55335fb7-58db-4d33-8dcb-f34467cf3f5a | multi-resolution-networks-for-flexible | 1905.00125 | null | http://arxiv.org/abs/1905.00125v1 | http://arxiv.org/pdf/1905.00125v1.pdf | Multi-resolution Networks For Flexible Irregular Time Series Modeling (Multi-FIT) | Missing values, irregularly collected samples, and multi-resolution signals
commonly occur in multivariate time series data, making predictive tasks
difficult. These challenges are especially prevalent in the healthcare domain,
where patients' vital signs and electronic records are collected at different
frequencies an... | ['Bryon Kucharski', 'Akhila Josyula', 'Madalina Fiterau', 'Rheeya Uppaal', 'Bhanu Pratap Singh', 'Iman Deznabi', 'Bharath Narasimhan'] | 2019-04-30 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [ 3.33815396e-01 -1.83605120e-01 -2.33770683e-01 -3.99256736e-01
-5.51214278e-01 -2.05415800e-01 2.01578215e-01 1.33748502e-01
-3.83250713e-02 8.58401537e-01 7.79256523e-01 -1.03286862e-01
-9.38302815e-01 -8.87075782e-01 -5.15076697e-01 -6.79937661e-01
-4.98969615e-01 4.49270606e-01 -3.55619758e-01 -3.99402268... | [7.11093807220459, 3.2410898208618164] |
261ae703-3b05-4f4e-97d9-e53743225c26 | vmav-c-a-deep-attention-based-reinforcement | 1812.09968 | null | http://arxiv.org/abs/1812.09968v1 | http://arxiv.org/pdf/1812.09968v1.pdf | VMAV-C: A Deep Attention-based Reinforcement Learning Algorithm for Model-based Control | Recent breakthroughs in Go play and strategic games have witnessed the great
potential of reinforcement learning in intelligently scheduling in uncertain
environment, but some bottlenecks are also encountered when we generalize this
paradigm to universal complex tasks. Among them, the low efficiency of data
utilization... | ['Qi. Wang', 'Xingxing Liang', 'Zhong Liu', 'Yanghe Feng', 'Jincai Huang'] | 2018-12-24 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-3.44560653e-01 7.46723711e-02 -1.78100228e-01 1.37999654e-01
-3.78285237e-02 -8.63391235e-02 4.48148906e-01 -3.26106876e-01
-6.12044096e-01 1.15164971e+00 1.68518946e-01 -6.24923669e-02
-3.77255470e-01 -8.29414070e-01 -6.59694254e-01 -8.43744338e-01
-2.70268738e-01 7.00872540e-01 2.26608783e-01 -5.75361371... | [3.8850739002227783, 2.0258281230926514] |
fcdc847f-cd52-4592-93bd-ca3391ed35d6 | an-efficient-approach-to-the-online-multi | 2301.04446 | null | https://arxiv.org/abs/2301.04446v1 | https://arxiv.org/pdf/2301.04446v1.pdf | An Efficient Approach to the Online Multi-Agent Path Finding Problem by Using Sustainable Information | Multi-agent path finding (MAPF) is the problem of moving agents to the goal vertex without collision. In the online MAPF problem, new agents may be added to the environment at any time, and the current agents have no information about future agents. The inability of existing online methods to reuse previous planning co... | ['Lujia Wang', 'Ming Liu', 'Hongji Liu', 'Yuanhang Li', 'Boyi Liu', 'Mingkai Tang'] | 2023-01-11 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 6.79054409e-02 1.30242124e-01 -2.61652261e-01 1.45082861e-01
-3.78981620e-01 -7.28900850e-01 3.08925569e-01 4.82199878e-01
-4.61496204e-01 1.40353143e+00 -2.70743757e-01 -3.60938400e-01
-6.56266928e-01 -1.22697186e+00 -4.14113253e-01 -6.57339692e-01
-7.33115673e-01 1.07051039e+00 1.02520454e+00 -2.61551142... | [4.959190845489502, 1.8060814142227173] |
9bbf943e-3eac-4d79-8bd8-246d5ec99f61 | lone-pine-at-semeval-2021-task-5-fine-grained | 2104.03506 | null | https://arxiv.org/abs/2104.03506v1 | https://arxiv.org/pdf/2104.03506v1.pdf | Lone Pine at SemEval-2021 Task 5: Fine-Grained Detection of Hate Speech Using BERToxic | This paper describes our approach to the Toxic Spans Detection problem (SemEval-2021 Task 5). We propose BERToxic, a system that fine-tunes a pre-trained BERT model to locate toxic text spans in a given text and utilizes additional post-processing steps to refine the boundaries. The post-processing steps involve (1) la... | ['Soroush Vosoughi', 'Weicheng Ma', 'Yakoob Khan'] | 2021-04-08 | null | https://aclanthology.org/2021.semeval-1.132 | https://aclanthology.org/2021.semeval-1.132.pdf | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [ 2.43883625e-01 -1.23695277e-01 1.56324446e-01 -1.36970878e-01
-1.25814569e+00 -7.20809937e-01 4.99542117e-01 5.57212710e-01
-6.93109870e-01 6.69052541e-01 1.80371732e-01 -3.78203958e-01
1.48460492e-01 -5.10755062e-01 -8.07663977e-01 -4.96172071e-01
-4.66496386e-02 4.21054900e-01 4.84049797e-01 -7.62289315... | [8.978611946105957, 10.637084007263184] |
9de4d96e-b7e9-4f95-b987-b7e18243298b | teaching-a-new-dog-old-tricks-resurrecting | 1912.13080 | null | https://arxiv.org/abs/1912.13080v1 | https://arxiv.org/pdf/1912.13080v1.pdf | Teaching a New Dog Old Tricks: Resurrecting Multilingual Retrieval Using Zero-shot Learning | While billions of non-English speaking users rely on search engines every day, the problem of ad-hoc information retrieval is rarely studied for non-English languages. This is primarily due to a lack of data set that are suitable to train ranking algorithms. In this paper, we tackle the lack of data by leveraging pre-t... | ['Nazli Goharian', 'Sean MacAvaney', 'Luca Soldaini'] | 2019-12-30 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [-1.54880390e-01 -3.98047537e-01 -2.63286650e-01 -1.95146367e-01
-1.59045541e+00 -7.72073627e-01 1.00617135e+00 4.01523113e-01
-8.73831213e-01 6.96322858e-01 1.83352649e-01 -4.33232278e-01
-1.83632255e-01 -5.45282602e-01 -4.18929189e-01 -2.27597058e-01
-2.79227793e-02 8.80657256e-01 3.94785494e-01 -8.28654051... | [11.380242347717285, 9.821402549743652] |
ba4631e1-51b8-400c-8de5-3864f15e8745 | 3d-densely-convolutional-networks-for-1 | null | null | https://arxiv.org/abs/1709.03199 | https://arxiv.org/pdf/1709.03199.pdf | 3D Densely Convolutional Networks for VolumetricSegmentation | In the isointense stage, the accurate volumetric image segmentation is a challenging task due to the low contrast between tissues. In this paper, we propose a novel very deep network architecture based on densely convolutional network for volumetric brain segmentation. The proposed network architecture provides a dense... | ['Jitae Shin', 'Toan Duc Bui', 'Taesup Moon'] | 2017-09-13 | null | null | null | arxiv-preprint-2017-9 | ['3d-medical-imaging-segmentation', 'infant-brain-mri-segmentation', 'volumetric-medical-image-segmentation'] | ['medical', 'medical', 'medical'] | [ 2.85091419e-02 9.95623842e-02 8.07347819e-02 -6.17621303e-01
-1.81519210e-01 -9.49170440e-03 -2.52680518e-02 2.29077876e-01
-5.29515207e-01 6.39239669e-01 2.55859494e-01 -4.47224230e-02
-1.35285422e-01 -7.87942708e-01 -6.65537179e-01 -5.75024247e-01
-3.78806889e-01 4.76192325e-01 5.29999614e-01 7.78810540... | [14.258218765258789, -2.3778746128082275] |
abd2ca0b-b314-4ac3-aed0-0449781632f3 | surgical-fine-tuning-for-grape-bunch | 2307.00837 | null | https://arxiv.org/abs/2307.00837v1 | https://arxiv.org/pdf/2307.00837v1.pdf | Surgical fine-tuning for Grape Bunch Segmentation under Visual Domain Shifts | Mobile robots will play a crucial role in the transition towards sustainable agriculture. To autonomously and effectively monitor the state of plants, robots ought to be equipped with visual perception capabilities that are robust to the rapid changes that characterise agricultural settings. In this paper, we focus on ... | ['Matteo Matteucci', 'Matteo Gatti', 'Nico Catalano', 'Riccardo Bertoglio', 'Agnese Chiatti'] | 2023-07-03 | null | null | null | null | ['instance-segmentation'] | ['computer-vision'] | [ 5.05146623e-01 1.61814064e-01 -5.60193846e-04 -4.78617907e-01
2.00978950e-01 -1.11331773e+00 1.78550154e-01 4.60552037e-01
-3.99780333e-01 4.55781311e-01 -7.86939561e-01 -4.16523755e-01
-2.08216123e-02 -8.31392705e-01 -8.76326382e-01 -5.34658551e-01
-1.22902304e-01 5.56198359e-01 3.58014345e-01 -6.15849435... | [9.113179206848145, -1.555993914604187] |
369d8d21-0592-419e-b7e8-d1198cb200c0 | utrnet-high-resolution-urdu-text-recognition | 2306.15782 | null | https://arxiv.org/abs/2306.15782v2 | https://arxiv.org/pdf/2306.15782v2.pdf | UTRNet: High-Resolution Urdu Text Recognition In Printed Documents | In this paper, we propose a novel approach to address the challenges of printed Urdu text recognition using high-resolution, multi-scale semantic feature extraction. Our proposed UTRNet architecture, a hybrid CNN-RNN model, demonstrates state-of-the-art performance on benchmark datasets. To address the limitations of p... | ['Chetan Arora', 'Arjun Ghosh', 'Abdur Rahman'] | 2023-06-27 | null | null | null | null | ['optical-character-recognition', 'line-detection'] | ['computer-vision', 'computer-vision'] | [ 2.71892071e-01 -2.32877523e-01 -3.54305357e-02 -1.94170788e-01
-9.73425210e-01 -7.01463640e-01 4.98921782e-01 -1.92312956e-01
-2.02545539e-01 5.41422904e-01 1.45997286e-01 -3.80270034e-01
4.47411776e-01 -6.06572568e-01 -7.86061764e-01 -6.23833761e-02
6.69664383e-01 3.96235466e-01 3.80115509e-01 -1.32535234... | [11.836586952209473, 2.5635507106781006] |
cf414641-c257-438a-b62b-37856a6767af | redditbias-a-real-world-resource-for-bias | 2106.03521 | null | https://arxiv.org/abs/2106.03521v1 | https://arxiv.org/pdf/2106.03521v1.pdf | RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models | Text representation models are prone to exhibit a range of societal biases, reflecting the non-controlled and biased nature of the underlying pretraining data, which consequently leads to severe ethical issues and even bias amplification. Recent work has predominantly focused on measuring and mitigating bias in pretrai... | ['Goran Glavaš', 'Ivan Vulić', 'Anne Lauscher', 'Soumya Barikeri'] | 2021-06-07 | null | https://aclanthology.org/2021.acl-long.151 | https://aclanthology.org/2021.acl-long.151.pdf | acl-2021-5 | ['conversational-response-generation'] | ['natural-language-processing'] | [-5.62804341e-02 5.94152331e-01 -4.96613234e-01 -7.13302433e-01
-1.03872500e-01 -4.82178181e-01 1.10904849e+00 4.75225560e-02
-4.07761812e-01 1.03401101e+00 1.07881367e+00 -2.52244323e-01
2.18677282e-01 -7.32300043e-01 -1.42061099e-01 -4.50557828e-01
5.19380212e-01 8.43763351e-01 -2.31854022e-01 -8.59147847... | [9.283846855163574, 10.14884090423584] |
2373f572-6e0a-4a9c-91f2-03bd120aea19 | segmental-recurrent-neural-networks-for-end | 1603.00223 | null | http://arxiv.org/abs/1603.00223v2 | http://arxiv.org/pdf/1603.00223v2.pdf | Segmental Recurrent Neural Networks for End-to-end Speech Recognition | We study the segmental recurrent neural network for end-to-end acoustic
modelling. This model connects the segmental conditional random field (CRF)
with a recurrent neural network (RNN) used for feature extraction. Compared to
most previous CRF-based acoustic models, it does not rely on an external system
to provide fe... | ['Steve Renals', 'Noah A. Smith', 'Lingpeng Kong', 'Liang Lu', 'Chris Dyer'] | 2016-03-01 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 3.07979822e-01 4.25127983e-01 2.07591400e-01 -6.81930721e-01
-1.17945051e+00 -4.75820154e-01 3.55558366e-01 -1.18999422e-01
-6.99529409e-01 4.74486738e-01 2.77913630e-01 -7.45991588e-01
4.60324019e-01 -4.06764746e-01 -7.29953468e-01 -5.89876950e-01
6.26057833e-02 5.45817137e-01 2.16643900e-01 3.31150517... | [14.471124649047852, 6.7585344314575195] |
1ef2f75d-801a-4fe1-8fbd-127553dfa0ea | child-care-provider-survival-analysis | 2208.02154 | null | https://arxiv.org/abs/2208.02154v1 | https://arxiv.org/pdf/2208.02154v1.pdf | Child Care Provider Survival Analysis | The aggregate ability of child care providers to meet local demand for child care is linked to employment rates in many sectors of the economy. Amid growing concern regarding child care provider sustainability due to the COVID-19 pandemic, state and local governments have received large amounts of new funding to better... | ['Courtney K. Blackwell', 'Maya Schreiber', 'Robert Chapman', 'Herman T. Knopf', 'Phillip Sherlock'] | 2022-08-03 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-2.66666621e-01 3.14804882e-01 -1.04157066e+00 -2.84111053e-01
-4.02884543e-01 -2.24755958e-01 -5.59202991e-02 8.70811343e-01
-1.84766188e-01 4.72755224e-01 6.71251535e-01 -8.84005904e-01
-2.21630290e-01 -6.94681406e-01 -1.85275495e-01 -6.17580056e-01
-3.44613552e-01 4.49198455e-01 -6.38095856e-01 -2.01896176... | [8.022582054138184, 5.518388748168945] |
873a8905-4af2-4a26-88b8-9d0480feefef | reading-like-her-human-reading-inspired | null | null | https://aclanthology.org/D19-1300 | https://aclanthology.org/D19-1300.pdf | Reading Like HER: Human Reading Inspired Extractive Summarization | In this work, we re-examine the problem of extractive text summarization for long documents. We observe that the process of extracting summarization of human can be divided into two stages: 1) a rough reading stage to look for sketched information, and 2) a subsequent careful reading stage to select key sentences to fo... | ['Feiyang Pan', 'Yan Song', 'Xiang Ao', 'Min Yang', 'Qing He', 'Ling Luo'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 5.42447567e-01 3.01095158e-01 -4.62358326e-01 -3.28531116e-01
-1.17185473e+00 -4.81667876e-01 6.37761354e-01 3.62778544e-01
-5.04481792e-01 7.81383991e-01 1.03216636e+00 -4.51931924e-01
-2.27167942e-02 -6.37419879e-01 -6.94432080e-01 -4.03111458e-01
3.03249806e-01 4.48265165e-01 -1.10983581e-03 -1.09788075... | [12.519285202026367, 9.477912902832031] |
3ca71281-b019-473d-8f48-1141a17e2de7 | exploring-grammatical-error-correction-with | null | null | https://aclanthology.org/W12-2005 | https://aclanthology.org/W12-2005.pdf | Exploring Grammatical Error Correction with Not-So-Crummy Machine Translation | null | ['Nitin Madnani', 'Martin Chodorow', 'Joel Tetreault'] | 2012-06-01 | null | null | null | ws-2012-6 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-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... | [-7.261100769042969, 3.6947214603424072] |
033eca93-2765-48c4-ad65-cb69e1dd645d | self-supervised-learning-based-cervical | 2302.05195 | null | https://arxiv.org/abs/2302.05195v2 | https://arxiv.org/pdf/2302.05195v2.pdf | Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime | Screening Papanicolaou test samples has proven to be highly effective in reducing cervical cancer-related mortality. However, the lack of trained cytopathologists hinders its widespread implementation in low-resource settings. Deep learning-based telecytology diagnosis emerges as an appealing alternative, but it requir... | ['Jean-Philippe Thiran', 'Pierre Vassilakos', 'Patrick Petignat', 'Holly Clarke', 'Behzad Bozorgtabar', 'Christian Abbet', 'Thomas Stegmüller'] | 2023-02-10 | null | null | null | null | ['whole-slide-images', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 6.05869949e-01 3.32092911e-01 -4.71879423e-01 4.39148471e-02
-1.56728327e+00 -7.63280272e-01 2.39094257e-01 4.90664005e-01
-5.80348611e-01 1.12639046e+00 -2.52994955e-01 -9.87286210e-01
-1.09586297e-02 -9.63637054e-01 -9.66187119e-01 -1.26915956e+00
3.89420033e-01 6.97691858e-01 4.21171449e-03 2.26783082... | [15.065332412719727, -2.979355573654175] |
e289da9a-0308-4ff0-9cb2-ce4862de1032 | hierarchical-graph-neural-networks-for-causal | 2302.01987 | null | https://arxiv.org/abs/2302.01987v1 | https://arxiv.org/pdf/2302.01987v1.pdf | Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization | In this paper, we propose REASON, a novel framework that enables the automatic discovery of both intra-level (i.e., within-network) and inter-level (i.e., across-network) causal relationships for root cause localization. REASON consists of Topological Causal Discovery and Individual Causal Discovery. The Topological Ca... | ['Haifeng Chen', 'Yanjie Fu', 'Zheng Wang', 'Liang Tong', 'Jingchao Ni', 'Zhengzhang Chen', 'Dongjie Wang'] | 2023-02-03 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [-1.77108452e-01 1.43407151e-01 2.73398142e-02 1.14290588e-01
-6.36033192e-02 -5.78988969e-01 5.05284786e-01 4.44912732e-01
7.61890590e-01 6.07815802e-01 3.71745706e-01 -5.12315571e-01
-1.09151316e+00 -1.13151312e+00 -7.82273591e-01 -4.83112723e-01
-1.02429056e+00 2.16274321e-01 4.52015221e-01 1.54984653... | [7.659582138061523, 5.060122013092041] |
3a5f648d-c58a-4c64-b402-5676c3be0d34 | high-fidelity-audio-compression-with-improved | 2306.06546 | null | https://arxiv.org/abs/2306.06546v1 | https://arxiv.org/pdf/2306.06546v1.pdf | High-Fidelity Audio Compression with Improved RVQGAN | Language models have been successfully used to model natural signals, such as images, speech, and music. A key component of these models is a high quality neural compression model that can compress high-dimensional natural signals into lower dimensional discrete tokens. To that end, we introduce a high-fidelity univers... | ['Kundan Kumar', 'Ishaan Kumar', 'Alejandro Luebs', 'Prem Seetharaman', 'Rithesh Kumar'] | 2023-06-11 | null | null | null | null | ['audio-generation', 'quantization'] | ['audio', 'methodology'] | [ 4.13808912e-01 7.57549107e-02 -2.05182567e-01 -1.64543077e-01
-1.37704587e+00 -3.08128297e-01 4.83873338e-01 -2.09258214e-01
-1.18778192e-01 5.79933763e-01 7.60354042e-01 -4.70619760e-02
2.01831758e-01 -6.36127830e-01 -1.01261175e+00 -4.06763703e-01
-3.67664635e-01 3.50576103e-01 -7.65888765e-02 -1.55467346... | [15.525093078613281, 5.8619384765625] |
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