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8af71914-6910-4ef4-aa6e-588192228c66 | large-scale-unsupervised-person-re | 2105.07914 | null | https://arxiv.org/abs/2105.07914v1 | https://arxiv.org/pdf/2105.07914v1.pdf | Large-Scale Unsupervised Person Re-Identification with Contrastive Learning | Existing public person Re-Identification~(ReID) datasets are small in modern terms because of labeling difficulty. Although unlabeled surveillance video is abundant and relatively easy to obtain, it is unclear how to leverage these footage to learn meaningful ReID representations. In particular, most existing unsupervi... | ['Yin Wang', 'Ming Feng', 'Xinbo Zhao', 'Qiuyu Ren', 'Yan Bai', 'Weiquan Huang'] | 2021-05-17 | null | null | null | null | ['self-supervised-image-classification', 'unsupervised-person-re-identification'] | ['computer-vision', 'computer-vision'] | [ 7.28414133e-02 -3.41902584e-01 -3.51820320e-01 -6.06987178e-01
-8.37939382e-01 -8.24057996e-01 5.25033712e-01 -1.11766763e-01
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1.48514092e-01 4.92186099e-01 2.16491759e-01 4.76296544... | [14.72867488861084, 0.9807615876197815] |
bbdec1c7-59ac-4d2e-8866-e2078b605ffa | vdtr-video-deblurring-with-transformer | 2204.08023 | null | https://arxiv.org/abs/2204.08023v1 | https://arxiv.org/pdf/2204.08023v1.pdf | VDTR: Video Deblurring with Transformer | Video deblurring is still an unsolved problem due to the challenging spatio-temporal modeling process. While existing convolutional neural network-based methods show a limited capacity for effective spatial and temporal modeling for video deblurring. This paper presents VDTR, an effective Transformer-based model that m... | ['Yujiu Yang', 'Jue Wang', 'Yong Zhang', 'Yanbo Fan', 'Mingdeng Cao'] | 2022-04-17 | null | null | null | null | ['video-restoration'] | ['computer-vision'] | [-1.67603850e-01 -8.99539709e-01 -2.91091651e-01 1.36939203e-02
-7.00924158e-01 -2.90047348e-01 3.57582718e-01 -5.87339997e-01
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-7.89187998e-02 -3.67647588e-01 3.96549612e-01 -5.00980914... | [11.318318367004395, -2.286377191543579] |
1eff5b83-b07c-4737-8b71-8d4685230e6d | analysis-of-adversarial-image-manipulations | 2305.06307 | null | https://arxiv.org/abs/2305.06307v1 | https://arxiv.org/pdf/2305.06307v1.pdf | Analysis of Adversarial Image Manipulations | As virtual and physical identity grow increasingly intertwined, the importance of privacy and security in the online sphere becomes paramount. In recent years, multiple news stories have emerged of private companies scraping web content and doing research with or selling the data. Images uploaded online can be scraped ... | ['Michael C. King', 'Gabriella Pangelinan', 'Ahsi Lo'] | 2023-05-10 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 2.22708687e-01 6.57055080e-02 -2.02855691e-01 -4.63609099e-01
-4.25050050e-01 -9.95169044e-01 3.69408131e-01 7.96158537e-02
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9.79047939e-02 -1.56272128e-01 -8.91454220e-02 8.44425410... | [12.814892768859863, 0.9786702990531921] |
8468ed60-8ebc-4090-9e08-3fb683af89f1 | causal-effect-estimation-with-variational | 2304.11969 | null | https://arxiv.org/abs/2304.11969v1 | https://arxiv.org/pdf/2304.11969v1.pdf | Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterion | An essential problem in causal inference is estimating causal effects from observational data. The problem becomes more challenging with the presence of unobserved confounders. When there are unobserved confounders, the commonly used back-door adjustment is not applicable. Although the instrumental variable (IV) method... | ['Kui Yu', 'Lin Liu', 'Jixue Liu', 'Jiuyong Li', 'Debo Cheng', 'Ziqi Xu'] | 2023-04-24 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 1.64416641e-01 4.61638011e-02 -6.06794000e-01 -2.72641271e-01
-6.14397645e-01 -4.25079137e-01 6.97853327e-01 2.76724864e-02
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-1.08934425e-01 5.11412561e-01 -4.59915221e-01 2.19178032... | [8.000360488891602, 5.358336448669434] |
2526b735-7b87-4669-99bf-e35a563d721a | graph-representation-learning-for-audio-music | 1910.11117 | null | https://arxiv.org/abs/1910.11117v1 | https://arxiv.org/pdf/1910.11117v1.pdf | Graph Representation learning for Audio & Music genre Classification | Music genre is arguably one of the most important and discriminative information for music and audio content. Visual representation based approaches have been explored on spectrograms for music genre classification. However, lack of quality data and augmentation techniques makes it difficult to employ deep learning tec... | ['Vasudev Singh', 'Shubham Dokania'] | 2019-10-23 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 9.82437357e-02 -2.09790319e-01 -8.40723068e-02 1.70441747e-01
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-5.45674384e-01 2.78391242e-01 -4.12848502e-01 -3.20157737... | [15.729440689086914, 5.2683258056640625] |
a88422ee-ef49-47b7-a681-4260c8c1aa49 | data-integration-in-systems-genetics-and | 2207.03540 | null | https://arxiv.org/abs/2207.03540v1 | https://arxiv.org/pdf/2207.03540v1.pdf | Data integration in systems genetics and aging research | Human life expectancy has dramatically improved over the course of the last century. Although this reflects a global improvement in sanitation and medical care, this also implies that more people suffer from diseases that typically manifest later in life, like Alzheimer and atherosclerosis. Increasing healthspan by del... | ['Johan Auwerx', 'Maroun Bou Sleiman', 'Alexis Rapin'] | 2022-07-07 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 4.38771933e-01 -3.54014039e-01 -3.00478071e-01 -1.33774072e-01
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-3.54881287e-01 3.44506264e-01 -2.48698860e-01 -1.98818237... | [6.6127777099609375, 5.464190483093262] |
a61e6015-23d7-4872-95f4-d9b9bedde31a | hetergraphlongsum-heterogeneous-graph-neural | null | null | https://aclanthology.org/2022.coling-1.545 | https://aclanthology.org/2022.coling-1.545.pdf | HeterGraphLongSum: Heterogeneous Graph Neural Network with Passage Aggregation for Extractive Long Document Summarization | Graph Neural Network (GNN)-based models have proven effective in various Natural Language Processing (NLP) tasks in recent years. Specifically, in the case of the Extractive Document Summarization (EDS) task, modeling documents under graph structure is able to analyze the complex relations between semantic units (e.g.,... | ['Khac-Hoai Nam Bui', 'Ngoc-Dung Ngoc Nguyen', 'Tuan-Anh Phan'] | null | null | null | null | coling-2022-10 | ['document-summarization'] | ['natural-language-processing'] | [ 3.55833709e-01 5.71516395e-01 -2.10658893e-01 -8.63379799e-03
-4.81179625e-01 -3.21996361e-01 5.75395763e-01 1.05645549e+00
-3.39115620e-01 8.82778645e-01 8.23522508e-01 -1.31440714e-01
-1.52812868e-01 -9.88818586e-01 -5.32714903e-01 -4.91568297e-01
1.03863880e-01 2.75028229e-01 5.29785268e-02 -4.43959057... | [12.645139694213867, 9.568184852600098] |
55fc2324-9ce3-48d5-9e69-6afe1777aa59 | road-damage-detection-based-on-unsupervised | 1910.04988 | null | https://arxiv.org/abs/1910.04988v1 | https://arxiv.org/pdf/1910.04988v1.pdf | Road Damage Detection Based on Unsupervised Disparity Map Segmentation | This paper presents a novel road damage detection algorithm based on unsupervised disparity map segmentation. Firstly, a disparity map is transformed by minimizing an energy function with respect to stereo rig roll angle and road disparity projection model. Instead of solving this energy minimization problem using non-... | ['Rui Fan', 'Ming Liu'] | 2019-10-11 | null | null | null | null | ['road-damage-detection'] | ['computer-vision'] | [ 4.24510568e-01 6.24466687e-04 1.05439387e-01 -1.65131122e-01
-5.19376636e-01 -5.72509281e-02 2.22277455e-02 -1.88514858e-01
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4.83559340e-01 2.46477947e-02 6.14236832e-01 3.60784903... | [9.020442008972168, -2.3682498931884766] |
8792e741-37d9-4329-8c18-12fd21515d1f | hoi4d-a-4d-egocentric-dataset-for-category | 2203.01577 | null | https://arxiv.org/abs/2203.01577v3 | https://arxiv.org/pdf/2203.01577v3.pdf | HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction | We present HOI4D, a large-scale 4D egocentric dataset with rich annotations, to catalyze the research of category-level human-object interaction. HOI4D consists of 2.4M RGB-D egocentric video frames over 4000 sequences collected by 4 participants interacting with 800 different object instances from 16 categories over 6... | ['Zhoujie Fu', 'Weikang Wan', 'Li Yi', 'He Wang', 'Boqiang Liang', 'Hao Shen', 'Kangbo Lyu', 'Che Jiang', 'Yun Liu', 'Yunze Liu'] | 2022-03-03 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_HOI4D_A_4D_Egocentric_Dataset_for_Category-Level_Human-Object_Interaction_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_HOI4D_A_4D_Egocentric_Dataset_for_Category-Level_Human-Object_Interaction_CVPR_2022_paper.pdf | cvpr-2022-1 | ['motion-segmentation'] | ['computer-vision'] | [-1.15504965e-01 -2.66012400e-01 6.54159114e-02 -2.62243629e-01
-3.39626193e-01 -7.42506325e-01 2.62035400e-01 -4.36439991e-01
-3.17761227e-02 3.50275859e-02 5.55730879e-01 5.38985014e-01
-1.21600211e-01 -3.95334154e-01 -8.13984513e-01 -3.55423301e-01
-1.98447794e-01 9.66808259e-01 3.67277950e-01 -2.36357730... | [6.8852338790893555, -0.9579700827598572] |
96274ec7-42e3-4d48-82c6-2103a74c2437 | using-deep-neural-network-for-android-malware | 1904.00736 | null | http://arxiv.org/abs/1904.00736v1 | http://arxiv.org/pdf/1904.00736v1.pdf | Using Deep Neural Network for Android Malware Detection | The pervasiveness of the Android operating system, with the availability of
applications almost for everything, is readily accessible in the official
Google play store or a dozen alternative third-party markets. Additionally, the
vital role of smartphones in modern life leads to store significant information
on devices... | ['Abdelmonim Naway', 'Yuancheng LI'] | 2019-01-16 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [-2.67543048e-02 -3.06889266e-01 -5.23558736e-01 4.34053421e-01
-3.43661815e-01 -9.16042507e-01 5.91679633e-01 -3.83667976e-01
-2.79774386e-02 4.01339084e-01 -3.57621133e-01 -9.52156425e-01
2.78114051e-01 -7.35960126e-01 -6.72602773e-01 -2.59678900e-01
-2.80196499e-02 -8.56606215e-02 7.04505682e-01 -4.47707504... | [14.423316955566406, 9.680148124694824] |
f70cc143-103f-4118-909c-5c704bbdf208 | random-feedback-alignment-algorithms-to-train | 2306.02325 | null | https://arxiv.org/abs/2306.02325v1 | https://arxiv.org/pdf/2306.02325v1.pdf | Random Feedback Alignment Algorithms to train Neural Networks: Why do they Align? | Feedback alignment algorithms are an alternative to backpropagation to train neural networks, whereby some of the partial derivatives that are required to compute the gradient are replaced by random terms. This essentially transforms the update rule into a random walk in weight space. Surprisingly, learning still works... | ['Florian Bacho', 'Dominique Chu'] | 2023-06-04 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 1.08097970e-01 -6.57429844e-02 -1.91765413e-01 -5.59900463e-01
2.54281074e-01 -2.90631056e-01 5.63435316e-01 8.26012120e-02
-6.88151777e-01 8.66828740e-01 -1.45234257e-01 -5.25351644e-01
-1.27878025e-01 -8.42880189e-01 -1.05342233e+00 -8.99669349e-01
-1.40105918e-01 9.64021459e-02 3.44126463e-01 -4.74883765... | [7.893991470336914, 3.5364742279052734] |
47cc4119-ec0b-4463-9e61-c3ffb2674885 | lit-tuned-models-for-efficient-species | 2302.10281 | null | https://arxiv.org/abs/2302.10281v1 | https://arxiv.org/pdf/2302.10281v1.pdf | LiT Tuned Models for Efficient Species Detection | Recent advances in training vision-language models have demonstrated unprecedented robustness and transfer learning effectiveness; however, standard computer vision datasets are image-only, and therefore not well adapted to such training methods. Our paper introduces a simple methodology for adapting any fine-grained i... | ['Chinmay Hegde', 'Benjamin Feuer', 'Andre Nakkab'] | 2023-02-12 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 7.02208996e-01 -3.42304051e-01 -9.54061225e-02 -8.43179896e-02
-7.47807980e-01 -8.24473858e-01 9.20912206e-01 2.29203310e-02
-6.79152906e-01 4.51703131e-01 -2.08328918e-01 -6.71937525e-01
3.63170415e-01 -7.04500794e-01 -1.14592206e+00 -6.90535486e-01
-9.75900795e-03 4.86731052e-01 3.36160779e-01 -3.47476870... | [10.053557395935059, 2.0209317207336426] |
cff263b9-0076-476f-99dc-c406dd09e0b2 | why-having-10000-parameters-in-your-camera-1 | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Schops_Why_Having_10000_Parameters_in_Your_Camera_Model_Is_Better_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Schops_Why_Having_10000_Parameters_in_Your_Camera_Model_Is_Better_CVPR_2020_paper.pdf | Why Having 10,000 Parameters in Your Camera Model Is Better Than Twelve | Camera calibration is an essential first step in setting up 3D Computer Vision systems. Commonly used parametric camera models are limited to a few degrees of freedom and thus often do not optimally fit to complex real lens distortion. In contrast, generic camera models allow for very accurate calibration due to their ... | [' Torsten Sattler', ' Marc Pollefeys', ' Viktor Larsson', 'Thomas Schops'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['stereo-depth-estimation'] | ['computer-vision'] | [-1.38561383e-01 -2.88526993e-02 1.90560278e-02 -3.58105630e-01
-4.36213702e-01 -9.67857420e-01 5.06327569e-01 -2.06094012e-01
-3.39627236e-01 4.44922298e-01 8.08049738e-02 -2.81472892e-01
1.33579627e-01 -4.02975440e-01 -8.55733633e-01 -3.16092640e-01
6.61846399e-01 7.26737022e-01 3.57279003e-01 6.36105612... | [8.237353324890137, -2.3849596977233887] |
e7dde1a7-0c0b-4a22-86cc-d66f2f2e23be | wifi-based-multi-task-sensing | 2111.14619 | null | https://arxiv.org/abs/2111.14619v1 | https://arxiv.org/pdf/2111.14619v1.pdf | WiFi-based Multi-task Sensing | WiFi-based sensing has aroused immense attention over recent years. The rationale is that the signal fluctuations caused by humans carry the information of human behavior which can be extracted from the channel state information of WiFi. Still, the prior studies mainly focus on single-task sensing (STS), e.g., gesture ... | ['Kang Yin', 'Yasong An', 'Chengpei Tang', 'Xie Zhang'] | 2021-11-26 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 3.66034299e-01 -6.48514688e-01 -1.70749605e-01 -3.43344033e-01
-8.81949842e-01 -1.94522202e-01 2.83361793e-01 -6.50518596e-01
-2.79516190e-01 5.28091729e-01 3.18365604e-01 5.25735617e-02
-2.92420298e-01 -3.22481573e-01 -4.27300245e-01 -8.97323489e-01
2.61177123e-01 -2.70417333e-01 2.31773838e-01 2.12396875... | [6.694449424743652, 0.7050118446350098] |
5c14d6f6-4cf4-4517-9954-c0175238fb5a | why-does-chatgpt-fall-short-in-answering | 2304.10513 | null | https://arxiv.org/abs/2304.10513v2 | https://arxiv.org/pdf/2304.10513v2.pdf | Why Does ChatGPT Fall Short in Providing Truthful Answers? | Recent advancements in Large Language Models, such as ChatGPT, have demonstrated significant potential to impact various aspects of human life. However, ChatGPT still faces challenges in aspects like truthfulness, e.g. providing accurate and reliable outputs. Therefore, in this paper, we seek to understand why ChatGPT ... | ['Kevin Chen-Chuan Chang', 'Jie Huang', 'Shen Zheng'] | 2023-04-20 | null | null | null | null | ['memorization', 'specificity', 'open-domain-question-answering'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-3.03507090e-01 5.29608846e-01 9.57085714e-02 -3.20654571e-01
-9.08102512e-01 -6.90526128e-01 2.58044839e-01 3.73432398e-01
9.63983033e-03 8.07703555e-01 3.57214272e-01 -5.66402256e-01
-4.05763507e-01 -9.21804011e-01 -4.79394019e-01 5.97591326e-02
5.10071218e-01 4.42427993e-01 2.53233880e-01 -5.88818371... | [10.797511100769043, 7.893683910369873] |
25d5bc66-a528-4a85-8770-c14a2996bfba | ubernet-training-a-universal-convolutional-1 | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Kokkinos_Ubernet_Training_a_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Kokkinos_Ubernet_Training_a_CVPR_2017_paper.pdf | Ubernet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory | In this work we train in an end-to-end manner a convolutional neural network (CNN) that jointly handles low-, mid-, and high-level vision tasks in a unified architecture. Such a network can act like a `swiss knife' for vision tasks; we call it an "UberNet" to indicate its overarching nature. The main contributio... | ['Iasonas Kokkinos'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['human-part-segmentation'] | ['computer-vision'] | [ 2.86086172e-01 2.11003140e-01 1.53338566e-01 -2.56714404e-01
-7.40341306e-01 -4.44363594e-01 5.74776769e-01 1.83303967e-01
-6.65830433e-01 4.22060430e-01 -2.33315557e-01 -4.30464059e-01
5.28363347e-01 -4.94531959e-01 -1.09788990e+00 -4.07998592e-01
6.49131387e-02 5.13706028e-01 9.11660552e-01 -2.67446607... | [9.44040298461914, 0.15048018097877502] |
6abc75a4-7501-4b75-99ea-6788fd68d74a | stimulating-student-engagement-with-an-ai | 2304.11376 | null | https://arxiv.org/abs/2304.11376v1 | https://arxiv.org/pdf/2304.11376v1.pdf | Stimulating student engagement with an AI board game tournament | Strong foundations in basic AI techniques are key to understanding more advanced concepts. We believe that introducing AI techniques, such as search methods, early in higher education helps create a deeper understanding of the concepts seen later in more advanced AI and algorithms courses. We present a project-based an... | ['Quentin Lurkin', 'Ken Hasselmann'] | 2023-04-22 | null | null | null | null | ['board-games'] | ['playing-games'] | [-2.11462498e-01 1.37776002e-01 3.70358340e-02 -1.55582666e-01
-9.91071761e-02 -4.48035687e-01 1.68469250e-01 4.56559330e-01
-4.23058927e-01 5.17413855e-01 -4.51463073e-01 -7.75811613e-01
-4.65544134e-01 -1.27641356e+00 -3.34924400e-01 -7.79187754e-02
-6.22575358e-02 5.22961617e-01 3.80846649e-01 -1.06863368... | [3.4597327709198, 1.4891256093978882] |
8e18831d-b5db-4047-8019-f2b9ed9e98f1 | gfpose-learning-3d-human-pose-prior-with | 2212.08641 | null | https://arxiv.org/abs/2212.08641v1 | https://arxiv.org/pdf/2212.08641v1.pdf | GFPose: Learning 3D Human Pose Prior with Gradient Fields | Learning 3D human pose prior is essential to human-centered AI. Here, we present GFPose, a versatile framework to model plausible 3D human poses for various applications. At the core of GFPose is a time-dependent score network, which estimates the gradient on each body joint and progressively denoises the perturbed 3D ... | ['Yizhou Wang', 'Fangwei Zhong', 'Hao Dong', 'Xiaoxuan Ma', 'Wentao Zhu', 'Mingdong Wu', 'Hai Ci'] | 2022-12-16 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ci_GFPose_Learning_3D_Human_Pose_Prior_With_Gradient_Fields_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ci_GFPose_Learning_3D_Human_Pose_Prior_With_Gradient_Fields_CVPR_2023_paper.pdf | cvpr-2023-1 | ['multi-hypotheses-3d-human-pose-estimation', '3d-human-pose-estimation', 'monocular-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-6.90187365e-02 1.67924121e-01 9.66406912e-02 -2.56119728e-01
-9.18780267e-01 -3.15068007e-01 4.03891295e-01 -6.80907488e-01
-3.03351969e-01 5.90363681e-01 5.04703045e-01 4.43494499e-01
8.14709887e-02 -2.87276566e-01 -7.86270738e-01 -4.50547338e-01
-9.69872251e-02 9.02824640e-01 4.24623303e-02 -3.86076748... | [7.024331092834473, -0.877502977848053] |
a6ae194d-dafc-4900-bb4c-f02f1b6fb37c | from-alignment-to-entailment-a-unified | 2305.11501 | null | https://arxiv.org/abs/2305.11501v1 | https://arxiv.org/pdf/2305.11501v1.pdf | From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment | Entity Alignment (EA) aims to find the equivalent entities between two Knowledge Graphs (KGs). Existing methods usually encode the triples of entities as embeddings and learn to align the embeddings, which prevents the direct interaction between the original information of the cross-KG entities. Moreover, they encode t... | ['Xiaojie Yuan', 'Haiwei Zhang', 'Ying Zhang', 'Xiangrui Cai', 'Yike Wu', 'Yu Zhao'] | 2023-05-19 | null | null | null | null | ['entity-alignment', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing'] | [-4.92809922e-01 2.73708016e-01 -3.90603662e-01 -3.75363648e-01
-3.72787774e-01 -6.42465711e-01 4.52175707e-01 5.69893479e-01
-4.55752492e-01 2.71788061e-01 4.99609768e-01 -2.10145861e-01
-1.66670710e-01 -1.15368569e+00 -9.00792003e-01 -5.46122372e-01
-1.63059011e-01 6.02038324e-01 8.69285539e-02 -3.80221725... | [8.746574401855469, 7.962504863739014] |
7952ec54-eafd-4187-83f5-84afd90e7e79 | hierarchically-refined-label-attention | 1908.08676 | null | https://arxiv.org/abs/1908.08676v3 | https://arxiv.org/pdf/1908.08676v3.pdf | Hierarchically-Refined Label Attention Network for Sequence Labeling | CRF has been used as a powerful model for statistical sequence labeling. For neural sequence labeling, however, BiLSTM-CRF does not always lead to better results compared with BiLSTM-softmax local classification. This can be because the simple Markov label transition model of CRF does not give much information gain ove... | ['Yue Zhang', 'Leyang Cui'] | 2019-08-23 | hierarchically-refined-label-attention-1 | https://aclanthology.org/D19-1422 | https://aclanthology.org/D19-1422.pdf | ijcnlp-2019-11 | ['ccg-supertagging'] | ['natural-language-processing'] | [ 1.31978944e-01 3.35695744e-01 -4.49423760e-01 -6.71307385e-01
-6.87205255e-01 -6.23418093e-01 3.55719626e-01 2.82417685e-01
-6.82238281e-01 9.97585177e-01 4.24882203e-01 -4.23064351e-01
6.98022306e-01 -4.84030157e-01 -3.66966546e-01 -7.71755338e-01
-7.11196510e-04 4.70779717e-01 2.52795577e-01 9.35301483... | [10.010204315185547, 9.743517875671387] |
8633667e-dcae-4ac4-87ac-652e08c40a53 | multi-label-zero-shot-learning-with | 1711.06526 | null | http://arxiv.org/abs/1711.06526v2 | http://arxiv.org/pdf/1711.06526v2.pdf | Multi-Label Zero-Shot Learning with Structured Knowledge Graphs | In this paper, we propose a novel deep learning architecture for multi-label
zero-shot learning (ML-ZSL), which is able to predict multiple unseen class
labels for each input instance. Inspired by the way humans utilize semantic
knowledge between objects of interests, we propose a framework that
incorporates knowledge ... | ['Yu-Chiang Frank Wang', 'Chih-Kuan Yeh', 'Chung-Wei Lee', 'Wei Fang'] | 2017-11-17 | multi-label-zero-shot-learning-with-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Lee_Multi-Label_Zero-Shot_Learning_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Lee_Multi-Label_Zero-Shot_Learning_CVPR_2018_paper.pdf | cvpr-2018-6 | ['multi-label-zero-shot-learning'] | ['computer-vision'] | [ 3.56486470e-01 3.80672872e-01 -3.22585911e-01 -5.99585354e-01
-2.60524631e-01 -3.99299145e-01 6.88132226e-01 6.09099925e-01
-1.45272702e-01 3.53754967e-01 3.68595943e-02 -8.64618644e-02
-3.41853827e-01 -1.13339293e+00 -5.53519130e-01 -3.48200470e-01
3.15837920e-01 7.08071351e-01 5.11412621e-01 7.04054758... | [10.055002212524414, 2.503145217895508] |
9cf6c0f1-3fd0-448a-8432-21f1c16ebc67 | hard-samples-rectification-for-unsupervised | 2106.07204 | null | https://arxiv.org/abs/2106.07204v1 | https://arxiv.org/pdf/2106.07204v1.pdf | Hard Samples Rectification for Unsupervised Cross-domain Person Re-identification | Person re-identification (re-ID) has received great success with the supervised learning methods. However, the task of unsupervised cross-domain re-ID is still challenging. In this paper, we propose a Hard Samples Rectification (HSR) learning scheme which resolves the weakness of original clustering-based methods being... | ['Shao-Yi Chien', 'Tsai-Shien Chen', 'Man-Yu Lee', 'Chih-Ting Liu'] | 2021-06-14 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 3.39960814e-01 -9.83737931e-02 -1.49251848e-01 -3.93815517e-01
-6.30039275e-01 -2.80541390e-01 7.23653376e-01 -1.69124767e-01
-3.46219122e-01 6.96951270e-01 2.52925336e-01 3.36170256e-01
-1.48677185e-01 -3.97332460e-01 -3.59689236e-01 -8.58997226e-01
3.60940427e-01 7.39043236e-01 1.69749185e-01 -1.82065219... | [14.788588523864746, 1.1114357709884644] |
4af620f6-15d0-4824-bb79-0526150eb70d | using-contextual-information-to-improve-blood | 1909.01735 | null | https://arxiv.org/abs/1909.01735v1 | https://arxiv.org/pdf/1909.01735v1.pdf | Using Contextual Information to Improve Blood Glucose Prediction | Blood glucose value prediction is an important task in diabetes management. While it is reported that glucose concentration is sensitive to social context such as mood, physical activity, stress, diet, alongside the influence of diabetes pathologies, we need more research on data and methodologies to incorporate and ev... | ['Rumi Chunara', 'Mohammad Akbari'] | 2019-08-24 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [ 4.01180208e-01 -2.53652275e-01 -4.58531380e-01 -1.04621100e+00
-6.45365596e-01 -1.98541105e-01 6.22357368e-01 7.61159420e-01
-3.41325343e-01 9.66847718e-01 9.27847087e-01 1.26586467e-01
-3.72960806e-01 -1.11129081e+00 -4.02495563e-01 -7.97670007e-01
-2.51235306e-01 2.69156337e-01 -2.28912517e-01 2.56568760... | [13.62446403503418, 3.2205724716186523] |
253f9582-c8ed-43a9-8c81-71c3290afa0d | solving-diffusion-odes-with-optimal-boundary | 2305.15357 | null | https://arxiv.org/abs/2305.15357v2 | https://arxiv.org/pdf/2305.15357v2.pdf | Solving Diffusion ODEs with Optimal Boundary Conditions for Better Image Super-Resolution | Diffusion models, as a kind of powerful generative model, have given impressive results on image super-resolution (SR) tasks. However, due to the randomness introduced in the reverse process of diffusion models, the performances of diffusion-based SR models are fluctuating at every time of sampling, especially for samp... | ['Jiaying Liu', 'Jianlong Fu', 'Wenhan Yang', 'Huan Yang', 'Yiyang Ma'] | 2023-05-24 | null | null | null | null | ['image-super-resolution', 'efficient-exploration'] | ['computer-vision', 'methodology'] | [ 2.54960746e-01 1.88113824e-01 5.74296713e-03 2.07665339e-01
-7.89759099e-01 -1.28131881e-01 8.12446177e-01 -4.36419994e-01
-1.49432212e-01 8.15501273e-01 2.00590163e-01 1.15797698e-01
-3.97807419e-01 -9.73871827e-01 -3.83775562e-01 -1.03971171e+00
-1.17375039e-01 4.93147850e-01 4.99355555e-01 -3.53747964... | [11.39459228515625, -2.009979724884033] |
efb03827-aa7a-47de-96c8-02092c42a004 | melanoma-detection-using-adversarial-training | 2004.06824 | null | https://arxiv.org/abs/2004.06824v2 | https://arxiv.org/pdf/2004.06824v2.pdf | Melanoma Detection using Adversarial Training and Deep Transfer Learning | Skin lesion datasets consist predominantly of normal samples with only a small percentage of abnormal ones, giving rise to the class imbalance problem. Also, skin lesion images are largely similar in overall appearance owing to the low inter-class variability. In this paper, we propose a two-stage framework for automat... | ['A. Ben Hamza', 'Hasib Zunair'] | 2020-04-14 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 9.20091212e-01 2.20362023e-01 -2.11655736e-01 -3.06968421e-01
-1.03919888e+00 -4.51151192e-01 4.61658895e-01 3.08931470e-01
-4.66952115e-01 6.75913393e-01 -2.59504080e-01 -1.12013690e-01
1.07119605e-01 -7.28777707e-01 -6.47087812e-01 -1.16791296e+00
3.39481175e-01 2.39904955e-01 1.55446799e-02 6.85570985... | [15.498924255371094, -2.7835607528686523] |
6c6eecd8-fb37-420e-8244-9085c6cd6a2e | time-discretization-invariant-safe-action | 2111.03941 | null | https://arxiv.org/abs/2111.03941v6 | https://arxiv.org/pdf/2111.03941v6.pdf | Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods | In reinforcement learning, continuous time is often discretized by a time scale $\delta$, to which the resulting performance is known to be highly sensitive. In this work, we seek to find a $\delta$-invariant algorithm for policy gradient (PG) methods, which performs well regardless of the value of $\delta$. We first i... | ['Gunhee Kim', 'Jaekyeom Kim', 'Seohong Park'] | 2021-11-06 | null | http://proceedings.neurips.cc/paper/2021/hash/024677efb8e4aee2eaeef17b54695bbe-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/024677efb8e4aee2eaeef17b54695bbe-Paper.pdf | neurips-2021-12 | ['policy-gradient-methods'] | ['methodology'] | [-5.28226839e-03 -5.41820168e-01 -1.57944351e-01 -7.35297054e-02
-6.96159363e-01 -7.12468565e-01 5.64352393e-01 -2.21416295e-01
-8.76742005e-01 1.06285417e+00 -3.06487501e-01 -4.60397571e-01
-2.33960748e-01 -7.22117782e-01 -6.22719705e-01 -8.61315668e-01
-4.49167788e-01 4.70167771e-02 3.54914486e-01 -4.43406641... | [4.0578718185424805, 2.2182810306549072] |
f78ae9a9-38c7-46d8-ada5-1cffc3ac6542 | learning-in-a-single-domain-for-non | 2305.06200 | null | https://arxiv.org/abs/2305.06200v1 | https://arxiv.org/pdf/2305.06200v1.pdf | Learning in a Single Domain for Non-Stationary Multi-Texture Synthesis | This paper aims for a new generation task: non-stationary multi-texture synthesis, which unifies synthesizing multiple non-stationary textures in a single model. Most non-stationary textures have large scale variance and can hardly be synthesized through one model. To combat this, we propose a multi-scale generator to ... | ['Zhen Zhu', 'Zhiliang Xu', 'Zijie Wu', 'Xudong Xie'] | 2023-05-10 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 6.26228034e-01 -3.31687629e-01 -3.74056511e-02 -9.34331398e-03
-7.84691155e-01 -5.82615793e-01 4.48648095e-01 -2.60653794e-01
2.18431681e-01 6.28011763e-01 -3.09481651e-01 2.20250543e-02
1.68656521e-02 -7.36217499e-01 -7.27250099e-01 -1.04134774e+00
3.47915292e-01 4.25461948e-01 5.12216747e-01 -3.57239008... | [11.524518013000488, -0.6496699452400208] |
dda7b8dd-56bf-4993-86a1-217e9d4e3977 | topological-data-analysis-guided-segment | 2306.17400 | null | https://arxiv.org/abs/2306.17400v1 | https://arxiv.org/pdf/2306.17400v1.pdf | Topological Data Analysis Guided Segment Anything Model Prompt Optimization for Zero-Shot Segmentation in Biological Imaging | Emerging foundation models in machine learning are models trained on vast amounts of data that have been shown to generalize well to new tasks. Often these models can be prompted with multi-modal inputs that range from natural language descriptions over images to point clouds. In this paper, we propose topological data... | ['Shusen Liu', 'Ruben Glatt'] | 2023-06-30 | null | null | null | null | ['zero-shot-segmentation', 'topological-data-analysis'] | ['computer-vision', 'graphs'] | [ 1.17822565e-01 1.55724529e-02 2.09745049e-01 -4.26481366e-01
-6.23130500e-01 -8.02675784e-01 8.53904009e-01 5.88005602e-01
-4.97948736e-01 6.23172581e-01 -2.82499075e-01 -3.78679484e-01
-3.90559733e-01 -7.65254498e-01 -7.16709197e-01 -5.26817501e-01
-2.12350264e-01 1.18610871e+00 6.57271802e-01 -2.31270775... | [8.104736328125, -3.0969181060791016] |
48c3a1e3-c7b0-4903-9207-e996e72758fa | deanet-decomposition-enhancement-and | 2209.06823 | null | https://arxiv.org/abs/2209.06823v1 | https://arxiv.org/pdf/2209.06823v1.pdf | DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement | Images obtained under low-light conditions will seriously affect the quality of the images. Solving the problem of poor low-light image quality can effectively improve the visual quality of images and better improve the usability of computer vision. In addition, it has very important applications in many fields. This p... | ['Hongbing Ma', 'Yuan Xue', 'Liangliang Li', 'Yonglong Jiang'] | 2022-09-14 | null | null | null | null | ['low-light-image-enhancement'] | ['computer-vision'] | [ 1.34311348e-01 -7.68674552e-01 2.08479747e-01 -2.46168926e-01
-1.65452898e-01 -2.98011992e-02 2.09552079e-01 -4.67879057e-01
-5.20430088e-01 6.67903185e-01 1.27639815e-01 -6.21488988e-02
2.42213309e-01 -1.01121354e+00 -4.62335825e-01 -1.10960317e+00
4.87452745e-01 -7.37983167e-01 3.67755085e-01 -4.54581857... | [10.809139251708984, -2.470363140106201] |
2f5f1e69-2971-475a-aa67-9f8987ea4002 | excalibr-expected-calibration-of | 2304.12311 | null | https://arxiv.org/abs/2304.12311v1 | https://arxiv.org/pdf/2304.12311v1.pdf | ExCalibR: Expected Calibration of Recommendations | In many recommender systems and search problems, presenting a well balanced set of results can be an important goal in addition to serving highly relevant content. For example, in a movie recommendation system, it may be helpful to achieve a certain balance of different genres, likewise, it may be important to balance ... | ['Pannagadatta Shivaswamy'] | 2023-04-24 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-3.38485204e-02 -3.26034054e-02 -3.16679686e-01 -6.37315452e-01
-8.01889420e-01 -6.42781198e-01 4.63408887e-01 6.28901199e-02
-4.53036070e-01 7.55553305e-01 2.45322093e-01 -3.05180937e-01
-6.94830537e-01 -6.36514843e-01 -5.54652989e-01 -8.16933036e-01
6.44247383e-02 5.99426925e-01 1.10420123e-01 -6.15941584... | [9.666354179382324, 5.601218223571777] |
accdf14d-e04a-44a7-8ab1-ead42cfe1843 | multimodal-learning-using-optimal-transport | 2110.10949 | null | https://arxiv.org/abs/2110.10949v1 | https://arxiv.org/pdf/2110.10949v1.pdf | Multimodal Learning using Optimal Transport for Sarcasm and Humor Detection | Multimodal learning is an emerging yet challenging research area. In this paper, we deal with multimodal sarcasm and humor detection from conversational videos and image-text pairs. Being a fleeting action, which is reflected across the modalities, sarcasm detection is challenging since large datasets are not available... | ['Vishal M. Patel', 'Aniket Roy', 'Shraman Pramanick'] | 2021-10-21 | null | null | null | null | ['humor-detection'] | ['natural-language-processing'] | [ 7.78173581e-02 -1.79044485e-01 -8.93528312e-02 -3.68081070e-02
-9.81857657e-01 -3.01569283e-01 6.90447867e-01 -1.27671743e-02
-2.96871454e-01 4.53254938e-01 6.15353882e-01 3.29992175e-01
4.08920556e-01 6.55402169e-02 -4.01432395e-01 -6.55227304e-01
3.52257639e-01 -4.58677337e-02 5.51381242e-03 -3.44070703... | [13.091118812561035, 5.1366167068481445] |
40fe4ec6-513f-4e21-a4c5-38b23643b091 | deep-decomposition-and-bilinear-pooling | 2205.05880 | null | https://arxiv.org/abs/2205.05880v2 | https://arxiv.org/pdf/2205.05880v2.pdf | Deep Decomposition and Bilinear Pooling Network for Blind Night-Time Image Quality Evaluation | Blind image quality assessment (BIQA), which aims to accurately predict the image quality without any pristine reference information, has been extensively concerned in the past decades. Especially, with the help of deep neural networks, great progress has been achieved. However, it remains less investigated on BIQA for... | ['Xiongkuo Min', 'Wei Zhou', 'Yudong Mao', 'Guangtao Zhai', 'Jiawu Xu', 'Qiuping Jiang'] | 2022-05-12 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [-1.78768989e-02 -7.30997562e-01 4.19112235e-01 -3.44720155e-01
-7.07962215e-01 -1.77330375e-01 4.50458884e-01 -2.54977763e-01
-1.74394682e-01 5.96881509e-01 3.95598441e-01 -1.76880304e-02
-1.32284462e-01 -7.31569111e-01 -5.00378132e-01 -1.15113997e+00
1.22176759e-01 -4.66295987e-01 7.77175277e-02 -2.02427745... | [11.82862377166748, -1.887738585472107] |
64c707f6-e4b7-406e-8611-c6fdb4817121 | image-search-with-text-feedback-by | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Image_Search_With_Text_Feedback_by_Visiolinguistic_Attention_Learning_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_Image_Search_With_Text_Feedback_by_Visiolinguistic_Attention_Learning_CVPR_2020_paper.pdf | Image Search With Text Feedback by Visiolinguistic Attention Learning | Image search with text feedback has promising impacts in various real-world applications, such as e-commerce and internet search. Given a reference image and text feedback from user, the goal is to retrieve images that not only resemble the input image, but also change certain aspects in accordance with the given text.... | [' Loris Bazzani', ' Shaogang Gong', 'Yanbei Chen'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 2.61998713e-01 -4.37654525e-01 -2.19204843e-01 -4.70238149e-01
-4.12557423e-01 -6.14467204e-01 6.12011850e-01 1.21262342e-01
-4.54895794e-01 2.86813706e-01 4.57375139e-01 -1.24898620e-01
-8.38495418e-02 -5.47472656e-01 -8.24243963e-01 -3.98751229e-01
6.66653693e-01 1.45039737e-01 2.13173658e-01 -4.64614362... | [10.782570838928223, 1.3958505392074585] |
31ef3674-6cf2-434a-a240-a9802ae08dc6 | a-morphable-face-albedo-model | 2004.02711 | null | https://arxiv.org/abs/2004.02711v2 | https://arxiv.org/pdf/2004.02711v2.pdf | A Morphable Face Albedo Model | In this paper, we bring together two divergent strands of research: photometric face capture and statistical 3D face appearance modelling. We propose a novel lightstage capture and processing pipeline for acquiring ear-to-ear, truly intrinsic diffuse and specular albedo maps that fully factor out the effects of illumin... | ['Joshua Tenenbaum', 'William A. P. Smith', 'Bernard Tiddeman', 'Alassane Seck', 'Hannah Dee', 'Bernhard Egger'] | 2020-04-06 | a-morphable-face-albedo-model-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Smith_A_Morphable_Face_Albedo_Model_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Smith_A_Morphable_Face_Albedo_Model_CVPR_2020_paper.pdf | cvpr-2020-6 | ['art-analysis'] | ['computer-vision'] | [ 3.86214942e-01 -3.20535935e-02 6.56672776e-01 -5.93891203e-01
-5.74201286e-01 -3.50116044e-01 4.84926552e-01 -5.87200701e-01
1.29594862e-01 1.45595878e-01 2.98392996e-02 4.90178429e-02
1.48143157e-01 -4.74852860e-01 -7.54388511e-01 -8.44367385e-01
4.18449044e-01 6.93123698e-01 -1.16803301e-02 -1.89750269... | [12.75777530670166, -0.34688320755958557] |
e00368e7-fbfd-484b-943b-6f1cb554465f | an-end-to-end-neural-network-for-polyphonic | 1508.01774 | null | http://arxiv.org/abs/1508.01774v2 | http://arxiv.org/pdf/1508.01774v2.pdf | An End-to-End Neural Network for Polyphonic Piano Music Transcription | We present a supervised neural network model for polyphonic piano music
transcription. The architecture of the proposed model is analogous to speech
recognition systems and comprises an acoustic model and a music language model.
The acoustic model is a neural network used for estimating the probabilities of
pitches in ... | ['Simon Dixon', 'Emmanouil Benetos', 'Siddharth Sigtia'] | 2015-08-07 | null | null | null | null | ['music-transcription'] | ['music'] | [ 2.41943806e-01 -1.68524116e-01 6.82240278e-02 -2.76471496e-01
-7.41245031e-01 -3.73568535e-01 3.99512738e-01 -2.47890621e-01
-3.47951829e-01 1.81392461e-01 3.30631524e-01 -2.45154887e-01
-3.08060735e-01 -6.44262195e-01 -5.75717390e-01 -7.91028559e-01
-2.35838354e-01 4.34150726e-01 1.65321872e-01 -2.34719533... | [15.619244575500488, 5.5186543464660645] |
5d55226d-cd6c-4ac6-ae36-7245a79e1ec2 | on-regularization-and-inference-with-label | 2307.03886 | null | https://arxiv.org/abs/2307.03886v1 | https://arxiv.org/pdf/2307.03886v1.pdf | On Regularization and Inference with Label Constraints | Prior knowledge and symbolic rules in machine learning are often expressed in the form of label constraints, especially in structured prediction problems. In this work, we compare two common strategies for encoding label constraints in a machine learning pipeline, regularization with constraints and constrained inferen... | ['Dan Roth', 'Piyush Kumar', 'Tin D. Nguyen', 'Hangfeng He', 'Kaifu Wang'] | 2023-07-08 | null | null | null | null | ['structured-prediction'] | ['methodology'] | [ 5.62418580e-01 8.92215967e-01 -4.73149508e-01 -6.69491231e-01
-4.91264939e-01 -5.54175496e-01 5.19898653e-01 4.41436410e-01
-3.87399107e-01 7.16914654e-01 2.98209310e-01 -4.62276280e-01
-3.72346133e-01 -6.16417825e-01 -8.77155900e-01 -3.79751861e-01
3.55369717e-01 4.37139034e-01 3.04268152e-01 2.90919214... | [8.79807186126709, 6.30372428894043] |
65ec636d-f79b-40fd-87e9-fd64c6cf9e20 | end-to-end-compressed-video-representation | 2203.15336 | null | https://arxiv.org/abs/2203.15336v1 | https://arxiv.org/pdf/2203.15336v1.pdf | End-to-End Compressed Video Representation Learning for Generic Event Boundary Detection | Generic event boundary detection aims to localize the generic, taxonomy-free event boundaries that segment videos into chunks. Existing methods typically require video frames to be decoded before feeding into the network, which demands considerable computational power and storage space. To that end, we propose a new en... | ['Libo Zhang', 'Tiejian Luo', 'Dexiang Hong', 'Longyin Wen', 'Xinyao Wang', 'CongCong Li'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_End-to-End_Compressed_Video_Representation_Learning_for_Generic_Event_Boundary_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_End-to-End_Compressed_Video_Representation_Learning_for_Generic_Event_Boundary_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['boundary-detection'] | ['computer-vision'] | [ 2.29582474e-01 -2.81415850e-01 -1.49242491e-01 -2.21116111e-01
-5.40059209e-01 -2.81474620e-01 4.82624024e-02 -7.90134296e-02
-4.76157069e-01 3.57086241e-01 1.75528854e-01 1.04422241e-01
2.57510871e-01 -5.04591763e-01 -8.25989723e-01 -7.33227849e-01
-5.22784829e-01 -4.37798321e-01 5.80308080e-01 4.01910782... | [8.813159942626953, 0.26419568061828613] |
3abab6ce-f14a-4493-8bdf-4182183f67ec | dsrn-an-efficient-deep-network-for-image | 2102.09242 | null | https://arxiv.org/abs/2102.09242v2 | https://arxiv.org/pdf/2102.09242v2.pdf | DSRN: an Efficient Deep Network for Image Relighting | Custom and natural lighting conditions can be emulated in images of the scene during post-editing. Extraordinary capabilities of the deep learning framework can be utilized for such purpose. Deep image relighting allows automatic photo enhancement by illumination-specific retouching. Most of the state-of-the-art method... | ['Himanshu Kumar', 'Saikat Dutta', 'Nisarg A. Shah', 'Sourya Dipta Das'] | 2021-02-18 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 5.16407371e-01 -1.95464194e-01 3.62211376e-01 -2.61682600e-01
-5.16156256e-01 -4.12734091e-01 3.72175336e-01 -4.37579125e-01
-5.32504201e-01 6.72795236e-01 -2.09108949e-01 -3.84619772e-01
4.83619034e-01 -8.71231139e-01 -1.19412899e+00 -9.31754291e-01
3.38821679e-01 -1.25704736e-01 3.77771914e-01 -3.18696678... | [10.611298561096191, -2.3420965671539307] |
1d030f82-2748-49d0-a4ca-d08906481978 | flame-facial-landmark-heatmap-activated | 2110.04828 | null | https://arxiv.org/abs/2110.04828v3 | https://arxiv.org/pdf/2110.04828v3.pdf | FLAME: Facial Landmark Heatmap Activated Multimodal Gaze Estimation | 3D gaze estimation is about predicting the line of sight of a person in 3D space. Person-independent models for the same lack precision due to anatomical differences of subjects, whereas person-specific calibrated techniques add strict constraints on scalability. To overcome these issues, we propose a novel technique, ... | ['Francois Bremond', 'Michal Balazia', 'Neelabh Sinha'] | 2021-10-10 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-3.23282391e-01 1.31118596e-01 -8.42700377e-02 -5.38349271e-01
-2.68229008e-01 -3.74748588e-01 4.69273627e-01 -3.29812318e-01
-6.25989556e-01 7.12360978e-01 2.00172037e-01 -4.23792489e-02
1.45629212e-01 -6.59334846e-03 -4.26392913e-01 -4.59692389e-01
2.01902404e-01 -2.71970443e-02 1.41834855e-01 6.84597045... | [14.112130165100098, 0.09601867198944092] |
6d5d8005-2324-4c9f-bab2-a76f0a3fd30b | incremental-outlier-detection-modelling-using | 2305.09907 | null | https://arxiv.org/abs/2305.09907v1 | https://arxiv.org/pdf/2305.09907v1.pdf | Incremental Outlier Detection Modelling Using Streaming Analytics in Finance & Health Care | In this paper, we had built the online model which are built incrementally by using online outlier detection algorithms under the streaming environment. We identified that there is highly necessity to have the streaming models to tackle the streaming data. The objective of this project is to study and analyze the impor... | ['Vivek', 'Ch Priyanka'] | 2023-05-17 | null | null | null | null | ['diabetes-prediction', 'outlier-detection', 'fraud-detection'] | ['medical', 'methodology', 'miscellaneous'] | [-3.95367920e-01 -2.29189694e-01 1.33898601e-01 -2.74893522e-01
2.55239218e-01 1.04252296e-03 1.56222492e-01 8.59039962e-01
-2.61480480e-01 6.64963722e-01 2.71864593e-01 -4.09048110e-01
-6.41026616e-01 -7.26236224e-01 -4.02889609e-01 -2.49851078e-01
-6.29455268e-01 8.35005879e-01 4.95174944e-01 -3.13792795... | [8.147663116455078, 4.919399738311768] |
36a8944c-369d-498e-a9de-8addad133f41 | an-embarrassingly-simple-baseline-for-extreme | 1912.08140 | null | https://arxiv.org/abs/1912.08140v2 | https://arxiv.org/pdf/1912.08140v2.pdf | On-the-fly Global Embeddings Using Random Projections for Extreme Multi-label Classification | The goal of eXtreme Multi-label Learning (XML) is to automatically annotate a given data point with the most relevant subset of labels from an extremely large vocabulary of labels (e.g., a million labels). Lately, many attempts have been made to address this problem that achieve reasonable performance on benchmark data... | ['Yashaswi Verma'] | 2019-12-17 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 4.22600478e-01 2.06193570e-02 -2.61648864e-01 -7.94469059e-01
-1.39753377e+00 -6.98792636e-01 5.42380869e-01 4.93728131e-01
-5.71010947e-01 5.41574776e-01 1.88339036e-02 -1.75990149e-01
-1.30507797e-01 -5.66649675e-01 -6.23951435e-01 -8.04890573e-01
1.00751929e-01 6.35435462e-01 1.81445211e-01 1.22343130... | [9.528292655944824, 4.381275653839111] |
796b3c8b-80d7-4173-8e3b-12cbf5e092a6 | egocentric-activity-recognition-with | 1601.06603 | null | http://arxiv.org/abs/1601.06603v1 | http://arxiv.org/pdf/1601.06603v1.pdf | Egocentric Activity Recognition with Multimodal Fisher Vector | With the increasing availability of wearable devices, research on egocentric
activity recognition has received much attention recently. In this paper, we
build a Multimodal Egocentric Activity dataset which includes egocentric videos
and sensor data of 20 fine-grained and diverse activity categories. We present
a novel... | ['Vijay Chandrasekhar', 'Ngai-Man Cheung', 'Sibo Song', 'Jie Lin', 'Bappaditya Mandal'] | 2016-01-25 | null | null | null | null | ['egocentric-activity-recognition'] | ['computer-vision'] | [ 9.16957259e-02 -6.11359000e-01 -4.05910760e-01 -5.28095603e-01
-5.38630247e-01 -4.68702406e-01 6.28363788e-01 -1.64406434e-01
-4.46026355e-01 6.25157773e-01 9.70032752e-01 3.00210208e-01
-3.98791611e-01 -4.99464244e-01 -3.90757978e-01 -6.05059147e-01
-5.60874701e-01 -5.55375218e-01 7.60078356e-02 2.35352620... | [8.051386833190918, 0.5512800216674805] |
67462d97-df05-4f96-aafe-dab55ca9e702 | edione-lt-edi-eacl2021-pre-trained | null | null | https://aclanthology.org/2021.ltedi-1.11 | https://aclanthology.org/2021.ltedi-1.11.pdf | EDIOne@LT-EDI-EACL2021: Pre-trained Transformers with Convolutional Neural Networks for Hope Speech Detection. | Hope is an essential aspect of mental health stability and recovery in every individual in this fast-changing world. Any tools and methods developed for detection, analysis, and generation of hope speech will be beneficial. In this paper, we propose a model on hope-speech detection to automatically detect web content t... | ['Radhika Mamidi', 'Suman Dowlagar'] | null | null | null | null | eacl-ltedi-2021-4 | ['hope-speech-detection'] | ['natural-language-processing'] | [-2.94037998e-01 7.80612975e-02 -5.86615145e-01 -1.91394404e-01
-7.67457545e-01 1.81777418e-01 7.51600981e-01 5.41099012e-01
-4.87224430e-01 7.04301775e-01 9.96915340e-01 -4.28937554e-01
-3.43302190e-02 -8.80408287e-01 -9.18027088e-02 -1.18786164e-01
-3.68192822e-01 2.13669956e-01 -3.97172660e-01 -6.17494166... | [8.993998527526855, 10.697962760925293] |
68208786-90bf-43f8-adfa-eac1b83f78c6 | weakly-supervised-silhouette-based-semantic | 1811.11985 | null | https://arxiv.org/abs/1811.11985v3 | https://arxiv.org/pdf/1811.11985v3.pdf | Weakly Supervised Silhouette-based Semantic Scene Change Detection | This paper presents a novel semantic scene change detection scheme with only weak supervision. A straightforward approach for this task is to train a semantic change detection network directly from a large-scale dataset in an end-to-end manner. However, a specific dataset for this task, which is usually labor-intensive... | ['Weimin WANG', 'Ken Sakurada', 'Mikiya Shibuya'] | 2018-11-29 | null | null | null | null | ['scene-change-detection'] | ['computer-vision'] | [ 2.84082979e-01 -6.08063400e-01 3.01826239e-01 -5.74630499e-01
-3.97043705e-01 -4.53286231e-01 4.43462491e-01 -8.61747712e-02
-7.63712645e-01 5.09966016e-01 -1.44979209e-01 1.98807977e-02
1.49220794e-01 -7.53858566e-01 -7.20258296e-01 -7.75486529e-01
4.29123044e-01 -4.02610265e-02 6.12279773e-01 -1.72051355... | [9.634237289428711, -1.1036251783370972] |
92ead621-fb86-45f4-b173-c6d6c441ef18 | learning-deep-representations-of-medical | 1711.08490 | null | http://arxiv.org/abs/1711.08490v2 | http://arxiv.org/pdf/1711.08490v2.pdf | Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrieval | Deep neural networks have been investigated in learning latent
representations of medical images, yet most of the studies limit their approach
in a single supervised convolutional neural network (CNN), which usually rely
heavily on a large scale annotated dataset for training. To learn image
representations with less s... | ['Wei-Hung Weng', 'Yu-An Chung'] | 2017-11-22 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [ 4.19638366e-01 2.37675801e-01 -6.20491743e-01 -6.33802474e-01
-8.70756507e-01 5.20654880e-02 4.73555177e-01 9.19339210e-02
-6.88310206e-01 5.79048336e-01 2.43239984e-01 -5.61837442e-02
-3.05412412e-01 -5.83831489e-01 -6.27675474e-01 -7.28479743e-01
5.91906756e-02 5.17817497e-01 8.72542674e-04 2.04798296... | [14.83836555480957, -2.436047077178955] |
698401a0-af79-4d76-a4eb-cd2789f0c0c8 | 3d-registration-with-maximal-cliques-1 | 2305.10854 | null | https://arxiv.org/abs/2305.10854v1 | https://arxiv.org/pdf/2305.10854v1.pdf | 3D Registration with Maximal Cliques | As a fundamental problem in computer vision, 3D point cloud registration (PCR) aims to seek the optimal pose to align a point cloud pair. In this paper, we present a 3D registration method with maximal cliques (MAC). The key insight is to loosen the previous maximum clique constraint, and mine more local consensus info... | ['Yanning Zhang', 'Shikun Zhang', 'Jiaqi Yang', 'Xiyu Zhang'] | 2023-05-18 | 3d-registration-with-maximal-cliques | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_3D_Registration_With_Maximal_Cliques_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_3D_Registration_With_Maximal_Cliques_CVPR_2023_paper.pdf | cvpr-2023-1 | ['point-cloud-registration'] | ['computer-vision'] | [-1.74842075e-01 2.66419679e-01 -1.79745153e-01 -7.44184181e-02
-7.95644760e-01 -3.72079790e-01 4.03282821e-01 1.71575889e-01
-1.85717568e-01 2.25660726e-01 -2.06098109e-02 3.77601665e-03
-2.03373820e-01 -8.02776933e-01 -8.30481887e-01 -5.39432883e-01
-3.85060757e-01 1.11776781e+00 3.92253637e-01 -2.07983047... | [7.64498233795166, -3.028937816619873] |
a1deb9f1-7c04-43d0-a9df-cea8aef5ecca | a-station-data-based-model-residual-machine | null | null | https://doi.org/10.1007/ | https://doi.org/10.1007/ | A station-data-based model residual machine learning method for fine-grained meteorological grid prediction | Fine-grained weather forecasting data, i.e., the grid data with high-resolution,
have attracted increasing attention in recent years, especially for some specific applications
such as the Winter Olympic Games. Although European Centre for Medium-Range
Weather Forecasts (ECMWF) provides grid prediction up to 240 hour... | ['†', 'Pingwen ZHANG1', 'Jiangjiang XIA3', 'Chen YU1', 'Haochen LI2', 'Chuansai ZHOU1'] | 2021-12-16 | a-station-data-based-model-residual-machine-1 | https://doi.org/ | https://paperswithcode.com/ | appl-math-mech-engl-ed-43-2-155-166-2022-2021 | ['machine-learning', 'weather-forecasting', 'machine-learning'] | ['methodology', 'miscellaneous', 'miscellaneous'] | [-3.93243521e-01 -4.52355921e-01 1.79020599e-01 -5.23279488e-01
-7.91500866e-01 -2.31258869e-01 7.34510005e-01 1.34309873e-01
-1.32301703e-01 1.39064050e+00 2.69500762e-01 -6.79979503e-01
6.00927100e-02 -1.28479218e+00 -3.91592711e-01 -8.72075260e-01
-2.47021973e-01 1.60828218e-01 1.83492433e-02 -6.44593596... | [6.596577167510986, 2.9866318702697754] |
d9c1d6f2-8fff-4dbf-b1a6-b5a99882d2cf | reducing-annotation-need-in-self-explanatory | 2206.13608 | null | https://arxiv.org/abs/2206.13608v2 | https://arxiv.org/pdf/2206.13608v2.pdf | Reducing Annotation Need in Self-Explanatory Models for Lung Nodule Diagnosis | Feature-based self-explanatory methods explain their classification in terms of human-understandable features. In the medical imaging community, this semantic matching of clinical knowledge adds significantly to the trustworthiness of the AI. However, the cost of additional annotation of features remains a pressing iss... | ['Sune Darkner', 'Michael Bachmann Nielsen', 'Kenny Erleben', 'Oswin Krause', 'CHONG YIN', 'Jiahao Lu'] | 2022-06-27 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 4.68196943e-02 8.90568912e-01 -5.76099336e-01 -4.49255198e-01
-9.70727324e-01 -4.08904612e-01 4.06390578e-01 3.38349581e-01
-4.93313111e-02 6.99567676e-01 3.77079368e-01 -3.14825773e-01
-3.91200602e-01 -4.63875115e-01 -3.52748871e-01 -7.07792580e-01
2.90540382e-02 8.59791160e-01 1.23444267e-01 2.53707469... | [15.014144897460938, -2.215553045272827] |
87d69ff8-d475-4868-b704-1a3437e4d635 | computational-complexity-of-observing | 1808.03387 | null | http://arxiv.org/abs/1808.03387v1 | http://arxiv.org/pdf/1808.03387v1.pdf | Computational Complexity of Observing Evolution in Artificial-Life Forms | Observations are an essential component of the simulation based studies on
artificial-evolutionary systems (AES) by which entities are identified and
their behavior is observed to uncover higher-level "emergent" phenomena.
Because of the heterogeneity of AES models and implicit nature of observations,
precise character... | ['Misra Janardan'] | 2018-06-24 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 2.78609723e-01 8.91795903e-02 1.99744925e-01 3.84463072e-01
2.85433710e-01 -8.61784220e-01 9.46994960e-01 7.79255092e-01
-2.21500918e-01 7.95846105e-01 -2.63920754e-01 -4.70455796e-01
-4.05568928e-01 -9.12464321e-01 -5.13508022e-01 -9.41929162e-01
-7.72002101e-01 5.80263317e-01 -8.97192489e-03 -4.03679371... | [5.609847068786621, 4.150233268737793] |
b2f1e8a0-f142-4c96-9ec7-7ec57a7f5c81 | an-in-depth-investigation-of-user-response | 2304.07944 | null | https://arxiv.org/abs/2304.07944v1 | https://arxiv.org/pdf/2304.07944v1.pdf | An In-depth Investigation of User Response Simulation for Conversational Search | Conversational search has seen increased recent attention in both the IR and NLP communities. It seeks to clarify and solve a user's search need through multi-turn natural language interactions. However, most existing systems are trained and demonstrated with recorded or artificial conversation logs. Eventually, conver... | ['Vivek Srikumar', 'Qingyao Ai', 'Zhichao Xu', 'Zhenduo Wang'] | 2023-04-17 | null | null | null | null | ['user-simulation', 'conversational-search'] | ['natural-language-processing', 'natural-language-processing'] | [ 8.44154432e-02 3.21936607e-01 1.23304680e-01 -3.48725319e-01
-1.00792336e+00 -8.20754290e-01 7.20096648e-01 -3.87505949e-01
-2.93427289e-01 7.33749449e-01 3.54992539e-01 -6.39981151e-01
-1.13096483e-01 -2.75980920e-01 -2.69693732e-01 -2.52645314e-01
2.55414784e-01 8.85205328e-01 9.25740823e-02 -6.62452936... | [12.167961120605469, 7.773397922515869] |
28e2f87d-f04c-487b-b52c-56d238526284 | learning-a-smooth-kernel-regularizer-for | 1903.01882 | null | http://arxiv.org/abs/1903.01882v1 | http://arxiv.org/pdf/1903.01882v1.pdf | Learning a smooth kernel regularizer for convolutional neural networks | Modern deep neural networks require a tremendous amount of data to train,
often needing hundreds or thousands of labeled examples to learn an effective
representation. For these networks to work with less data, more structure must
be built into their architectures or learned from previous experience. The
learned weight... | ['Reuben Feinman', 'Brenden M. Lake'] | 2019-03-05 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 2.15293512e-01 2.98913330e-01 -2.90525228e-01 -9.76475418e-01
-1.44842446e-01 -4.63046849e-01 6.75616324e-01 -1.59856424e-01
-6.93326652e-01 3.55932832e-01 3.81039500e-01 -1.62662849e-01
-1.85061581e-02 -5.66417575e-01 -9.07843173e-01 -7.01963902e-01
-1.63284257e-01 2.71887362e-01 4.70369041e-01 1.58505589... | [9.278127670288086, 2.7124645709991455] |
5566cd02-570e-439f-93de-0116a8a7b248 | meta-learners-for-estimation-of-causal | 2201.12692 | null | https://arxiv.org/abs/2201.12692v1 | https://arxiv.org/pdf/2201.12692v1.pdf | Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance | Estimation of causal effects using machine learning methods has become an active research field in econometrics. In this paper, we study the finite sample performance of meta-learners for estimation of heterogeneous treatment effects under the usage of sample-splitting and cross-fitting to reduce the overfitting bias. ... | ['Gabriel Okasa'] | 2022-01-30 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-7.74498284e-02 1.37296543e-01 -1.07978058e+00 -4.20724303e-01
-8.65181029e-01 -2.66008884e-01 4.61729765e-01 3.17293882e-01
-5.26577175e-01 9.86323893e-01 4.64616984e-01 -5.95127404e-01
-5.47772884e-01 -6.50407195e-01 -7.51112223e-01 -6.33392274e-01
-2.15768948e-01 2.48584032e-01 -2.65071005e-01 2.72486567... | [7.954464435577393, 5.227576732635498] |
d823a1f1-caa0-426b-b466-cb7a70751a0b | improving-negation-detection-with-negation-1 | 2205.04012 | null | https://arxiv.org/abs/2205.04012v1 | https://arxiv.org/pdf/2205.04012v1.pdf | Improving negation detection with negation-focused pre-training | Negation is a common linguistic feature that is crucial in many language understanding tasks, yet it remains a hard problem due to diversity in its expression in different types of text. Recent work has shown that state-of-the-art NLP models underperform on samples containing negation in various tasks, and that negatio... | ['Karin Verspoor', 'Trevor Cohn', 'Timothy Baldwin', 'Thinh Hung Truong'] | 2022-05-09 | null | https://aclanthology.org/2022.naacl-main.309 | https://aclanthology.org/2022.naacl-main.309.pdf | naacl-2022-7 | ['negation-detection'] | ['natural-language-processing'] | [ 6.85082823e-02 -9.72099081e-02 -6.15034878e-01 -7.27080405e-01
-5.19591808e-01 -8.04544091e-01 6.63315177e-01 4.22203898e-01
-7.53598392e-01 1.16719270e+00 2.67049134e-01 -4.59908664e-01
4.64967221e-01 -8.54272962e-01 -7.07884431e-01 -7.71382526e-02
1.96716160e-01 4.06846553e-01 3.07761550e-01 -9.13932562... | [10.400712966918945, 9.170385360717773] |
d63c30de-edc6-4323-a749-482927c7e7f0 | graph-self-supervised-learning-with-accurate | 2202.02989 | null | https://arxiv.org/abs/2202.02989v5 | https://arxiv.org/pdf/2202.02989v5.pdf | Graph Self-supervised Learning with Accurate Discrepancy Learning | Self-supervised learning of graph neural networks (GNNs) aims to learn an accurate representation of the graphs in an unsupervised manner, to obtain transferable representations of them for diverse downstream tasks. Predictive learning and contrastive learning are the two most prevalent approaches for graph self-superv... | ['Sung Ju Hwang', 'Jinheon Baek', 'DongKi Kim'] | 2022-02-07 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 4.23271865e-01 3.89543027e-01 -4.66839463e-01 -3.32650065e-01
-3.74443561e-01 -4.50977862e-01 4.29521739e-01 8.59673142e-01
6.66544512e-02 6.14184797e-01 9.70166922e-02 -1.53996825e-01
-3.02592158e-01 -1.05609059e+00 -9.32935119e-01 -7.34875083e-01
-3.94151300e-01 3.92944694e-01 1.94756821e-01 -2.96600610... | [7.210498332977295, 6.291930198669434] |
8d754e4d-30e8-4ba6-8184-19b4bb9c9cb0 | detecting-denial-of-service-attacks-from | null | null | https://aclanthology.org/N18-1147 | https://aclanthology.org/N18-1147.pdf | Detecting Denial-of-Service Attacks from Social Media Text: Applying NLP to Computer Security | This paper describes a novel application of NLP models to detect denial of service attacks using only social media as evidence. Individual networks are often slow in reporting attacks, so a detection system from public data could better assist a response to a broad attack across multiple services. We explore NLP method... | ['James McMasters', 'Ben Fry', 'Nathanael Chambers'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['computer-security'] | ['miscellaneous'] | [-2.21879750e-01 9.08362046e-02 -5.08692443e-01 -5.14202178e-01
-9.35770035e-01 -8.07658076e-01 8.09672713e-01 4.69285131e-01
-1.65887758e-01 6.04600310e-01 2.22962320e-01 -8.69649351e-01
-2.96864063e-01 -8.71985793e-01 -2.52315104e-01 -5.38365722e-01
-5.61372280e-01 1.12064481e+00 4.07840163e-01 -7.93870613... | [8.106654167175293, 9.557048797607422] |
ffcad9e7-282e-4837-904f-51c1a94d22d9 | domain-adversarial-training-of-self-attention | 2104.00564 | null | https://arxiv.org/abs/2104.00564v2 | https://arxiv.org/pdf/2104.00564v2.pdf | Domain-Adversarial Training of Self-Attention Based Networks for Land Cover Classification using Multi-temporal Sentinel-2 Satellite Imagery | The increasing availability of large-scale remote sensing labeled data has prompted researchers to develop increasingly precise and accurate data-driven models for land cover and crop classification (LC&CC). Moreover, with the introduction of self-attention and introspection mechanisms, deep learning approaches have sh... | ['Mauro Martini', 'Marcello Chiaberge', 'Aleem Khaliq', 'Vittorio Mazzia'] | 2021-04-01 | null | null | null | null | ['crop-classification'] | ['miscellaneous'] | [ 4.65116888e-01 -2.44830027e-01 -2.66476661e-01 -3.71497303e-01
-7.36876070e-01 -8.09717000e-01 5.85496724e-01 2.91648865e-01
-5.25497556e-01 1.01388586e+00 -1.75840314e-02 -4.98920918e-01
-2.50782192e-01 -1.05547225e+00 -6.50123775e-01 -8.27461541e-01
-1.21648476e-01 2.89107233e-01 -3.16668302e-02 -6.44785762... | [9.586417198181152, -1.4788589477539062] |
2d58e70a-2457-4037-9bb4-6d42905e7db3 | learning-blind-video-temporal-consistency | 1808.00449 | null | http://arxiv.org/abs/1808.00449v1 | http://arxiv.org/pdf/1808.00449v1.pdf | Learning Blind Video Temporal Consistency | Applying image processing algorithms independently to each frame of a video
often leads to undesired inconsistent results over time. Developing temporally
consistent video-based extensions, however, requires domain knowledge for
individual tasks and is unable to generalize to other applications. In this
paper, we prese... | ['Ming-Hsuan Yang', 'Jia-Bin Huang', 'Wei-Sheng Lai', 'Ersin Yumer', 'Eli Shechtman', 'Oliver Wang'] | 2018-08-01 | learning-blind-video-temporal-consistency-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Wei-Sheng_Lai_Real-Time_Blind_Video_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Wei-Sheng_Lai_Real-Time_Blind_Video_ECCV_2018_paper.pdf | eccv-2018-9 | ['intrinsic-image-decomposition', 'video-temporal-consistency'] | ['computer-vision', 'computer-vision'] | [ 3.81891370e-01 -5.51274896e-01 1.61989719e-01 -2.80159056e-01
-5.66633761e-01 -5.85516095e-01 3.33849043e-01 -3.74102563e-01
-5.98920286e-01 6.37191772e-01 -1.50015175e-01 3.43379453e-02
4.18521650e-02 -3.26008379e-01 -9.61252511e-01 -5.87265551e-01
-9.23057571e-02 -2.39901021e-01 2.05832273e-01 -8.48975778... | [10.851390838623047, -1.3979383707046509] |
994671d9-5f67-4b8e-a360-2344528da146 | autodepthnet-high-frame-rate-depth-map | 2305.14731 | null | https://arxiv.org/abs/2305.14731v1 | https://arxiv.org/pdf/2305.14731v1.pdf | AutoDepthNet: High Frame Rate Depth Map Reconstruction using Commodity Depth and RGB Cameras | Depth cameras have found applications in diverse fields, such as computer vision, artificial intelligence, and video gaming. However, the high latency and low frame rate of existing commodity depth cameras impose limitations on their applications. We propose a fast and accurate depth map reconstruction technique to red... | ['Robert Xiao', 'Peyman Gholami'] | 2023-05-24 | null | null | null | null | ['video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.60879302e-01 -1.38014972e-01 -8.16555396e-02 -2.30032623e-01
-4.35407043e-01 -3.82854730e-01 -8.07089210e-02 -2.39477605e-01
-8.21283102e-01 4.64212388e-01 -1.67070419e-01 -5.30893430e-02
6.34910345e-01 -1.02680337e+00 -6.26770318e-01 -3.82963210e-01
1.62211478e-01 2.59805262e-01 7.55089045e-01 2.41678238... | [8.868889808654785, -2.240626811981201] |
97ce08a8-b53b-450e-89ed-77991acce353 | unified-embedding-based-personalized | 2306.04833 | null | https://arxiv.org/abs/2306.04833v1 | https://arxiv.org/pdf/2306.04833v1.pdf | Unified Embedding Based Personalized Retrieval in Etsy Search | Embedding-based neural retrieval is a prevalent approach to address the semantic gap problem which often arises in product search on tail queries. In contrast, popular queries typically lack context and have a broad intent where additional context from users historical interaction can be helpful. In this paper, we shar... | ['Thrivikrama Taula', 'Ethan Benjamin', 'Siddharth Subramaniyam', 'Rishikesh Jha'] | 2023-06-07 | null | null | null | null | ['feature-engineering', 'semantic-retrieval'] | ['methodology', 'natural-language-processing'] | [-6.30541891e-02 -3.22032779e-01 -6.41158044e-01 -4.42506850e-01
-1.28679574e+00 -7.21317410e-01 4.56741691e-01 1.74553707e-01
-2.98118174e-01 3.04850144e-03 3.10235620e-01 -4.79593068e-01
-6.08143270e-01 -6.15875661e-01 -3.83502603e-01 -5.87452129e-02
1.54989110e-02 4.14009750e-01 8.24171752e-02 -6.38038516... | [11.323907852172852, 7.402409553527832] |
194153e7-1e94-4e5c-abc4-4c02cc9081e0 | discourse-analysis-via-questions-and-answers | 2210.05905 | null | https://arxiv.org/abs/2210.05905v2 | https://arxiv.org/pdf/2210.05905v2.pdf | Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion | Automatic discourse processing is bottlenecked by data: current discourse formalisms pose highly demanding annotation tasks involving large taxonomies of discourse relations, making them inaccessible to lay annotators. This work instead adopts the linguistic framework of Questions Under Discussion (QUD) for discourse a... | ['Junyi Jessy Li', 'Greg Durrett', 'Dananjay Srinivas', 'Cutter Dalton', 'Yating Wu', 'Wei-Jen Ko'] | 2022-10-12 | null | null | null | null | ['dependency-parsing'] | ['natural-language-processing'] | [ 2.39160687e-01 1.20816422e+00 -7.51277879e-02 -4.25648093e-01
-1.33789337e+00 -1.03429627e+00 9.57122147e-01 4.94973689e-01
-2.94509828e-01 9.72243309e-01 1.05976331e+00 -6.94585323e-01
-4.68330691e-03 -6.88155115e-01 -4.47153389e-01 -1.02902181e-01
2.78213024e-01 5.73848486e-01 5.03801167e-01 -7.81101704... | [10.80997371673584, 9.395743370056152] |
c25ce6f2-dbc0-4ab9-9ad7-e3b74e87a1b4 | beyond-cross-view-image-retrieval-highly | 2204.04752 | null | https://arxiv.org/abs/2204.04752v2 | https://arxiv.org/pdf/2204.04752v2.pdf | Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image | This paper addresses the problem of vehicle-mounted camera localization by matching a ground-level image with an overhead-view satellite map. Existing methods often treat this problem as cross-view image retrieval, and use learned deep features to match the ground-level query image to a partition (eg, a small patch) of... | ['Hongdong Li', 'Yujiao Shi'] | 2022-04-10 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Shi_Beyond_Cross-View_Image_Retrieval_Highly_Accurate_Vehicle_Localization_Using_Satellite_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Shi_Beyond_Cross-View_Image_Retrieval_Highly_Accurate_Vehicle_Localization_Using_Satellite_CVPR_2022_paper.pdf | cvpr-2022-1 | ['camera-localization'] | ['computer-vision'] | [-3.92291732e-02 -1.27780885e-01 5.36368378e-02 -5.13750255e-01
-1.26609278e+00 -9.07583654e-01 5.77448666e-01 -2.25149885e-01
-6.96065545e-01 2.55491942e-01 -3.69967699e-01 -1.10838838e-01
-8.18270147e-02 -7.74383128e-01 -1.06244624e+00 -6.38758719e-01
9.41803604e-02 5.21521747e-01 1.29288256e-01 -7.46615902... | [7.709802627563477, -2.1087300777435303] |
e47f502a-dc79-4464-8473-ccf3b3c5ff25 | resmem-learn-what-you-can-and-memorize-the | 2302.01576 | null | https://arxiv.org/abs/2302.01576v1 | https://arxiv.org/pdf/2302.01576v1.pdf | ResMem: Learn what you can and memorize the rest | The impressive generalization performance of modern neural networks is attributed in part to their ability to implicitly memorize complex training patterns. Inspired by this, we explore a novel mechanism to improve model generalization via explicit memorization. Specifically, we propose the residual-memorization (ResMe... | ['Sanjiv Kumar', 'Aditya Krishna Menon', 'Manzil Zaheer', 'Ankit Singh Rawat', 'Zonglin Li', 'Vaishnavh Nagarajan', 'Michal Lukasik', 'Zitong Yang'] | 2023-02-03 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 3.77715111e-01 9.69486013e-02 -2.04448909e-01 -6.38875127e-01
-2.44610101e-01 -1.42152742e-01 5.79460919e-01 4.51330133e-02
-5.60899436e-01 1.03158092e+00 1.39991820e-01 -3.28659326e-01
1.96659993e-02 -9.30945575e-01 -1.02591801e+00 -6.00089431e-01
2.01398104e-01 1.78633511e-01 -3.17848437e-02 6.28819177... | [8.866656303405762, 3.2313153743743896] |
100de743-96f2-42e4-b4ff-876ae03065ca | path-specific-causal-fair-prediction-via | null | null | https://openreview.net/forum?id=sWqjiqlUDso | https://openreview.net/pdf?id=sWqjiqlUDso | Path-specific Causal Fair Prediction via Auxiliary Graph Structure Learning | Algorithm fairness has become a trending topic, and it has a great impact on social welfare. Among different fairness definitions, path-specific causal fairness is a widely adopted one with great potentials, as it distinguishes the fair and unfair effects that the sensitive attributes exert on algorithm predictions. Ex... | ['Jing Gao', 'Mengdi Huai', 'Jinduo Liu', 'Jingren Zhou', 'Bolin Ding', 'Yaliang Li', 'Liuyi Yao'] | 2021-09-29 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 2.21291825e-01 -4.72298451e-02 -6.89585388e-01 -5.17244816e-01
-2.07056496e-02 -2.18946010e-01 2.83825159e-01 3.12094480e-01
-8.96703452e-02 9.35454607e-01 1.56991452e-01 -5.61263740e-01
-6.98854923e-01 -1.15421319e+00 -3.50365967e-01 -4.70726192e-01
-2.09845185e-01 1.97188750e-01 1.71036512e-01 -2.38577902... | [9.096705436706543, 5.401921272277832] |
8eac8e8c-332b-4df7-a55d-66f44e1f503d | a-data-augmentation-perspective-on-diffusion | 2304.10253 | null | https://arxiv.org/abs/2304.10253v1 | https://arxiv.org/pdf/2304.10253v1.pdf | A data augmentation perspective on diffusion models and retrieval | Diffusion models excel at generating photorealistic images from text-queries. Naturally, many approaches have been proposed to use these generative abilities to augment training datasets for downstream tasks, such as classification. However, diffusion models are themselves trained on large noisily supervised, but nonet... | ['Chris Russell', 'Francesco Locatello', 'Osama Makansi', 'Max Horn', 'Dominik Zietlow', 'Florian Wenzel', 'Max F. Burg'] | 2023-04-20 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 4.86400843e-01 6.69401824e-01 -3.05457935e-02 -4.36162204e-01
-7.66836643e-01 -8.29704583e-01 1.26540530e+00 1.91664755e-01
-6.80537701e-01 4.44901377e-01 5.47711909e-01 -5.91994464e-01
-7.12018739e-03 -8.50835085e-01 -4.54293966e-01 -5.99694550e-01
3.83861303e-01 5.93180835e-01 2.65429974e-01 -3.34574163... | [11.26136302947998, -0.10596626996994019] |
c2b88a50-17e5-42a6-b364-b631a8b138c0 | twistslam-fusing-multiple-modalities-for | 2209.07888 | null | https://arxiv.org/abs/2209.07888v2 | https://arxiv.org/pdf/2209.07888v2.pdf | TwistSLAM++: Fusing multiple modalities for accurate dynamic semantic SLAM | Most classical SLAM systems rely on the static scene assumption, which limits their applicability in real world scenarios. Recent SLAM frameworks have been proposed to simultaneously track the camera and moving objects. However they are often unable to estimate the canonical pose of the objects and exhibit a low object... | ['Jérôme Royan', 'Amine Kacete', 'Eric Marchand', 'Mathieu Gonzalez'] | 2022-09-16 | null | null | null | null | ['semantic-slam'] | ['computer-vision'] | [ 6.27928451e-02 -3.97247046e-01 -1.48734182e-01 -3.47515255e-01
-5.68561733e-01 -7.71326303e-01 7.26313531e-01 4.05774623e-01
-6.49586558e-01 3.82088095e-01 -4.65475261e-01 2.51077235e-01
-1.89545229e-01 -6.23987138e-01 -8.49643469e-01 -4.16145593e-01
2.54488409e-01 1.35495031e+00 8.08730245e-01 6.08243421... | [7.321204662322998, -2.312021493911743] |
696600c2-1b95-49a0-9dde-b9d6ca7c713a | disentangled-modeling-of-domain-and-relevance | 2208.05753 | null | https://arxiv.org/abs/2208.05753v1 | https://arxiv.org/pdf/2208.05753v1.pdf | Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval | Recent advance in Dense Retrieval (DR) techniques has significantly improved the effectiveness of first-stage retrieval. Trained with large-scale supervised data, DR models can encode queries and documents into a low-dimensional dense space and conduct effective semantic matching. However, previous studies have shown t... | ['Shaoping Ma', 'Min Zhang', 'Xiaohui Xie', 'Jiaxin Mao', 'Yiqun Liu', 'Qingyao Ai', 'Jingtao Zhan'] | 2022-08-11 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [ 1.64688855e-01 -2.47301042e-01 -4.82007205e-01 -3.19977403e-01
-1.16424894e+00 -6.79105699e-01 7.73301244e-01 2.75005698e-02
-4.94679064e-01 5.34110844e-01 3.45821470e-01 2.95728240e-02
-3.47361386e-01 -7.43412971e-01 -4.23589528e-01 -3.80601168e-01
3.24647814e-01 1.09380901e+00 2.75823742e-01 -4.16423082... | [11.3319673538208, 7.722308158874512] |
7e7fd3af-67a0-4448-9c23-048c50bd611b | channel-adversarial-training-for-cross | 1902.09074 | null | http://arxiv.org/abs/1902.09074v1 | http://arxiv.org/pdf/1902.09074v1.pdf | Channel adversarial training for cross-channel text-independent speaker recognition | The conventional speaker recognition frameworks (e.g., the i-vector and
CNN-based approach) have been successfully applied to various tasks when the
channel of the enrolment dataset is similar to that of the test dataset.
However, in real-world applications, mismatch always exists between these two
datasets, which may ... | [] | 2019-02-25 | null | null | null | null | ['text-independent-speaker-recognition'] | ['speech'] | [ 3.34498405e-01 -4.40669596e-01 1.61092151e-02 -4.80568588e-01
-1.09176219e+00 -3.78861994e-01 4.66092497e-01 -2.83183336e-01
-2.43207291e-01 5.64732313e-01 4.06790674e-01 -2.85146028e-01
2.94795036e-01 -4.68752503e-01 -8.07242155e-01 -1.01792383e+00
2.75837690e-01 -7.77806789e-02 -1.27872914e-01 -4.57354710... | [14.435523986816406, 6.015368461608887] |
97f26817-ef39-462e-9bd1-51ab6d073a8d | boosting-contrastive-self-supervised-learning | 2011.11765 | null | https://arxiv.org/abs/2011.11765v2 | https://arxiv.org/pdf/2011.11765v2.pdf | Boosting Contrastive Self-Supervised Learning with False Negative Cancellation | Self-supervised representation learning has made significant leaps fueled by progress in contrastive learning, which seeks to learn transformations that embed positive input pairs nearby, while pushing negative pairs far apart. While positive pairs can be generated reliably (e.g., as different views of the same image),... | ['Maryam Khademi', 'Michael Maire', 'Matthew R. Walter', 'Simon Kornblith', 'Tri Huynh'] | 2020-11-23 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 4.56097454e-01 9.79988575e-02 -3.38298261e-01 -4.09208417e-01
-1.15753794e+00 -7.82142878e-01 8.19498360e-01 1.20969906e-01
-5.02107739e-01 7.98860431e-01 1.41765952e-01 -4.93578687e-02
1.08454637e-01 -6.53875411e-01 -9.26720202e-01 -5.38420200e-01
-1.31744236e-01 3.66384625e-01 1.62497938e-01 -1.23108037... | [9.576974868774414, 2.729806423187256] |
3d747f3f-0838-4f8f-ba02-af0867b768fb | semantic-validation-in-structure-from-motion | 2304.02420 | null | https://arxiv.org/abs/2304.02420v1 | https://arxiv.org/pdf/2304.02420v1.pdf | Semantic Validation in Structure from Motion | The Structure from Motion (SfM) challenge in computer vision is the process of recovering the 3D structure of a scene from a series of projective measurements that are calculated from a collection of 2D images, taken from different perspectives. SfM consists of three main steps; feature detection and matching, camera m... | ['Joseph Rowell'] | 2023-04-05 | null | null | null | null | ['motion-estimation'] | ['computer-vision'] | [ 5.83228946e-01 1.61639169e-01 2.80168235e-01 -4.96957332e-01
-7.44599402e-01 -5.52037716e-01 4.56867486e-01 8.29408988e-02
-5.11757970e-01 8.98480490e-02 -5.72904229e-01 1.59926727e-01
-1.66306347e-01 -6.47610784e-01 -1.00582051e+00 -5.35569668e-01
3.28528643e-01 1.11820686e+00 6.59166753e-01 1.74795702... | [7.644425392150879, -2.6629722118377686] |
a3fc3825-deb8-45d2-98bf-9e43214755ef | embedded-deep-bilinear-interactive | 2007.06143 | null | https://arxiv.org/abs/2007.06143v1 | https://arxiv.org/pdf/2007.06143v1.pdf | Embedded Deep Bilinear Interactive Information and Selective Fusion for Multi-view Learning | As a concrete application of multi-view learning, multi-view classification improves the traditional classification methods significantly by integrating various views optimally. Although most of the previous efforts have been demonstrated the superiority of multi-view learning, it can be further improved by comprehensi... | ['Junwei Han', 'Xiwen Yao', 'Xiangsen Zhang', 'Peicheng Zhou', 'Xinwang Liu', 'Jiantao Shen', 'Wenbin Li', 'Jinglin Xu'] | 2020-07-13 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-4.02244478e-01 -6.61303282e-01 -2.18259618e-01 -5.84192634e-01
-8.93931448e-01 -6.17823839e-01 5.00697017e-01 -1.91209927e-01
-3.29735838e-02 1.63880542e-01 4.25766498e-01 1.22614361e-01
-2.82014310e-01 -8.61755908e-01 -3.86233449e-01 -1.08754289e+00
3.51761460e-01 4.83188443e-02 1.35405064e-01 -2.23800838... | [8.496973037719727, 4.513453006744385] |
81b3a8ff-94a5-45de-97f0-28662521f480 | source-side-prediction-for-neural-headline | 1712.08302 | null | http://arxiv.org/abs/1712.08302v1 | http://arxiv.org/pdf/1712.08302v1.pdf | Source-side Prediction for Neural Headline Generation | The encoder-decoder model is widely used in natural language generation
tasks. However, the model sometimes suffers from repeated redundant generation,
misses important phrases, and includes irrelevant entities. Toward solving
these problems we propose a novel source-side token prediction module. Our
method jointly est... | ['Masaaki Nagata', 'Kentaro Inui', 'Sho Takase', 'Shun Kiyono', 'Jun Suzuki', 'Naoaki Okazaki'] | 2017-12-22 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [-1.33897692e-01 3.71535420e-01 -4.65278745e-01 -2.74887860e-01
-1.27315748e+00 -4.31797773e-01 9.10743833e-01 1.59821138e-01
-2.74299234e-01 1.24965942e+00 7.21011519e-01 9.55524854e-03
3.86485189e-01 -8.98095369e-01 -7.96380103e-01 -2.45967254e-01
2.36032039e-01 7.43977368e-01 1.30778342e-01 -5.98570287... | [11.905048370361328, 9.024657249450684] |
ed2b05ff-fbf1-4aa4-81d3-ac965009da98 | learning-from-a-tiny-dataset-of-manual | 1812.00033 | null | https://arxiv.org/abs/1812.00033v3 | https://arxiv.org/pdf/1812.00033v3.pdf | Learning from a tiny dataset of manual annotations: a teacher/student approach for surgical phase recognition | Vision algorithms capable of interpreting scenes from a real-time video stream are necessary for computer-assisted surgery systems to achieve context-aware behavior. In laparoscopic procedures one particular algorithm needed for such systems is the identification of surgical phases, for which the current state of the a... | ['Tong Yu', 'Didier Mutter', 'Nicolas Padoy', 'Jacques Marescaux'] | 2018-11-30 | null | null | null | null | ['online-surgical-phase-recognition', 'surgical-phase-recognition'] | ['computer-vision', 'computer-vision'] | [ 6.13388479e-01 6.90431535e-01 -3.49514723e-01 -5.07328272e-01
-7.67938912e-01 -3.28670949e-01 4.61613744e-01 1.73344940e-01
-7.54599035e-01 6.68237209e-01 -8.26615691e-02 -3.90869200e-01
1.91018581e-01 -4.66240376e-01 -9.05469418e-01 -7.64251769e-01
6.29824102e-02 7.49095798e-01 8.65170434e-02 -1.13830045... | [14.148472785949707, -3.2271206378936768] |
7cc79c46-8a60-46ac-925c-e2f179835587 | exploring-the-limits-of-chatgpt-for-query-or | 2302.08081 | null | https://arxiv.org/abs/2302.08081v1 | https://arxiv.org/pdf/2302.08081v1.pdf | Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization | Text summarization has been a crucial problem in natural language processing (NLP) for several decades. It aims to condense lengthy documents into shorter versions while retaining the most critical information. Various methods have been proposed for text summarization, including extractive and abstractive summarization... | ['Wei Cheng', 'Haifeng Chen', 'Xinlu Zhang', 'Yan Li', 'Xianjun Yang'] | 2023-02-16 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 1.68308750e-01 4.67788041e-01 -3.77444565e-01 -1.84139058e-01
-1.20551610e+00 -5.27173102e-01 9.27740157e-01 7.61471391e-01
-2.67374903e-01 1.07312191e+00 1.24141347e+00 -9.74443853e-02
5.12332357e-02 -4.19154853e-01 -2.66721666e-01 -3.13873947e-01
-5.67421643e-03 5.38033903e-01 1.97095454e-01 -4.73238379... | [12.483285903930664, 9.438592910766602] |
6b06ad29-303a-4c4f-9485-c83bdcb34a35 | fusionseg-learning-to-combine-motion-and | 1701.05384 | null | http://arxiv.org/abs/1701.05384v2 | http://arxiv.org/pdf/1701.05384v2.pdf | FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos | We propose an end-to-end learning framework for segmenting generic objects in
videos. Our method learns to combine appearance and motion information to
produce pixel level segmentation masks for all prominent objects in videos. We
formulate this task as a structured prediction problem and design a two-stream
fully conv... | ['Kristen Grauman', 'Suyog Dutt Jain', 'Bo Xiong'] | 2017-01-19 | null | null | null | cvpr-2017 | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 5.28527558e-01 7.55364373e-02 -5.20619273e-01 -5.13766646e-01
-9.58586633e-01 -7.73811638e-01 2.67119706e-01 -5.77978015e-01
-4.48338479e-01 2.97746301e-01 1.01370044e-01 -1.33998215e-01
5.10809839e-01 -2.43104607e-01 -1.34453869e+00 -4.63070393e-01
-1.13053918e-01 2.39234224e-01 8.49306226e-01 2.75382787... | [9.175517082214355, -0.08169513195753098] |
baa21f1a-5e89-40b7-b438-a20912c77b1d | evolving-dictionary-representation-for-few | 2305.01885 | null | https://arxiv.org/abs/2305.01885v1 | https://arxiv.org/pdf/2305.01885v1.pdf | Evolving Dictionary Representation for Few-shot Class-incremental Learning | New objects are continuously emerging in the dynamically changing world and a real-world artificial intelligence system should be capable of continual and effectual adaptation to new emerging classes without forgetting old ones. In view of this, in this paper we tackle a challenging and practical continual learning sce... | ['Yuhong Guo', 'Xuejun Han'] | 2023-05-03 | null | null | null | null | ['class-incremental-learning', 'few-shot-class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology', 'methodology'] | [ 1.20379306e-01 -2.27831841e-01 -1.23365782e-01 -3.53584975e-01
6.81114867e-02 -4.47071582e-01 4.91257578e-01 2.65081227e-01
-5.93805373e-01 7.71733880e-01 -1.22976772e-01 1.51471913e-01
-4.97768335e-02 -1.02212179e+00 -4.44459736e-01 -6.99032187e-01
1.76383451e-01 5.87962270e-01 5.81135392e-01 -2.85992682... | [9.84841251373291, 3.371135711669922] |
ffe05410-f3ba-46df-b4e0-049e07e6db31 | one-shot-face-video-re-enactment-using-hybrid | 2302.07848 | null | https://arxiv.org/abs/2302.07848v1 | https://arxiv.org/pdf/2302.07848v1.pdf | One-Shot Face Video Re-enactment using Hybrid Latent Spaces of StyleGAN2 | While recent research has progressively overcome the low-resolution constraint of one-shot face video re-enactment with the help of StyleGAN's high-fidelity portrait generation, these approaches rely on at least one of the following: explicit 2D/3D priors, optical flow based warping as motion descriptors, off-the-shelf... | ['Yaser Yacoob', 'Trevine Oorloff'] | 2023-02-15 | null | null | null | null | ['video-generation'] | ['computer-vision'] | [ 3.68247956e-01 2.13356808e-01 1.61480550e-02 -4.96268272e-01
-4.66617405e-01 -4.83847797e-01 6.35400236e-01 -7.48120010e-01
-4.96848375e-02 7.41713583e-01 1.53877035e-01 3.24281245e-01
-1.66489497e-01 -8.41084719e-01 -6.78192914e-01 -5.91375828e-01
1.21697165e-01 -1.61171673e-04 -4.48018521e-01 -2.26085976... | [12.607306480407715, -0.24407434463500977] |
7e88e7f7-8d2f-4a43-b9e2-a291f53ca8de | mc-beit-multi-choice-discretization-for-image | 2203.15371 | null | https://arxiv.org/abs/2203.15371v4 | https://arxiv.org/pdf/2203.15371v4.pdf | mc-BEiT: Multi-choice Discretization for Image BERT Pre-training | Image BERT pre-training with masked image modeling (MIM) becomes a popular practice to cope with self-supervised representation learning. A seminal work, BEiT, casts MIM as a classification task with a visual vocabulary, tokenizing the continuous visual signals into discrete vision tokens using a pre-learned dVAE. Desp... | ['Ling-Yu Duan', 'Ying Shan', 'Zixuan Hu', 'Kun Yi', 'Yixiao Ge', 'Xiaotong Li'] | 2022-03-29 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 3.33881855e-01 4.10304606e-01 -5.17574549e-01 -4.36866611e-01
-9.54386771e-01 -5.46940923e-01 2.71094054e-01 -1.64172500e-01
-5.61324596e-01 5.05173087e-01 -4.77640420e-01 -2.35905960e-01
3.29424471e-01 -5.24245322e-01 -1.19454467e+00 -8.48302960e-01
2.90076107e-01 4.63862956e-01 1.81287542e-01 2.07858145... | [9.59519100189209, 0.7802178263664246] |
ef4471ce-88ce-4b3d-994d-6b41267a0501 | active-object-localization-with-deep | 1511.06015 | null | http://arxiv.org/abs/1511.06015v1 | http://arxiv.org/pdf/1511.06015v1.pdf | Active Object Localization with Deep Reinforcement Learning | We present an active detection model for localizing objects in scenes. The
model is class-specific and allows an agent to focus attention on candidate
regions for identifying the correct location of a target object. This agent
learns to deform a bounding box using simple transformation actions, with the
goal of determi... | ['Svetlana Lazebnik', 'Juan C. Caicedo'] | 2015-11-18 | active-object-localization-with-deep-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Caicedo_Active_Object_Localization_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Caicedo_Active_Object_Localization_ICCV_2015_paper.pdf | iccv-2015-12 | ['active-object-localization'] | ['computer-vision'] | [ 9.15378053e-03 4.66301769e-01 -9.53623578e-02 -2.96123087e-01
-8.42497468e-01 -7.36261308e-01 6.65059686e-01 5.93927383e-01
-1.08073151e+00 3.68179888e-01 -2.21008554e-01 2.80437589e-01
9.41471830e-02 -8.85741770e-01 -1.18082976e+00 -7.88147748e-01
-3.31526488e-01 7.25347161e-01 1.10414398e+00 2.27464810... | [9.325366020202637, 0.570793092250824] |
f36dc77c-98cb-4ee3-90f4-34f6a4a52806 | are-neural-open-domain-dialog-systems-robust | 2008.07683 | null | https://arxiv.org/abs/2008.07683v1 | https://arxiv.org/pdf/2008.07683v1.pdf | Are Neural Open-Domain Dialog Systems Robust to Speech Recognition Errors in the Dialog History? An Empirical Study | Large end-to-end neural open-domain chatbots are becoming increasingly popular. However, research on building such chatbots has typically assumed that the user input is written in nature and it is not clear whether these chatbots would seamlessly integrate with automatic speech recognition (ASR) models to serve the spe... | ['Dilek Hakkani-Tur', 'Longshaokan Wang', 'Yang Liu', 'Karthik Gopalakrishnan', 'Behnam Hedayatnia'] | 2020-08-18 | null | null | null | null | ['open-domain-dialog'] | ['natural-language-processing'] | [-3.8992271e-02 6.8154103e-01 3.0777106e-01 -5.9708714e-01
-1.0613191e+00 -8.3146399e-01 8.0621856e-01 -5.4994577e-01
-3.9728507e-01 5.4375482e-01 4.4372007e-01 -6.1578882e-01
4.2326131e-01 -4.6267071e-01 -5.5946916e-01 -2.3358256e-01
2.9255483e-01 1.2485821e+00 3.8229334e-01 -7.8351188e-01
-3.6219403e-01... | [12.826552391052246, 8.055208206176758] |
df1e604e-4ec0-4503-beca-695b46b9042f | strengthening-structural-baselines-for-graph | 2305.00724 | null | https://arxiv.org/abs/2305.00724v1 | https://arxiv.org/pdf/2305.00724v1.pdf | Strengthening structural baselines for graph classification using Local Topological Profile | We present the analysis of the topological graph descriptor Local Degree Profile (LDP), which forms a widely used structural baseline for graph classification. Our study focuses on model evaluation in the context of the recently developed fair evaluation framework, which defines rigorous routines for model selection an... | ['Wojciech Czech', 'Jakub Adamczyk'] | 2023-05-01 | null | null | null | null | ['graph-classification'] | ['graphs'] | [-2.22121067e-02 6.84373155e-02 -4.43919063e-01 -2.90897745e-03
-2.77857333e-01 -6.49554968e-01 9.04139102e-01 9.44876730e-01
-2.20440969e-01 5.19775748e-01 2.44971607e-02 -4.18805689e-01
-7.74161756e-01 -1.12376368e+00 -3.04719627e-01 -5.37042260e-01
-7.04252899e-01 6.89197421e-01 3.54261160e-01 -3.88663858... | [7.0267438888549805, 5.938589096069336] |
225bb5ff-f4f8-4be0-b60f-066a9f6fc7e4 | the-decomposition-of-the-higher-order | 2107.10970 | null | https://arxiv.org/abs/2107.10970v3 | https://arxiv.org/pdf/2107.10970v3.pdf | The decomposition of the higher-order homology embedding constructed from the $k$-Laplacian | The null space of the $k$-th order Laplacian $\mathbf{\mathcal L}_k$, known as the {\em $k$-th homology vector space}, encodes the non-trivial topology of a manifold or a network. Understanding the structure of the homology embedding can thus disclose geometric or topological information from the data. The study of the... | ['Marina Meilă', 'Yu-Chia Chen'] | 2021-07-23 | null | http://proceedings.neurips.cc/paper/2021/hash/842424a1d0595b76ec4fa03c46e8d755-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/842424a1d0595b76ec4fa03c46e8d755-Paper.pdf | neurips-2021-12 | ['stochastic-block-model'] | ['graphs'] | [ 4.14460376e-02 4.85215694e-01 7.10216239e-02 6.12968095e-02
-3.52939874e-01 -7.85266936e-01 1.50558412e-01 4.56975214e-02
-1.03090882e-01 8.10860991e-02 -2.81876355e-01 -5.13395727e-01
-6.36495471e-01 -8.74072373e-01 -8.55748951e-01 -1.01821208e+00
-5.73660851e-01 5.07435322e-01 1.29947573e-01 -1.26934707... | [7.324032783508301, 4.456172466278076] |
0c89e09c-224d-423f-a74b-3e134b8b59e5 | segvitv2-exploring-efficient-and-continual | 2306.06289 | null | https://arxiv.org/abs/2306.06289v1 | https://arxiv.org/pdf/2306.06289v1.pdf | SegViTv2: Exploring Efficient and Continual Semantic Segmentation with Plain Vision Transformers | We explore the capability of plain Vision Transformers (ViTs) for semantic segmentation using the encoder-decoder framework and introduce SegViTv2. In our work, we implement the decoder with the global attention mechanism inherent in ViT backbones and propose the lightweight Attention-to-Mask module that effectively co... | ['Yifan Liu', 'Chunhua Shen', 'Zhi Tian', 'Minh Hieu Phan', 'Liyang Liu', 'BoWen Zhang'] | 2023-06-09 | null | null | null | null | ['continual-semantic-segmentation'] | ['computer-vision'] | [ 1.62301928e-01 4.10660535e-01 -2.33816728e-02 -3.82324249e-01
-9.47622716e-01 -2.92570710e-01 1.45218909e-01 -1.99394777e-01
-7.25046396e-01 4.01665509e-01 -7.60306641e-02 -4.25871313e-01
5.25614023e-01 -8.93197477e-01 -1.05624437e+00 -3.69718134e-01
4.90548968e-01 2.93649167e-01 8.66805911e-01 -1.96452543... | [9.522019386291504, 0.15129072964191437] |
2c7bd6cd-3900-48d2-a947-66373b0eaefa | co-learning-meets-stitch-up-for-noisy-multi | 2307.00880 | null | https://arxiv.org/abs/2307.00880v1 | https://arxiv.org/pdf/2307.00880v1.pdf | Co-Learning Meets Stitch-Up for Noisy Multi-label Visual Recognition | In real-world scenarios, collected and annotated data often exhibit the characteristics of multiple classes and long-tailed distribution. Additionally, label noise is inevitable in large-scale annotations and hinders the applications of learning-based models. Although many deep learning based methods have been proposed... | ['Yi Yang', 'Linchao Zhu', 'Zongxin Yang', 'Chao Liang'] | 2023-07-03 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 4.18017685e-01 -5.64127922e-01 -1.23179041e-01 -6.41255200e-01
-1.33338547e+00 -6.14257812e-01 3.75367194e-01 1.10253491e-01
-2.70568848e-01 6.08092189e-01 9.45787430e-02 1.42198965e-01
-2.14362238e-02 -2.21526951e-01 -6.09502435e-01 -1.11669517e+00
6.45016909e-01 3.03973645e-01 -2.34900311e-01 2.48071238... | [9.43031120300293, 3.8495843410491943] |
affdb1b4-41e4-4e7c-a7fd-b320b76a4c23 | toward-fairness-through-fair-multi-exit | 2306.14518 | null | https://arxiv.org/abs/2306.14518v2 | https://arxiv.org/pdf/2306.14518v2.pdf | Toward Fairness Through Fair Multi-Exit Framework for Dermatological Disease Diagnosis | Fairness has become increasingly pivotal in medical image recognition. However, without mitigating bias, deploying unfair medical AI systems could harm the interests of underprivileged populations. In this paper, we observe that while features extracted from the deeper layers of neural networks generally offer higher a... | ['Tsung-Yi Ho', 'Yiyu Shi', 'Yu-Jen Chen', 'Hao-Wei Chung', 'Ching-Hao Chiu'] | 2023-06-26 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [ 2.08586916e-01 5.47734499e-01 -6.85628116e-01 -8.26948702e-01
-1.10659763e-01 2.24453956e-02 3.40229720e-01 2.58485764e-01
-8.87710989e-01 1.15833151e+00 -1.71973124e-01 -2.51105130e-01
-3.49482536e-01 -9.80238080e-01 -2.70325840e-01 -7.43522644e-01
2.19415985e-02 1.69186428e-01 -4.61071908e-01 -6.18042052... | [8.969541549682617, 5.156896591186523] |
4a890052-cea3-465c-88d3-eaeb209fe939 | speaker-profiling-in-multi-party | null | null | https://openreview.net/forum?id=iozkB44VlOl | https://openreview.net/pdf?id=iozkB44VlOl | Speaker Profiling in Multi-party Conversations | In a conversation, individual speakers respond uniquely. Consequently, a `one size fits all' technique is not the best way for a dialog agent to generate responses. While many studies design personalized dialog agents with the help of persona information of speakers, all of them assume that speaker persona is supplied ... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['speaker-profiling'] | ['speech'] | [ 4.79574911e-02 3.57782930e-01 1.79511830e-01 -7.96940207e-01
-8.23316753e-01 -6.90214932e-01 9.47350323e-01 -3.95893976e-02
-2.30608687e-01 4.46462512e-01 6.26684785e-01 -1.20219275e-01
-3.83194350e-02 -5.07885277e-01 -7.28849992e-02 -6.98721111e-01
1.96461365e-01 1.14801562e+00 8.19532722e-02 -4.92772430... | [12.781282424926758, 7.884990692138672] |
3d8b2e69-5f16-49c1-8521-5db3cf9e81d2 | memonav-selecting-informative-memories-for | 2208.09610 | null | https://arxiv.org/abs/2208.09610v1 | https://arxiv.org/pdf/2208.09610v1.pdf | MemoNav: Selecting Informative Memories for Visual Navigation | Image-goal navigation is a challenging task, as it requires the agent to navigate to a target indicated by an image in a previously unseen scene. Current methods introduce diverse memory mechanisms which save navigation history to solve this task. However, these methods use all observations in the memory for generating... | ['Zhaoxiang Zhang', 'Shuqi Mei', 'Yuran Yang', 'Xu Yang', 'Hongxin Li'] | 2022-08-20 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [ 2.25771498e-02 3.43742758e-01 1.27517805e-01 -4.41519618e-02
-4.75611836e-01 -3.41263175e-01 7.02186465e-01 -2.38619387e-01
-7.33628988e-01 8.29680383e-01 3.98997933e-01 -1.37177825e-01
-1.40186235e-01 -1.34977913e+00 -9.95963812e-01 -7.97927380e-01
-1.65759698e-01 5.38679361e-01 7.81514645e-01 -5.53020775... | [4.509902000427246, 0.45255789160728455] |
94bbd35e-df11-4cc5-9191-9060d3199d2e | hybrid-facial-expression-recognition-fer2013 | 2206.09509 | null | https://arxiv.org/abs/2206.09509v2 | https://arxiv.org/pdf/2206.09509v2.pdf | Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Classification and Prediction | Facial Expression Recognition is a vital research topic in most fields ranging from artificial intelligence and gaming to Human-Computer Interaction (HCI) and Psychology. This paper proposes a hybrid model for Facial Expression recognition, which comprises a Deep Convolutional Neural Network (DCNN) and Haar Cascade dee... | ['Kanyifeechukwu Jane Oguine', 'Daniel Ofuani', 'Hashim Ibrahim Bisallah', 'Ozioma Collins Oguine'] | 2022-06-19 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [-7.04893470e-02 -2.84328222e-01 1.20529413e-01 -5.86222172e-01
1.07528426e-01 1.88563809e-01 2.91568249e-01 -4.63839084e-01
-6.11522436e-01 4.49955940e-01 -3.04026932e-01 -2.30068788e-02
2.89557308e-01 -8.07477772e-01 -2.04264164e-01 -7.47972310e-01
-2.19324678e-01 -3.16486031e-01 -3.35398525e-01 -3.67594838... | [13.534934043884277, 1.775122880935669] |
f371429e-9ab6-43e1-8901-7cdc3c052dc8 | simplifying-graph-convolutional-networks | 1902.07153 | null | https://arxiv.org/abs/1902.07153v2 | https://arxiv.org/pdf/1902.07153v2.pdf | Simplifying Graph Convolutional Networks | Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations. GCNs derive inspiration primarily from recent deep learning approaches, and as a result, may inherit unnecessary complexity and redundant computation. In... | ['Amauri Holanda de Souza Jr.', 'Kilian Q. Weinberger', 'Tianyi Zhang', 'Tao Yu', 'Felix Wu', 'Christopher Fifty'] | 2019-02-19 | null | null | null | null | ['node-classification-on-non-homophilic', 'graph-regression'] | ['graphs', 'graphs'] | [ 1.53645054e-01 4.86318827e-01 -1.24726519e-01 -1.78051978e-01
-3.00570309e-01 -7.10514605e-01 7.96674132e-01 6.15292430e-01
-3.07876796e-01 5.86518466e-01 8.36758912e-02 -7.15410650e-01
-2.84317791e-01 -9.34247732e-01 -7.40786195e-01 -6.35819197e-01
-3.72305781e-01 1.13596842e-02 1.94705427e-01 -2.88262367... | [6.901280879974365, 6.05600118637085] |
21c1d2d9-e326-408a-b41b-429d54b78039 | netsentry-a-deep-learning-approach-to | 2202.09873 | null | https://arxiv.org/abs/2202.09873v2 | https://arxiv.org/pdf/2202.09873v2.pdf | NetSentry: A Deep Learning Approach to Detecting Incipient Large-scale Network Attacks | Machine Learning (ML) techniques are increasingly adopted to tackle ever-evolving high-profile network attacks, including DDoS, botnet, and ransomware, due to their unique ability to extract complex patterns hidden in data streams. These approaches are however routinely validated with data collected in the same environ... | ['Paul Patras', 'Haoyu Liu'] | 2022-02-20 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 1.62433863e-01 -7.19751418e-01 -3.44169587e-01 -1.15835175e-01
-9.61895287e-02 -9.44578826e-01 8.63865554e-01 2.60449667e-02
-3.80317450e-01 5.28862834e-01 -2.05391124e-01 -9.96163428e-01
-3.49219084e-01 -8.21475029e-01 -4.90773320e-01 -5.20973146e-01
-9.21066940e-01 6.02331460e-01 5.94885468e-01 -2.93504983... | [5.349460124969482, 7.3437604904174805] |
6cb0b512-b41f-4104-8711-6c9097df41b3 | look-into-person-joint-body-parsing-pose | 1804.01984 | null | http://arxiv.org/abs/1804.01984v1 | http://arxiv.org/pdf/1804.01984v1.pdf | Look into Person: Joint Body Parsing & Pose Estimation Network and A New Benchmark | Human parsing and pose estimation have recently received considerable
interest due to their substantial application potentials. However, the existing
datasets have limited numbers of images and annotations and lack a variety of
human appearances and coverage of challenging cases in unconstrained
environments. In this p... | ['Liang Lin', 'Xiaodan Liang', 'Ke Gong', 'Xiaohui Shen'] | 2018-04-05 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 1.50124565e-01 3.44122052e-02 -2.14489102e-01 -5.30515194e-01
-8.37385893e-01 -4.73742396e-01 2.73730546e-01 -3.61679196e-01
-2.71136254e-01 4.10624593e-01 1.73684835e-01 2.70095944e-01
2.66980737e-01 -3.61294150e-01 -5.82947075e-01 -4.66120839e-01
4.30019759e-02 5.71549773e-01 2.80682683e-01 -1.50811523... | [7.9774932861328125, -0.3683710992336273] |
b555ca2e-896b-4277-964e-a6bbe5f487ae | the-many-ai-challenges-of-hearthstone | 1907.06562 | null | https://arxiv.org/abs/1907.06562v1 | https://arxiv.org/pdf/1907.06562v1.pdf | The Many AI Challenges of Hearthstone | Games have benchmarked AI methods since the inception of the field, with classic board games such as Chess and Go recently leaving room for video games with related yet different sets of challenges. The set of AI problems associated with video games has in recent decades expanded from simply playing games to win, to pl... | ['Fernando De Mesentier Silva', 'Julian Togelius', 'Amy K. Hoover', 'Scott Lee'] | 2019-07-15 | null | null | null | null | ['board-games', 'card-games'] | ['playing-games', 'playing-games'] | [ 2.14167431e-01 1.68857388e-02 1.39324144e-01 2.49877304e-01
-4.32200521e-01 -1.00019777e+00 5.90657473e-01 -1.61890671e-01
-4.28556621e-01 6.41513348e-01 6.96103573e-02 -3.03948279e-02
-5.36265135e-01 -8.69749188e-01 -3.02367181e-01 -3.70865285e-01
-2.95445204e-01 7.29663789e-01 5.56865931e-01 -1.17983115... | [3.496347427368164, 1.4493240118026733] |
ba25b523-cf73-4277-8083-8143c591c01e | personalized-keyword-spotting-through-multi | 2206.13708 | null | https://arxiv.org/abs/2206.13708v1 | https://arxiv.org/pdf/2206.13708v1.pdf | Personalized Keyword Spotting through Multi-task Learning | Keyword spotting (KWS) plays an essential role in enabling speech-based user interaction on smart devices, and conventional KWS (C-KWS) approaches have concentrated on detecting user-agnostic pre-defined keywords. However, in practice, most user interactions come from target users enrolled in the device which motivates... | ['Simyung Chang', 'Inseop Chung', 'Byeonggeun Kim', 'Seunghan Yang'] | 2022-06-28 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 3.01254869e-01 -2.73034513e-01 -4.20348078e-01 -5.07092237e-01
-1.62885928e+00 -4.08421636e-01 1.85136139e-01 -4.01028305e-01
-1.60639212e-01 3.34086657e-01 2.30522946e-01 -5.70353866e-01
-1.79442883e-01 5.25971390e-02 -3.85073245e-01 -5.48800409e-01
2.62223095e-01 1.91975057e-01 2.12236628e-01 2.38138810... | [14.218816757202148, 6.341888904571533] |
5aea2eea-9de2-418c-8800-4b24e586cbea | important-object-identification-with-semi | 2203.02634 | null | https://arxiv.org/abs/2203.02634v1 | https://arxiv.org/pdf/2203.02634v1.pdf | Important Object Identification with Semi-Supervised Learning for Autonomous Driving | Accurate identification of important objects in the scene is a prerequisite for safe and high-quality decision making and motion planning of intelligent agents (e.g., autonomous vehicles) that navigate in complex and dynamic environments. Most existing approaches attempt to employ attention mechanisms to learn importan... | ['Chiho Choi', 'Masayoshi Tomizuka', 'Hengbo Ma', 'Haiming Gang', 'Jiachen Li'] | 2022-03-05 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [-5.82927326e-03 3.08677673e-01 -5.18355489e-01 -7.36788988e-01
-6.38896883e-01 -2.90908337e-01 7.96312928e-01 1.36230499e-01
-6.64557397e-01 7.05345869e-01 1.88690856e-01 -2.59382457e-01
-1.05122730e-01 -6.00751460e-01 -9.12272811e-01 -5.74758708e-01
2.47996300e-01 5.91999829e-01 3.18178415e-01 -2.80027211... | [6.249282360076904, 0.6862313747406006] |
242ef5b8-79dc-4fff-b2fe-3cffd6e8e67f | spec-summary-preference-decomposition-for-low | 2303.14011 | null | https://arxiv.org/abs/2303.14011v1 | https://arxiv.org/pdf/2303.14011v1.pdf | SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization | Neural abstractive summarization has been widely studied and achieved great success with large-scale corpora. However, the considerable cost of annotating data motivates the need for learning strategies under low-resource settings. In this paper, we investigate the problems of learning summarizers with only few example... | ['Hong-Han Shuai', 'Yun-Zhu Song', 'Yi-Syuan Chen'] | 2023-03-24 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 4.27010387e-01 -3.84147577e-02 -5.28347194e-01 -4.74884421e-01
-1.26402545e+00 -4.53256607e-01 5.72608948e-01 2.91718185e-01
-6.47590756e-01 1.07454014e+00 4.04073626e-01 3.54470394e-04
-1.17522292e-01 -6.13790572e-01 -7.67548621e-01 -6.33799911e-01
1.07339084e-01 5.26669502e-01 5.41645996e-02 -1.87537506... | [11.892318725585938, 8.942936897277832] |
445c5168-b440-4495-a033-a403fff387da | diffsound-discrete-diffusion-model-for-text | 2207.09983 | null | https://arxiv.org/abs/2207.09983v2 | https://arxiv.org/pdf/2207.09983v2.pdf | Diffsound: Discrete Diffusion Model for Text-to-sound Generation | Generating sound effects that humans want is an important topic. However, there are few studies in this area for sound generation. In this study, we investigate generating sound conditioned on a text prompt and propose a novel text-to-sound generation framework that consists of a text encoder, a Vector Quantized Variat... | ['Dong Yu', 'Yuexian Zou', 'Chao Weng', 'Wen Wang', 'Helin Wang', 'Jianwei Yu', 'Dongchao Yang'] | 2022-07-20 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 4.81292512e-03 -7.72976205e-02 3.47781986e-01 4.71207462e-02
-7.84886301e-01 -1.42092392e-01 2.89507508e-01 -2.17325240e-01
-1.20664202e-01 5.77584505e-01 4.13204253e-01 -1.82704881e-01
2.54685879e-01 -8.97965014e-01 -4.95553464e-01 -8.60200047e-01
5.92599034e-01 -2.22619902e-02 3.87209624e-01 -3.24731797... | [15.275729179382324, 6.245105266571045] |
5460949b-78e5-4375-b017-cb1ec65f624b | effective-cloud-detection-and-segmentation | 1809.10801 | null | http://arxiv.org/abs/1809.10801v1 | http://arxiv.org/pdf/1809.10801v1.pdf | Effective Cloud Detection and Segmentation using a Gradient-Based Algorithm for Satellite Imagery; Application to improve PERSIANN-CCS | Being able to effectively identify clouds and monitor their evolution is one
important step toward more accurate quantitative precipitation estimation and
forecast. In this study, a new gradient-based cloud-image segmentation
technique is developed using tools from image processing techniques. This
method integrates mo... | ['Kuo-lin Hsu', 'Soroosh Sorooshian', 'Negin Hayatbini', 'Yunji Zhang', 'Fuqing Zhang'] | 2018-09-27 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 2.41924852e-01 -6.20752394e-01 2.33441040e-01 -4.11844999e-01
-3.90719414e-01 -6.37632489e-01 5.14143229e-01 1.76227018e-01
-5.59310794e-01 8.67517829e-01 -4.74386215e-01 -7.77680635e-01
-1.53535247e-01 -1.29552674e+00 1.25156343e-02 -9.24830437e-01
-3.56200457e-01 6.76210403e-01 3.25352214e-02 -5.38451672... | [9.70119571685791, -1.7621225118637085] |
12ff4e87-3ef1-40df-a96f-c901129c0253 | daml-chinese-named-entity-recognition-with-a | null | null | https://openreview.net/forum?id=N-DZvl4bQsO | https://openreview.net/pdf?id=N-DZvl4bQsO | DAML: Chinese Named Entity Recognition with a fusion method of data-augmentation and meta-learning | Overfitting is still a common problem in NER with insufficient data. Latest methods such as Transfer Learning, which focuses on storing knowledge gained while solving one task and applying it to a different but related task, or Model-Agnostic Meta-Learning (MAML), which learns a model parameter initialization that gene... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-9.25458968e-02 3.29778567e-02 9.65342820e-02 -4.62338656e-01
-6.49388850e-01 -4.50269222e-01 6.45119786e-01 -3.05465423e-02
-9.39140916e-01 1.06480277e+00 3.38520080e-01 -1.06335677e-01
8.81064776e-03 -8.90056133e-01 -5.37536383e-01 -4.76495266e-01
5.94253719e-01 5.55216849e-01 7.50282481e-02 -5.89725256... | [9.840950965881348, 9.53619384765625] |
187c9e45-f26c-4da8-ac7f-cd15dba855e2 | liquid-structural-state-space-models | 2209.12951 | null | https://arxiv.org/abs/2209.12951v1 | https://arxiv.org/pdf/2209.12951v1.pdf | Liquid Structural State-Space Models | A proper parametrization of state transition matrices of linear state-space models (SSMs) followed by standard nonlinearities enables them to efficiently learn representations from sequential data, establishing the state-of-the-art on a large series of long-range sequence modeling benchmarks. In this paper, we show tha... | ['Daniela Rus', 'Alexander Amini', 'Makram Chahine', 'Tsun-Hsuan Wang', 'Mathias Lechner', 'Ramin Hasani'] | 2022-09-26 | null | null | null | null | ['spo2-estimation', 'heart-rate-estimation', 'long-range-modeling'] | ['medical', 'medical', 'natural-language-processing'] | [ 4.71408814e-01 -1.76526770e-01 -4.72325921e-01 -2.82445759e-01
-3.98336351e-01 -4.38787431e-01 8.36924136e-01 -1.05273277e-01
-4.17737544e-01 3.62597078e-01 2.52509236e-01 -7.40893781e-01
-2.97073513e-01 -2.06202880e-01 -1.16459978e+00 -6.96458459e-01
-5.07491052e-01 4.28754449e-01 2.86905289e-01 -1.13406949... | [7.5192413330078125, 3.4071807861328125] |
e229bb24-1cad-4886-bb15-27deb663ed9f | meeting-summarization-a-survey-of-the-state | 2212.08206 | null | https://arxiv.org/abs/2212.08206v1 | https://arxiv.org/pdf/2212.08206v1.pdf | Meeting Summarization: A Survey of the State of the Art | Information overloading requires the need for summarizers to extract salient information from the text. Currently, there is an overload of dialogue data due to the rise of virtual communication platforms. The rise of Covid-19 has led people to rely on online communication platforms like Zoom, Slack, Microsoft Teams, Di... | ['Arman Kabiri', 'Lakshmi Prasanna Kumar'] | 2022-12-16 | null | null | null | null | ['meeting-summarization'] | ['natural-language-processing'] | [ 2.09415093e-01 1.98226795e-01 -2.21418500e-01 -3.53412300e-01
-1.13735104e+00 -6.87925637e-01 4.60822761e-01 9.42629039e-01
-9.61429253e-02 9.29006636e-01 1.03025150e+00 -8.64540711e-02
-8.17011520e-02 -2.17344210e-01 2.90186793e-01 1.64359715e-02
1.45436645e-01 3.50151092e-01 -5.01105152e-02 -5.49640119... | [12.608647346496582, 9.399872779846191] |
83d9ccf1-b945-4708-a0a6-1433aeb77656 | a-statistical-method-for-object-counting | 1807.08335 | null | http://arxiv.org/abs/1807.08335v1 | http://arxiv.org/pdf/1807.08335v1.pdf | A Statistical Method for Object Counting | In this paper we present a new object counting method that is intended for
counting similarly sized and mostly round objects. Unlike many other algorithms
of the same purpose, the proposed method does not rely on identifying every
object, it uses statistical data obtained from the image instead. The method is
evaluated... | ['Jans Glagolevs', 'Karlis Freivalds'] | 2018-07-22 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 1.83134720e-01 -3.64712328e-01 1.86760053e-01 -4.13785614e-02
-1.67730048e-01 -3.95821869e-01 4.76887852e-01 4.87182826e-01
-9.63238478e-01 1.01480818e+00 -3.89250070e-01 4.24118293e-03
5.55935428e-02 -7.81121850e-01 -7.68776089e-02 -5.49520731e-01
1.06387779e-01 1.10190880e+00 1.01479816e+00 3.41369331... | [14.673741340637207, -3.182264566421509] |
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