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28c2f34c-6497-429a-8e6b-fa42c6e64885 | an-active-learning-approach-for-reducing | 1909.02344 | null | https://arxiv.org/abs/1909.02344v1 | https://arxiv.org/pdf/1909.02344v1.pdf | An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis | Automated skin lesion analysis is very crucial in clinical practice, as skin cancer is among the most common human malignancy. Existing approaches with deep learning have achieved remarkable performance on this challenging task, however, heavily relying on large-scale labelled datasets. In this paper, we present a nove... | ['Pheng-Ann Heng', 'Cheng Xue', 'Qi Dou', 'Hao Chen', 'Xueying Shi', 'Jing Qin'] | 2019-09-05 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 8.01236928e-01 1.70694679e-01 -5.92214406e-01 -2.83621401e-01
-1.52455056e+00 -2.73167074e-01 6.78355098e-01 5.97080529e-01
-7.96833634e-01 7.71711051e-01 -3.67157646e-02 -8.02636053e-03
-2.57386386e-01 -6.62271738e-01 -3.22585016e-01 -1.16037560e+00
1.78527802e-01 2.37409845e-01 3.51857275e-01 2.07275093... | [15.465348243713379, -2.7514255046844482] |
85e338fa-ffb8-4060-8e7e-f05269ce573b | attention-wise-masked-graph-contrastive | 2206.08262 | null | https://arxiv.org/abs/2206.08262v1 | https://arxiv.org/pdf/2206.08262v1.pdf | Attention-wise masked graph contrastive learning for predicting molecular property | Accurate and efficient prediction of the molecular properties of drugs is one of the fundamental problems in drug research and development. Recent advancements in representation learning have been shown to greatly improve the performance of molecular property prediction. However, due to limited labeled data, supervised... | ['Lei Deng', 'Xuejun Liu', 'Yibiao Huang', 'Hui Liu'] | 2022-05-02 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 7.12812543e-01 8.86081532e-02 -5.38965225e-01 -3.81239384e-01
-6.53690040e-01 -4.57958281e-01 2.45470628e-01 6.15581870e-01
3.09128105e-03 1.15640593e+00 3.88518497e-02 -5.99498749e-01
-2.94206906e-02 -8.95724416e-01 -9.60642278e-01 -8.15625370e-01
-1.38731897e-01 1.78881496e-01 -1.96734786e-01 -7.16614872... | [5.1072773933410645, 5.913553714752197] |
c66ac54e-53a6-4475-9c46-95119f1416e9 | augdmc-data-augmentation-guided-deep-multiple | 2306.13023 | null | https://arxiv.org/abs/2306.13023v1 | https://arxiv.org/pdf/2306.13023v1.pdf | AugDMC: Data Augmentation Guided Deep Multiple Clustering | Clustering aims to group similar objects together while separating dissimilar ones apart. Thereafter, structures hidden in data can be identified to help understand data in an unsupervised manner. Traditional clustering methods such as k-means provide only a single clustering for one data set. Deep clustering methods s... | ['Juhua Hu', 'Maham Rashid', 'Enbei Liu', 'Jiawei Yao'] | 2023-06-22 | null | null | null | null | ['clustering', 'deep-clustering', 'deep-clustering'] | ['methodology', 'miscellaneous', 'natural-language-processing'] | [-1.72285721e-01 -1.24550559e-01 -1.94507703e-01 -5.33897698e-01
-6.05280280e-01 -4.05153364e-01 3.97360474e-01 3.38959664e-01
-1.10063739e-01 3.11732870e-02 2.64324069e-01 3.90674293e-01
-4.92813736e-01 -5.68573654e-01 -4.20033395e-01 -1.01537478e+00
7.27927089e-02 6.69475436e-01 -2.09834114e-01 1.30633965... | [9.032363891601562, 3.595463752746582] |
3883685b-e282-41e3-8c46-d48ad74bc2c3 | xcoref-cross-document-coreference-resolution | 2109.05252 | null | https://arxiv.org/abs/2109.05252v1 | https://arxiv.org/pdf/2109.05252v1.pdf | XCoref: Cross-document Coreference Resolution in the Wild | Datasets and methods for cross-document coreference resolution (CDCR) focus on events or entities with strict coreference relations. They lack, however, annotating and resolving coreference mentions with more abstract or loose relations that may occur when news articles report about controversial and polarized events. ... | ['Bela Gipp', 'Karsten Donnay', 'Felix Hamborg', 'Anastasia Zhukova'] | 2021-09-11 | xcoref-cross-document-coreference-resolution-1 | https://dl.acm.org/doi/abs/10.1007/978-3-030-96957-8_25 | https://www.gipp.com/wp-content/papercite-data/pdf/zhukova2022.pdf | information-for-a-better-world-shaping-the | ['cross-document-coreference-resolution'] | ['natural-language-processing'] | [-3.79702821e-02 4.40151751e-01 -5.88316858e-01 -3.43800485e-01
-8.78289938e-01 -8.83652508e-01 1.14058125e+00 6.24959171e-01
-5.47272146e-01 1.17983365e+00 1.05526686e+00 -3.65230381e-01
-3.84074122e-01 -8.11857104e-01 -5.77351570e-01 -3.37294787e-01
1.39222905e-01 1.01570725e+00 2.77659118e-01 -9.49915469... | [9.383354187011719, 9.509909629821777] |
c9471da5-eb17-4751-91a5-123dd258e6dd | semi-supervised-learning-with-sparse | 1610.00520 | null | http://arxiv.org/abs/1610.00520v1 | http://arxiv.org/pdf/1610.00520v1.pdf | Semi-supervised Learning with Sparse Autoencoders in Phone Classification | We propose the application of a semi-supervised learning method to improve
the performance of acoustic modelling for automatic speech recognition based on
deep neural net- works. As opposed to unsupervised initialisation followed by
supervised fine tuning, our method takes advantage of both unlabelled and
labelled data... | ['Giampiero Salvi', 'Akash Kumar Dhaka'] | 2016-10-03 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 4.17425454e-01 5.02394676e-01 1.27654141e-02 -8.95415783e-01
-1.12231731e+00 -1.62463844e-01 8.06600809e-01 -1.01656877e-01
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-1.44044578e-01 9.61405516e-01 4.17896837e-01 -4.49331524... | [14.424020767211914, 6.653627872467041] |
f649718b-e642-4021-9d31-01590d8093df | deeply-supervised-rotation-equivariant | 1807.02804 | null | http://arxiv.org/abs/1807.02804v1 | http://arxiv.org/pdf/1807.02804v1.pdf | Deeply Supervised Rotation Equivariant Network for Lesion Segmentation in Dermoscopy Images | Automatic lesion segmentation in dermoscopy images is an essential step for
computer-aided diagnosis of melanoma. The dermoscopy images exhibits rotational
and reflectional symmetry, however, this geometric property has not been
encoded in the state-of-the-art convolutional neural networks based skin lesion
segmentatio... | ['Pheng-Ann Heng', 'Chi-Wing Fu', 'Lequan Yu', 'Xiaomeng Li'] | 2018-07-08 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 5.75584352e-01 2.35469580e-01 -4.09666449e-01 -2.15634584e-01
-5.93713164e-01 -3.23274821e-01 3.06200117e-01 -4.67076272e-01
-5.62887549e-01 2.02656299e-01 -1.69181395e-02 -4.95821089e-01
-1.21281736e-01 -5.30078769e-01 -5.61902165e-01 -9.54561532e-01
3.90682608e-01 6.92439377e-02 1.44963652e-01 -2.95955300... | [15.623648643493652, -2.9225449562072754] |
6adaec8a-438a-4698-a1a9-dd993dcbc6a2 | transcut-transparent-object-segmentation-from | 1511.06853 | null | http://arxiv.org/abs/1511.06853v1 | http://arxiv.org/pdf/1511.06853v1.pdf | TransCut: Transparent Object Segmentation from a Light-Field Image | The segmentation of transparent objects can be very useful in computer vision
applications. However, because they borrow texture from their background and
have a similar appearance to their surroundings, transparent objects are not
handled well by regular image segmentation methods. We propose a method that
overcomes t... | ['Rin-ichiro Taniguchi', 'Yichao Xu', 'Atsushi Shimada', 'Hajime Nagahara'] | 2015-11-21 | transcut-transparent-object-segmentation-from-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Xu_TransCut_Transparent_Object_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Xu_TransCut_Transparent_Object_ICCV_2015_paper.pdf | iccv-2015-12 | ['transparent-objects'] | ['computer-vision'] | [ 3.68137211e-01 -1.40436456e-01 -5.07187471e-02 -4.65979815e-01
-9.95554477e-02 -3.90937299e-01 1.73125193e-01 -1.53844133e-01
-2.83407927e-01 5.76755345e-01 -5.41257381e-01 -2.64157020e-02
3.87161195e-01 -9.52373981e-01 -3.76118511e-01 -9.60148275e-01
5.49866855e-01 4.67361301e-01 1.08892846e+00 3.21683317... | [9.734906196594238, -2.779841184616089] |
d4113f70-3a58-4d39-bba1-347c965fbf0c | quantitative-evaluation-of-base-and-detail | 1808.09411 | null | http://arxiv.org/abs/1808.09411v1 | http://arxiv.org/pdf/1808.09411v1.pdf | Quantitative Evaluation of Base and Detail Decomposition Filters Based on their Artifacts | This paper introduces a quantitative evaluation of filters that seek to
separate an image into its large-scale variations, the base layer, and its
fine-scale variations, the detail layer. Such methods have proliferated with
the development of HDR imaging and the proposition of many new tone-mapping
operators. We argue ... | ['Jean-Michel Morel', 'Charles Hessel'] | 2018-08-28 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 3.57502818e-01 -1.32599294e-01 4.48824346e-01 -1.85705647e-01
-6.67861342e-01 -3.86291355e-01 6.97874427e-01 1.03889115e-01
-1.47786230e-01 8.52655232e-01 2.33997494e-01 4.32798304e-02
-4.21041578e-01 -5.37120223e-01 -1.92682579e-01 -8.47444355e-01
-2.24477388e-02 -3.68502289e-02 6.25639379e-01 -3.16566169... | [11.293927192687988, -2.236971378326416] |
6237e181-ba14-42eb-9021-3fc8d3ec5c8f | enriching-entity-grids-and-graphs-with | null | null | https://aclanthology.org/W15-5619 | https://aclanthology.org/W15-5619.pdf | Enriching entity grids and graphs with discourse relations: the impact in local coherence evaluation | null | ["M{\\'a}rcio de S. Dias", 'Thiago A. S. Pardo'] | 2015-11-01 | null | null | null | ws-2015-11 | ['coherence-evaluation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.379876136779785, 3.7160580158233643] |
65c9f6a2-f128-48d2-8cec-010ee40d0cb2 | regclr-a-self-supervised-framework-for | 2211.01165 | null | https://arxiv.org/abs/2211.01165v1 | https://arxiv.org/pdf/2211.01165v1.pdf | RegCLR: A Self-Supervised Framework for Tabular Representation Learning in the Wild | Recent advances in self-supervised learning (SSL) using large models to learn visual representations from natural images are rapidly closing the gap between the results produced by fully supervised learning and those produced by SSL on downstream vision tasks. Inspired by this advancement and primarily motivated by the... | ['Varun Ganapathi', 'Byung-Hak Kim', 'Weiyao Wang'] | 2022-11-02 | null | null | null | null | ['table-recognition'] | ['computer-vision'] | [ 6.31117404e-01 2.52129078e-01 -5.99879734e-02 -3.57405365e-01
-8.12329769e-01 -6.41113400e-01 7.76159704e-01 3.17268103e-01
-3.12974691e-01 5.14360070e-01 3.52633715e-01 -3.18092048e-01
-4.63165231e-02 -6.13295019e-01 -1.04062927e+00 -4.68703091e-01
1.09478109e-01 5.79546690e-01 -1.77033022e-01 -1.87719181... | [10.00249195098877, 2.226872444152832] |
fe3023dd-7b2d-424e-b893-148592e91859 | plug-and-play-autoencoders-for-conditional | 2010.02983 | null | https://arxiv.org/abs/2010.02983v2 | https://arxiv.org/pdf/2010.02983v2.pdf | Plug and Play Autoencoders for Conditional Text Generation | Text autoencoders are commonly used for conditional generation tasks such as style transfer. We propose methods which are plug and play, where any pretrained autoencoder can be used, and only require learning a mapping within the autoencoder's embedding space, training embedding-to-embedding (Emb2Emb). This reduces the... | ['James Henderson', 'Noah A. Smith', 'Ivan Montero', 'Nikolaos Pappas', 'Florian Mai'] | 2020-10-06 | null | https://aclanthology.org/2020.emnlp-main.491 | https://aclanthology.org/2020.emnlp-main.491.pdf | emnlp-2020-11 | ['conditional-text-generation'] | ['natural-language-processing'] | [ 1.19162448e-01 4.05372769e-01 -3.92430983e-02 -4.19067949e-01
-4.22392249e-01 -5.78932524e-01 8.53439450e-01 -3.02304059e-01
-5.47555089e-01 8.23127687e-01 4.64227706e-01 -4.93810892e-01
5.75013697e-01 -7.82766998e-01 -1.03278267e+00 -5.01916468e-01
3.40358227e-01 6.41610146e-01 7.79407695e-02 -4.00624007... | [11.655662536621094, 9.471370697021484] |
50044378-2fdb-4c2d-bcbf-864809268ab0 | wonderm-skin-lesion-classification-with-fine | 1808.03426 | null | https://arxiv.org/abs/1808.03426v3 | https://arxiv.org/pdf/1808.03426v3.pdf | WonDerM: Skin Lesion Classification with Fine-tuned Neural Networks | As skin cancer is one of the most frequent cancers globally, accurate, non-invasive dermoscopy-based diagnosis becomes essential and promising. A task of the Part 3 of the ISIC Skin Image Analysis Challenge at MICCAI 2018 is to predict seven disease classes with skin lesion images, including melanoma (MEL), melanocytic... | ['Hong-Hee Won', 'Sang-Hyuk Jung', 'Yeong Chan Lee'] | 2018-08-10 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 8.07025552e-01 1.42520741e-01 -2.93841392e-01 -5.61200175e-03
-4.07197833e-01 -5.11182010e-01 6.56449437e-01 5.37493117e-02
-3.62144172e-01 6.77740872e-01 -2.30390742e-01 -5.80826879e-01
-1.90944821e-01 -5.96515119e-01 -2.35115394e-01 -9.23537076e-01
2.50659823e-01 9.80952755e-02 2.59735852e-01 6.46934137... | [15.690374374389648, -3.000481128692627] |
b7686392-1f20-4ae0-8ade-dcdcf34b9597 | maximal-divergence-sequential-autoencoder-for | null | null | https://openreview.net/forum?id=ByloIiCqYQ | https://openreview.net/pdf?id=ByloIiCqYQ | Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection | Due to the sharp increase in the severity of the threat imposed by software vulnerabilities, the detection of vulnerabilities in binary code has become an important concern in the software industry, such as the embedded systems industry, and in the field of computer security. However, most of the work in binary code vu... | ['Trung Le', 'Dinh Phung', 'Tue Le', 'Tuan Nguyen', 'Olivier De Vel', 'Paul Montague', 'Lizhen Qu'] | 2019-05-01 | null | null | null | iclr-2019-5 | ['vulnerability-detection', 'computer-security'] | ['miscellaneous', 'miscellaneous'] | [ 9.76498351e-02 -1.70609877e-01 -2.92847455e-01 -2.50800371e-01
-4.92636651e-01 -9.92974162e-01 3.30755055e-01 4.44291651e-01
-1.64723977e-01 4.86303359e-01 1.23284280e-01 -6.79544449e-01
-2.36363355e-02 -6.84638619e-01 -5.16550004e-01 -5.60711503e-01
-2.92635590e-01 -5.36254272e-02 4.40147430e-01 -2.62842327... | [7.087465286254883, 7.781181335449219] |
b44b944e-e97b-4b46-a4f7-32f54bee89d9 | explore-and-match-end-to-end-video-grounding | 2201.10168 | null | https://arxiv.org/abs/2201.10168v4 | https://arxiv.org/pdf/2201.10168v4.pdf | Explore-And-Match: Bridging Proposal-Based and Proposal-Free With Transformer for Sentence Grounding in Videos | Natural Language Video Grounding (NLVG) aims to localize time segments in an untrimmed video according to sentence queries. In this work, we present a new paradigm named Explore-And-Match for NLVG that seamlessly unifies the strengths of two streams of NLVG methods: proposal-free and proposal-based; the former explores... | ['Changick Kim', 'Minki Jeong', 'Sumin Lee', 'Inyong Koo', 'Jinyoung Park', 'Sangmin Woo'] | 2022-01-25 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [ 4.97961678e-02 -2.60288604e-02 -6.87691391e-01 -4.18217599e-01
-1.23830807e+00 -7.05852509e-01 5.29866219e-01 -1.19167186e-01
-4.46012974e-01 6.54868841e-01 3.23422819e-01 -1.93087429e-01
-1.55974776e-02 -6.07262671e-01 -1.02978873e+00 -6.21194899e-01
-1.04515053e-01 3.71583968e-01 6.32523715e-01 -1.32946953... | [9.907205581665039, 0.6544288396835327] |
8e463456-a4a5-46bc-bbc0-c28427b68d7b | non-contact-atrial-fibrillation-detection | 2110.07610 | null | https://arxiv.org/abs/2110.07610v2 | https://arxiv.org/pdf/2110.07610v2.pdf | Non-contact Atrial Fibrillation Detection from Face Videos by Learning Systolic Peaks | Objective: We propose a non-contact approach for atrial fibrillation (AF) detection from face videos. Methods: Face videos, electrocardiography (ECG), and contact photoplethysmography (PPG) from 100 healthy subjects and 100 AF patients are recorded. Data recordings from healthy subjects are all labeled as healthy. Two ... | ['Xiaobai Li', 'Tapio Seppänen', 'Mikko Tulppo', 'Juhani Junttila', 'Zhaodong Sun'] | 2021-10-14 | null | null | null | null | ['photoplethysmography-ppg', 'heart-rate-variability', 'atrial-fibrillation-detection', 'electrocardiography-ecg'] | ['medical', 'medical', 'medical', 'methodology'] | [ 3.59710813e-01 -1.88592479e-01 -1.93929762e-01 -4.71704990e-01
-7.09845960e-01 -6.85457289e-01 -2.43057594e-01 -2.96631038e-01
-5.45117147e-02 7.40146995e-01 -7.32071400e-02 -2.96614438e-01
2.07141042e-04 -5.66252053e-01 -1.20262951e-01 -8.20420504e-01
-6.38061583e-01 2.01114953e-01 -5.58852375e-01 4.11420941... | [14.153963088989258, 3.135256767272949] |
bad44840-2b8d-4cbf-b509-7e862a996cd3 | svarah-evaluating-english-asr-systems-on | 2305.15760 | null | https://arxiv.org/abs/2305.15760v1 | https://arxiv.org/pdf/2305.15760v1.pdf | Svarah: Evaluating English ASR Systems on Indian Accents | India is the second largest English-speaking country in the world with a speaker base of roughly 130 million. Thus, it is imperative that automatic speech recognition (ASR) systems for English should be evaluated on Indian accents. Unfortunately, Indian speakers find a very poor representation in existing English ASR b... | ['Mitesh M. Khapra', 'Pratyush Kumar', 'Kaushal Bhogale', 'Abhigyan Raman', 'Janki Nawale', 'Sai Sundaresan', 'Vignesh Nagarajan', 'Sakshi Joshi', 'Tahir Javed'] | 2023-05-25 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [-2.72964001e-01 -4.13382659e-03 6.14962727e-02 -6.24466300e-01
-1.41610146e+00 -8.64886999e-01 5.40087044e-01 -1.51647985e-01
-5.39270818e-01 8.21308732e-01 9.28862572e-01 -5.48914015e-01
3.19080770e-01 -3.94462347e-01 -1.57386258e-01 -4.34327722e-01
1.30452499e-01 6.97697103e-01 -1.92501873e-01 -8.59633267... | [14.2860689163208, 6.79240608215332] |
6a99e919-e59a-4009-abc4-01ae3074bffd | deep-tensor-networks-with-matrix-product | 2209.09098 | null | https://arxiv.org/abs/2209.09098v1 | https://arxiv.org/pdf/2209.09098v1.pdf | Deep tensor networks with matrix product operators | We introduce deep tensor networks, which are exponentially wide neural networks based on the tensor network representation of the weight matrices. We evaluate the proposed method on the image classification (MNIST, FashionMNIST) and sequence prediction (cellular automata) tasks. In the image classification case, deep t... | ['Bojan Žunkovič'] | 2022-09-16 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 1.40687257e-01 -3.73140931e-01 -2.41938382e-01 1.18532576e-01
-1.13328449e-01 -8.59695017e-01 4.93567854e-01 4.93891276e-02
-5.56876898e-01 5.09272099e-01 1.82738543e-01 -7.14761376e-01
-5.81652485e-02 -5.00905335e-01 -9.59500849e-01 -8.20292115e-01
-3.63948941e-01 5.22261798e-01 2.61998177e-01 -3.18262517... | [6.04067850112915, 5.0236921310424805] |
6cc1aeb2-4bde-4911-83b4-dc8ae66986fc | cxtrack-improving-3d-point-cloud-tracking | 2211.08542 | null | https://arxiv.org/abs/2211.08542v2 | https://arxiv.org/pdf/2211.08542v2.pdf | CXTrack: Improving 3D Point Cloud Tracking with Contextual Information | 3D single object tracking plays an essential role in many applications, such as autonomous driving. It remains a challenging problem due to the large appearance variation and the sparsity of points caused by occlusion and limited sensor capabilities. Therefore, contextual information across two consecutive frames is cr... | ['Song-Hai Zhang', 'Yu-Kun Lai', 'Yuan-Chen Guo', 'Tian-Xing Xu'] | 2022-11-12 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_CXTrack_Improving_3D_Point_Cloud_Tracking_With_Contextual_Information_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_CXTrack_Improving_3D_Point_Cloud_Tracking_With_Contextual_Information_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-single-object-tracking', '3d-object-tracking'] | ['computer-vision', 'computer-vision'] | [-1.21552251e-01 -6.31083786e-01 -2.78519571e-01 -2.14382708e-01
-4.64388609e-01 -5.85674822e-01 3.56484741e-01 5.67711145e-02
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3.44891921e-02 6.78930059e-02 1.00443816e+00 -1.54215693... | [6.566880226135254, -2.294078826904297] |
6608b1b8-594d-4ec1-958b-77829269241f | leveraging-predictions-in-power-system | 2305.12044 | null | https://arxiv.org/abs/2305.12044v1 | https://arxiv.org/pdf/2305.12044v1.pdf | Leveraging Predictions in Power System Frequency Control: an Adaptive Approach | Ensuring the frequency stability of electric grids with increasing renewable resources is a key problem in power system operations. In recent years, a number of advanced controllers have been designed to optimize frequency control. These controllers, however, almost always assume that the net load in the system remains... | ['Baosen Zhang', 'Yuanyuan Shi', 'Guanya Shi', 'Wenqi Cui'] | 2023-05-20 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-2.68889666e-01 -2.15511769e-01 -4.53139842e-01 2.54601717e-01
1.61577724e-02 -9.38457549e-01 2.99098581e-01 8.78948998e-03
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-4.76062745e-01 1.53365418e-01 -2.85532683e-01 -6.86298966... | [5.6922383308410645, 2.577162981033325] |
1261815b-6d67-4ad5-9608-d426a5dca321 | emotiongesture-audio-driven-diverse-emotional | 2305.18891 | null | https://arxiv.org/abs/2305.18891v1 | https://arxiv.org/pdf/2305.18891v1.pdf | EmotionGesture: Audio-Driven Diverse Emotional Co-Speech 3D Gesture Generation | Generating vivid and diverse 3D co-speech gestures is crucial for various applications in animating virtual avatars. While most existing methods can generate gestures from audio directly, they usually overlook that emotion is one of the key factors of authentic co-speech gesture generation. In this work, we propose Emo... | ['Xin Yu', 'Haoran Xin', 'Jie Hou', 'Lincheng Li', 'Chen Liu', 'Xingqun Qi'] | 2023-05-30 | null | null | null | null | ['gesture-generation'] | ['robots'] | [-4.24325392e-02 -1.92545190e-01 1.71615139e-01 -3.01461577e-01
-7.69022465e-01 -6.46212876e-01 6.87399209e-01 -7.24481523e-01
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-1.10487796e-01 1.49322689e-01 -1.14686787e-01 -3.77921671... | [5.721756935119629, -0.18667438626289368] |
733eeaca-da0e-4433-b037-a8a795908afc | sgm-net-semantic-guided-matting-net | 2208.07496 | null | https://arxiv.org/abs/2208.07496v1 | https://arxiv.org/pdf/2208.07496v1.pdf | SGM-Net: Semantic Guided Matting Net | Human matting refers to extracting human parts from natural images with high quality, including human detail information such as hair, glasses, hat, etc. This technology plays an essential role in image synthesis and visual effects in the film industry. When the green screen is not available, the existing human matting... | ['Chun Liu', 'Mengjie Hu', 'Donghan Yang', 'Wenfeng Sun', 'Qing Song'] | 2022-08-16 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 3.80990237e-01 -4.05038930e-02 5.34380600e-02 -1.98228776e-01
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6.88521624e-01 3.25696468e-01 8.03530753e-01 -3.08589250... | [10.645964622497559, -0.9915552735328674] |
aab2f32b-1626-473b-b273-ca468e2fec72 | a-comparison-of-time-based-models-for | 2306.13076 | null | https://arxiv.org/abs/2306.13076v1 | https://arxiv.org/pdf/2306.13076v1.pdf | A Comparison of Time-based Models for Multimodal Emotion Recognition | Emotion recognition has become an important research topic in the field of human-computer interaction. Studies on sound and videos to understand emotions focused mainly on analyzing facial expressions and classified 6 basic emotions. In this study, the performance of different sequence models in multi-modal emotion rec... | ['Sena Nur Cavsak', 'Selahattin Serdar Helli', 'Ege Kesim'] | 2023-06-22 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-6.75557852e-02 -4.25435841e-01 3.43277037e-01 -3.49940717e-01
-2.58458823e-01 -2.79961854e-01 3.60744357e-01 -1.40133291e-01
-6.27180040e-01 3.94491911e-01 1.44001827e-01 4.01886344e-01
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-3.09733331e-01 -2.87999004e-01 -4.46975604e-02 -3.55256796... | [13.412208557128906, 5.235416889190674] |
5b29bd82-4a0b-421d-ba82-78962ec73198 | s2rl-do-we-really-need-to-perceive-all-states | 2206.11054 | null | https://arxiv.org/abs/2206.11054v1 | https://arxiv.org/pdf/2206.11054v1.pdf | S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning? | Collaborative multi-agent reinforcement learning (MARL) has been widely used in many practical applications, where each agent makes a decision based on its own observation. Most mainstream methods treat each local observation as an entirety when modeling the decentralized local utility functions. However, they ignore t... | ['Chao Wu', 'Yunfeng Shao', 'Furui Liu', 'Kun Kuang', 'Jiahui Li', 'Yinchuan Li', 'Shuang Luo'] | 2022-06-20 | null | null | null | null | ['starcraft-ii'] | ['playing-games'] | [-7.08242893e-01 1.59900159e-01 -6.89445972e-01 -5.72386347e-02
-4.97884005e-01 -2.74834961e-01 4.49140012e-01 2.11196482e-01
-4.00130212e-01 9.57634091e-01 3.23042035e-01 1.76051542e-01
-1.13085248e-01 -7.95454204e-01 -5.66808701e-01 -1.05156684e+00
-3.89601700e-02 5.00269532e-01 4.43649828e-01 -4.06103045... | [3.7670648097991943, 2.046658992767334] |
1ab10da0-5ae1-4b9b-8009-8556a99bf9b4 | meta-meta-classification-for-one-shot | 2004.08083 | null | https://arxiv.org/abs/2004.08083v4 | https://arxiv.org/pdf/2004.08083v4.pdf | Meta-Meta Classification for One-Shot Learning | We present a new approach, called meta-meta classification, to learning in small-data settings. In this approach, one uses a large set of learning problems to design an ensemble of learners, where each learner has high bias and low variance and is skilled at solving a specific type of learning problem. The meta-meta cl... | ['Chris Jermaine', 'Dipak Chaudhari', 'Arkabandhu Chowdhury', 'Swarat Chaudhuri'] | 2020-04-17 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 3.74900430e-01 1.33235931e-01 -4.06521767e-01 -3.43616456e-01
-1.21992397e+00 -2.72053689e-01 5.68353653e-01 5.35656869e-01
-5.24471164e-01 7.37158537e-01 -1.75669014e-01 -7.37532154e-02
-4.78682935e-01 -8.58081818e-01 -6.01451695e-01 -6.77547395e-01
2.96452232e-02 7.20871687e-01 1.96773320e-01 -3.21516067... | [9.951714515686035, 3.1183838844299316] |
c476d3b4-da04-49f7-8833-b8c42a12cae4 | cognitive-compositional-semantics-using | null | null | https://aclanthology.info/papers/S14-1018/s14-1018 | https://www.aclweb.org/anthology/S14-1018v2 | Cognitive Compositional Semantics using Continuation Dependencies | null | ['William Schuler', 'Adam Wheeler'] | 2014-08-01 | cognitive-compositional-semantics-using-1 | https://aclanthology.org/S14-1018 | https://aclanthology.org/S14-1018.pdf | semeval-2014-8 | ['implicatures'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.5392378568649292, 15.86925220489502] |
121133fe-1d27-497e-af07-a205ca29d3e5 | structure-aware-incremental-learning-with | 2305.01204 | null | https://arxiv.org/abs/2305.01204v1 | https://arxiv.org/pdf/2305.01204v1.pdf | Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems | Recommender systems now consume large-scale data and play a significant role in improving user experience. Graph Neural Networks (GNNs) have emerged as one of the most effective recommender system models because they model the rich relational information. The ever-growing volume of data can make training GNNs prohibiti... | ['Mark Coates', 'Jianye Hao', 'Chen Ma', 'Ruiming Tang', 'Antonios Valkanas', 'Yingxue Zhang', 'Yuening Wang'] | 2023-05-02 | null | null | null | null | ['incremental-learning'] | ['methodology'] | [-2.75485992e-01 1.73015505e-01 -4.59245265e-01 -1.59016132e-01
8.68990049e-02 -5.03976166e-01 3.44423860e-01 2.12799147e-01
-3.97370458e-01 6.63043559e-01 4.84630525e-01 -1.12198986e-01
-3.86845887e-01 -1.01543605e+00 -6.30101025e-01 -3.81208628e-01
-1.68922961e-01 6.66346312e-01 3.34108531e-01 -8.14047635... | [10.138126373291016, 5.589784622192383] |
089b219e-e384-4bf7-a24e-f67ef3fa1c72 | the-art-of-transfer-learning-an-adaptive-and | 2305.00520 | null | https://arxiv.org/abs/2305.00520v1 | https://arxiv.org/pdf/2305.00520v1.pdf | The ART of Transfer Learning: An Adaptive and Robust Pipeline | Transfer learning is an essential tool for improving the performance of primary tasks by leveraging information from auxiliary data resources. In this work, we propose Adaptive Robust Transfer Learning (ART), a flexible pipeline of performing transfer learning with generic machine learning algorithms. We establish the ... | ['Chenglong Ye', 'Yunan Wu', 'Boxiang Wang'] | 2023-04-30 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 2.94449806e-01 5.15967682e-02 -6.92981303e-01 -3.27985644e-01
-1.63865077e+00 -4.10169721e-01 4.10900712e-01 -4.39663120e-02
-3.13309669e-01 9.42592442e-01 4.53431308e-02 -4.63307619e-01
-3.08313429e-01 -3.92635584e-01 -1.29396141e+00 -5.75733125e-01
-4.83962059e-01 5.25086522e-01 -1.08830519e-01 9.86874476... | [9.603504180908203, 3.0564982891082764] |
9d2fac36-1b71-4ae8-9642-356fec7196ac | disease-oriented-image-embedding-with-pseudo | 2108.06518 | null | https://arxiv.org/abs/2108.06518v1 | https://arxiv.org/pdf/2108.06518v1.pdf | Disease-oriented image embedding with pseudo-scanner standardization for content-based image retrieval on 3D brain MRI | To build a robust and practical content-based image retrieval (CBIR) system that is applicable to a clinical brain MRI database, we propose a new framework -- Disease-oriented image embedding with pseudo-scanner standardization (DI-PSS) -- that consists of two core techniques, data harmonization and a dimension reducti... | ['Kenichi Oishi', 'Hitoshi Iyatomi', 'Yusuke Chayama', 'Kumpei Ikuta', 'Yuto Onga', 'Hayato Arai'] | 2021-08-14 | null | null | null | null | ['content-based-image-retrieval', 'skull-stripping'] | ['computer-vision', 'medical'] | [ 2.96354860e-01 -2.92104464e-02 3.07034433e-01 -4.83357221e-01
-8.72164965e-01 -1.66459858e-01 5.21856844e-01 -1.95182160e-01
-6.73646450e-01 4.35180336e-01 3.60537887e-01 1.26608163e-01
-4.05199677e-01 -6.57677174e-01 -2.46758103e-01 -7.92329013e-01
-4.73002017e-01 6.27146065e-01 2.02015817e-01 -7.62297288... | [14.242191314697266, -1.7375420331954956] |
8e1d7111-4aff-4172-a9ec-32e7f6bd6356 | searching-efficient-3d-architectures-with | 2007.16100 | null | https://arxiv.org/abs/2007.16100v2 | https://arxiv.org/pdf/2007.16100v2.pdf | Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution | Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive safely. Given the limited hardware resources, existing 3D perception models are not able to recognize small instances (e.g., pedestrians, cyclists) very well due to the low-resolution voxelization and aggressive downsampling. To... | ['Song Han', 'Yujun Lin', 'Haotian Tang', 'Hanrui Wang', 'Shengyu Zhao', 'Zhijian Liu', 'Ji Lin'] | 2020-07-31 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6421_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123730681.pdf | eccv-2020-8 | ['robust-3d-semantic-segmentation', 'lidar-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [-2.10286483e-01 5.47750182e-02 -2.56315649e-01 -3.82222831e-01
-5.19641221e-01 -3.55022728e-01 5.31485260e-01 -3.03729653e-01
-2.40347683e-01 6.43245578e-02 -2.51966178e-01 -7.17547119e-01
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2.26295236e-02 5.89130580e-01 8.73901010e-01 -3.42460811... | [7.734495639801025, -2.631298065185547] |
926bdaa9-9e40-4a8f-b780-8d43a057def2 | tetra-nerf-representing-neural-radiance | 2304.09987 | null | https://arxiv.org/abs/2304.09987v2 | https://arxiv.org/pdf/2304.09987v2.pdf | Tetra-NeRF: Representing Neural Radiance Fields Using Tetrahedra | Neural Radiance Fields (NeRFs) are a very recent and very popular approach for the problems of novel view synthesis and 3D reconstruction. A popular scene representation used by NeRFs is to combine a uniform, voxel-based subdivision of the scene with an MLP. Based on the observation that a (sparse) point cloud of the s... | ['Torsten Sattler', 'Jonas Kulhanek'] | 2023-04-19 | null | null | null | null | ['3d-reconstruction', 'novel-view-synthesis'] | ['computer-vision', 'computer-vision'] | [ 3.21815461e-01 -7.28047341e-02 4.55656379e-01 -4.06634301e-01
-6.20437264e-01 -2.95537233e-01 7.51389980e-01 1.72410235e-01
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-1.96857631e-01 -1.45074821e+00 -9.24978912e-01 -5.92185855e-01
7.62766227e-02 5.81677675e-01 8.33599195e-02 -5.26568949... | [9.340560913085938, -3.0862107276916504] |
e5e6ddbf-2a6f-4fd1-a992-67ae781313c7 | a-weak-supervision-approach-for-predicting | null | null | https://aclanthology.org/2022.coling-1.400 | https://aclanthology.org/2022.coling-1.400.pdf | A Weak Supervision Approach for Predicting Difficulty of Technical Interview Questions | Predicting difficulty of questions is crucial for technical interviews. However, such questions are long-form and more open-ended than factoid and multiple choice questions explored so far for question difficulty prediction. Existing models also require large volumes of candidate response data for training. We study we... | ['Indrajit Bhattacharya', 'Tapas Nayak', 'Pratik Saini', 'Subhasish Ghosh', 'Arpita Kundu'] | null | null | null | null | coling-2022-10 | ['question-generation'] | ['natural-language-processing'] | [ 5.02263494e-02 8.56827557e-01 -6.25051409e-02 -8.48228455e-01
-1.30875766e+00 -8.24225366e-01 1.79709822e-01 2.82466054e-01
-2.75855392e-01 4.91910875e-01 8.92888784e-01 -8.49452138e-01
-3.84930581e-01 -5.00789702e-01 -1.94591388e-01 2.55140096e-01
4.25065041e-01 8.05842221e-01 3.24213095e-02 -6.71146154... | [11.52406120300293, 8.104268074035645] |
5ee46b03-0657-4ad8-8e89-1fa4e2b3efb0 | c-norm-a-neural-approach-to-few-shot-entity | null | null | https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03886-8 | https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-020-03886-8.pdf | C-Norm: a neural approach to few-shot entity normalization | Entity normalization is an important information extraction task which has gained renewed attention in the last decade, particularly in the biomedical and life science domains. In these domains, and more generally in all specialized domains, this task is still challenging for the latest machine learning-based approache... | ['Claire Nédellec', 'Pierre Zweigenbaum', 'Robert Bossy', 'Louise Deléger', 'Arnaud Ferré'] | 2020-12-29 | null | null | null | bmc-bioinformatics-2020-12 | ['medical-concept-normalization'] | ['medical'] | [ 3.47591788e-01 -4.73058186e-02 -3.90293032e-01 -3.56802166e-01
-2.10838839e-01 -5.25668785e-02 5.03447056e-01 7.29846895e-01
-9.70735669e-01 1.10205078e+00 2.60888994e-01 -1.03826895e-02
-3.38807762e-01 -7.64681816e-01 -2.98813749e-02 -5.90286195e-01
1.10106103e-01 2.11260930e-01 6.14852846e-01 -4.23408359... | [9.671523094177246, 9.34350872039795] |
b1e326b2-70fc-411f-8ca4-87e55197cd55 | transform-invariant-convolutional-neural-1 | 2206.13388 | null | https://arxiv.org/abs/2206.13388v1 | https://arxiv.org/pdf/2206.13388v1.pdf | Transform-Invariant Convolutional Neural Networks for Image Classification and Search | This paper demonstrates that a simple modification of the variational autoencoder (VAE) formalism enables the method to identify and classify rotated and distorted digits. In particular, the conventional objective (cost) function employed during the training process of a VAE both quantifies the agreement between the in... | ['David Yevick'] | 2022-06-18 | null | null | null | null | ['rotated-mnist'] | ['computer-vision'] | [ 3.56446028e-01 1.34451136e-01 7.83998370e-02 -3.08783233e-01
-8.00567627e-01 -8.17889035e-01 9.42967594e-01 -3.46549213e-01
-3.40315163e-01 8.00557554e-01 1.73572302e-01 -1.53040346e-02
-1.72257647e-01 -6.24437034e-01 -4.72857118e-01 -1.10186756e+00
2.68349230e-01 6.53389633e-01 -5.28616428e-01 1.69869438... | [15.012696266174316, 6.201170444488525] |
377bb1ff-2057-4108-b511-e96c1c31b060 | adversarial-attack-based-on-prediction | 2306.01809 | null | https://arxiv.org/abs/2306.01809v1 | https://arxiv.org/pdf/2306.01809v1.pdf | Adversarial Attack Based on Prediction-Correction | Deep neural networks (DNNs) are vulnerable to adversarial examples obtained by adding small perturbations to original examples. The added perturbations in existing attacks are mainly determined by the gradient of the loss function with respect to the inputs. In this paper, the close relationship between gradient-based ... | ['Fangjun Huang', 'Chen Wan'] | 2023-06-02 | null | null | null | null | ['adversarial-attack'] | ['adversarial'] | [ 1.79067533e-02 -9.80727300e-02 2.19933480e-01 -4.24514413e-02
-3.95202786e-01 -5.96031368e-01 4.48072463e-01 -1.44796655e-01
-5.79041004e-01 7.77775645e-01 -2.84968257e-01 -3.35749775e-01
-5.73878409e-03 -8.87884021e-01 -6.64806426e-01 -9.79052484e-01
-1.15594506e-01 -7.83044174e-02 6.07029319e-01 -6.67346239... | [5.57551383972168, 7.960184574127197] |
53116d69-2b2c-4fe8-8b7f-19152ad9803b | a-family-of-cognitively-realistic-parsing | null | null | https://openreview.net/forum?id=YKrSi_BzRh | https://openreview.net/pdf?id=YKrSi_BzRh | A Family of Cognitively Realistic Parsing Environments for Deep Reinforcement Learning | The hierarchical syntactic structure of natural language is a key feature of human cognition that enables us to recursively construct arbitrarily long sentences supporting communication of complex, relational information. In this work, we describe a framework in which learning cognitively-realistic left-corner parsers ... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-6.79238141e-03 9.44461703e-01 2.29479000e-01 -4.48507935e-01
-1.01370454e+00 -8.74322593e-01 3.63631874e-01 2.49182597e-01
-5.64168990e-01 1.02092636e+00 5.17937005e-01 -9.42945719e-01
-2.14451730e-01 -8.14086139e-01 -6.54627800e-01 -1.34801015e-01
-3.47792685e-01 5.81166744e-01 2.44521081e-01 -6.01087391... | [4.072351932525635, 1.387505054473877] |
edcdea0f-9054-447d-9884-17d5294d9a8d | deep-person-generation-a-survey-from-the | 2109.02081 | null | https://arxiv.org/abs/2109.02081v1 | https://arxiv.org/pdf/2109.02081v1.pdf | Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis | Deep person generation has attracted extensive research attention due to its wide applications in virtual agents, video conferencing, online shopping and art/movie production. With the advancement of deep learning, visual appearances (face, pose, cloth) of a person image can be easily generated or manipulated on demand... | ['Tao Mei', 'Zhoujun Li', 'Tong Shen', 'Wei zhang', 'Tong Sha'] | 2021-09-05 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [ 3.86007190e-01 4.63327974e-01 2.75779843e-01 -2.60730833e-01
-1.51449472e-01 -3.05481583e-01 7.59863734e-01 -7.05114305e-01
1.85241908e-01 8.61006498e-01 2.27525339e-01 3.78245264e-01
2.76787460e-01 -8.36312830e-01 -4.87842441e-01 -8.23707283e-01
8.12270194e-02 5.97100854e-01 -4.43407923e-01 -4.38578814... | [12.024898529052734, -0.7886855006217957] |
b433bae6-460b-4ec3-a742-7efe811313cf | multi-label-image-classification-with | 1612.01082 | null | http://arxiv.org/abs/1612.01082v3 | http://arxiv.org/pdf/1612.01082v3.pdf | Multi-Label Image Classification with Regional Latent Semantic Dependencies | Deep convolution neural networks (CNN) have demonstrated advanced performance
on single-label image classification, and various progress also have been made
to apply CNN methods on multi-label image classification, which requires to
annotate objects, attributes, scene categories etc. in a single shot. Recent
state-of-t... | ['Jun-Jie Zhang', 'Chunhua Shen', 'Qi Wu', 'Jianfeng Lu', 'Jian Zhang'] | 2016-12-04 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 1.92564085e-01 -3.65968257e-01 -3.91812533e-01 -4.71847087e-01
-7.05043256e-01 -3.03661436e-01 2.59768814e-01 2.05793485e-01
-3.20624352e-01 4.51249719e-01 -9.65485070e-03 3.96682695e-02
1.06525056e-01 -5.61152041e-01 -6.01705194e-01 -8.84935737e-01
2.40396932e-01 2.50635147e-01 3.62882346e-01 2.99498230... | [9.792252540588379, 4.022725582122803] |
d47bb08a-5098-4f88-8aa3-702004dd7f89 | irrgn-an-implicit-relational-reasoning-graph | 2212.00482 | null | https://arxiv.org/abs/2212.00482v1 | https://arxiv.org/pdf/2212.00482v1.pdf | IRRGN: An Implicit Relational Reasoning Graph Network for Multi-turn Response Selection | The task of response selection in multi-turn dialogue is to find the best option from all candidates. In order to improve the reasoning ability of the model, previous studies pay more attention to using explicit algorithms to model the dependencies between utterances, which are deterministic, limited and inflexible. In... | ['Wei Peng', 'Yuanchen Ju', 'Xuewei Guo', 'Hengwei Dai', 'Jingcheng Deng'] | 2022-12-01 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 5.70777804e-02 7.57327199e-01 -2.13490482e-02 -7.74363577e-01
-6.33492053e-01 -5.22499502e-01 6.18104875e-01 1.81851424e-02
-3.06567103e-01 3.53996843e-01 8.11057031e-01 -6.79221988e-01
1.07853092e-01 -9.12112772e-01 -3.59988540e-01 -1.80128068e-01
5.59801221e-01 8.81511271e-01 2.66889304e-01 -9.50382173... | [12.365357398986816, 7.950145721435547] |
b77d6b3f-2cc8-41f3-ac76-ff0c070b1210 | edvr-video-restoration-with-enhanced | 1905.02716 | null | https://arxiv.org/abs/1905.02716v1 | https://arxiv.org/pdf/1905.02716v1.pdf | EDVR: Video Restoration with Enhanced Deformable Convolutional Networks | Video restoration tasks, including super-resolution, deblurring, etc, are drawing increasing attention in the computer vision community. A challenging benchmark named REDS is released in the NTIRE19 Challenge. This new benchmark challenges existing methods from two aspects: (1) how to align multiple frames given large ... | ['Chen Change Loy', 'Xintao Wang', 'Kelvin C. K. Chan', 'Chao Dong', 'Ke Yu'] | 2019-05-07 | null | null | null | null | ['video-enhancement', 'video-restoration'] | ['computer-vision', 'computer-vision'] | [ 0.1663237 -0.77225274 0.01562331 0.00757188 -0.6237472 -0.36257818
0.44273847 -0.63179207 -0.4001946 0.74202716 0.7526974 0.18791288
-0.10236313 -0.42022565 -0.686941 -0.75569934 0.186762 -0.32034642
0.43926764 -0.42899647 0.16191009 0.45313445 -1.4400182 0.39569834
0.9468335 0.7029963 0.... | [11.082847595214844, -1.933424949645996] |
942fba5e-73a3-4d39-8046-50537acf249a | crowdsourcing-cybersecurity-cyber-attack | 1702.07745 | null | http://arxiv.org/abs/1702.07745v1 | http://arxiv.org/pdf/1702.07745v1.pdf | Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media | Social media is often viewed as a sensor into various societal events such as
disease outbreaks, protests, and elections. We describe the use of social media
as a crowdsourced sensor to gain insight into ongoing cyber-attacks. Our
approach detects a broad range of cyber-attacks (e.g., distributed denial of
service (DDO... | ['Ramakrishnan Naren', 'Lu Chang-Tien', 'Wang Gang', 'Jan Steve', 'Ji Taoran', 'Khandpur Rupinder Paul'] | 2017-02-24 | null | null | null | null | ['cyber-attack-detection'] | ['miscellaneous'] | [-1.49986625e-01 -5.09807616e-02 -3.13060582e-01 -2.40440547e-01
-7.32393265e-01 -1.10977077e+00 9.00438547e-01 1.32056618e+00
-3.05421531e-01 5.25669456e-01 4.70052153e-01 -3.77287120e-01
2.89591640e-01 -1.34917045e+00 -5.44587612e-01 1.87742472e-01
-3.61943096e-01 4.55594867e-01 7.41163194e-01 -3.34465504... | [8.366738319396973, 9.469409942626953] |
fe9df5cf-779f-4512-9f0b-e8a290dd5f26 | on-transforming-reinforcement-learning-by | 2212.14164 | null | https://arxiv.org/abs/2212.14164v2 | https://arxiv.org/pdf/2212.14164v2.pdf | On Transforming Reinforcement Learning by Transformer: The Development Trajectory | Transformer, originally devised for natural language processing, has also attested significant success in computer vision. Thanks to its super expressive power, researchers are investigating ways to deploy transformers to reinforcement learning (RL) and the transformer-based models have manifested their potential in re... | ['DaCheng Tao', 'Yixin Chen', 'Ya zhang', 'Li Shen', 'Shengchao Hu'] | 2022-12-29 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [-2.51277722e-02 -2.47196872e-02 -3.76519322e-01 -1.03595786e-01
-4.89036828e-01 -9.05433893e-01 7.94848859e-01 -4.52680677e-01
-6.44059658e-01 6.32958770e-01 9.70405713e-02 -5.64904451e-01
-2.91678518e-01 -7.69093990e-01 -6.96043134e-01 -8.00277710e-01
-1.74560905e-01 6.41926825e-01 1.02436639e-01 -8.38291824... | [4.120167255401611, 1.6389027833938599] |
dafe45da-f9cb-4686-8b26-f1ded5103ac7 | self-supervised-image-to-point-distillation | 2301.05709 | null | https://arxiv.org/abs/2301.05709v2 | https://arxiv.org/pdf/2301.05709v2.pdf | Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss | An effective framework for learning 3D representations for perception tasks is distilling rich self-supervised image features via contrastive learning. However, image-to point representation learning for autonomous driving datasets faces two main challenges: 1) the abundance of self-similarity, which results in the con... | ['Steven L. Waslander', 'Liam Paull', 'Ali Harakeh', 'Tianshu Kuai', 'Jordan S. K. Hu', 'Anas Mahmoud'] | 2023-01-12 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Mahmoud_Self-Supervised_Image-to-Point_Distillation_via_Semantically_Tolerant_Contrastive_Loss_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Mahmoud_Self-Supervised_Image-to-Point_Distillation_via_Semantically_Tolerant_Contrastive_Loss_CVPR_2023_paper.pdf | cvpr-2023-1 | ['semantic-textual-similarity'] | ['natural-language-processing'] | [ 3.22358370e-01 1.96868867e-01 -4.60319638e-01 -7.44793057e-01
-9.28284883e-01 -4.60870206e-01 6.21825695e-01 4.44605857e-01
-3.50199372e-01 1.79092005e-01 -6.59833103e-02 -4.76651974e-02
2.00914480e-02 -8.24820936e-01 -1.07648289e+00 -5.62151313e-01
9.60614905e-02 6.07443154e-01 4.76385355e-01 -4.07455266... | [8.038609504699707, -3.2873220443725586] |
6abecf8a-0701-4774-9222-cad475d665ac | annotating-arguments-in-a-corpus-of-opinion | null | null | https://aclanthology.org/2022.lrec-1.201 | https://aclanthology.org/2022.lrec-1.201.pdf | Annotating Arguments in a Corpus of Opinion Articles | Interest in argument mining has resulted in an increasing number of argument annotated corpora. However, most focus on English texts with explicit argumentative discourse markers, such as persuasive essays or legal documents. Conversely, we report on the first extensive and consolidated Portuguese argument annotation p... | ['Miguel Won', 'Bruno Martins', 'Paula Carvalho', 'Rui Sousa-Silva', 'Henrique Lopes Cardoso', 'Luís Trigo', 'Gil Rocha'] | null | null | null | null | lrec-2022-6 | ['argument-mining'] | ['natural-language-processing'] | [ 2.88053632e-01 1.05648613e+00 -5.47464132e-01 -1.73154548e-01
-9.33058321e-01 -1.13626444e+00 1.01519430e+00 9.33188140e-01
-4.70727593e-01 1.05933213e+00 9.95396018e-01 -8.49779904e-01
-4.80549634e-01 -4.03589487e-01 -3.19532067e-01 -3.22940379e-01
3.04762930e-01 6.60530031e-01 2.05980256e-01 -4.82660204... | [9.515731811523438, 9.679492950439453] |
5316eb73-dded-4b14-a2d8-5e0941d3b4a4 | augmented-sbert-data-augmentation-method-for | 2010.08240 | null | https://arxiv.org/abs/2010.08240v2 | https://arxiv.org/pdf/2010.08240v2.pdf | Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks | There are two approaches for pairwise sentence scoring: Cross-encoders, which perform full-attention over the input pair, and Bi-encoders, which map each input independently to a dense vector space. While cross-encoders often achieve higher performance, they are too slow for many practical use cases. Bi-encoders, on th... | ['Iryna Gurevych', 'Johannes Daxenberger', 'Nils Reimers', 'Nandan Thakur'] | 2020-10-16 | null | https://aclanthology.org/2021.naacl-main.28 | https://aclanthology.org/2021.naacl-main.28.pdf | naacl-2021-4 | ['sentence-pair-modeling'] | ['natural-language-processing'] | [ 3.23412716e-01 -1.61477253e-02 1.97450500e-02 -7.94182479e-01
-1.51466262e+00 -6.27076507e-01 5.72274625e-01 3.15290600e-01
-5.62457502e-01 8.74752641e-01 4.14568245e-01 -2.05646664e-01
3.50582242e-01 -4.74974990e-01 -8.17161322e-01 -3.93180341e-01
1.76277563e-01 7.71440506e-01 2.79057711e-01 -4.16608602... | [10.909427642822266, 8.566184043884277] |
27727233-02d6-4da2-8dc4-2df4a97582d7 | speech-to-speech-translation-between | 1910.00795 | null | https://arxiv.org/abs/1910.00795v2 | https://arxiv.org/pdf/1910.00795v2.pdf | Speech-to-speech Translation between Untranscribed Unknown Languages | In this paper, we explore a method for training speech-to-speech translation tasks without any transcription or linguistic supervision. Our proposed method consists of two steps: First, we train and generate discrete representation with unsupervised term discovery with a discrete quantized autoencoder. Second, we train... | ['Andros Tjandra', 'Satoshi Nakamura', 'Sakriani Sakti'] | 2019-10-02 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [ 6.28447652e-01 4.64888871e-01 -4.45566885e-02 -5.46140373e-01
-1.13403738e+00 -5.94770014e-01 8.04920018e-01 -2.33794093e-01
-9.73783731e-02 1.03188574e+00 1.60685048e-01 -7.71942139e-01
6.31262839e-01 -6.05092764e-01 -8.51241231e-01 -4.89032120e-01
3.48066598e-01 8.76883984e-01 -4.44726795e-02 -4.11455929... | [14.586840629577637, 7.055795192718506] |
c673fc17-7df5-4fc7-a520-c63126bce827 | densepose-from-wifi | 2301.00250 | null | https://arxiv.org/abs/2301.00250v1 | https://arxiv.org/pdf/2301.00250v1.pdf | DensePose From WiFi | Advances in computer vision and machine learning techniques have led to significant development in 2D and 3D human pose estimation from RGB cameras, LiDAR, and radars. However, human pose estimation from images is adversely affected by occlusion and lighting, which are common in many scenarios of interest. Radar and Li... | ['Fernando de la Torre', 'Dong Huang', 'Jiaqi Geng'] | 2022-12-31 | null | null | null | null | ['body-detection', '3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 3.97447735e-01 -2.03006759e-01 1.68411553e-01 -4.98514533e-01
-6.13836527e-01 -4.89054590e-01 1.77251045e-02 6.95942715e-02
-6.06510997e-01 5.16574442e-01 -8.68809298e-02 2.55545199e-01
1.54140756e-01 -9.39938664e-01 -7.54246116e-01 -5.51579595e-01
-1.77946478e-01 3.32461685e-01 -1.83841735e-01 1.78755671... | [6.858797550201416, 0.351593554019928] |
d7a4ed53-b447-47dc-be26-3d709a3c1d07 | explaining-classes-through-stable-word | null | null | https://aclanthology.org/2022.findings-acl.85 | https://aclanthology.org/2022.findings-acl.85.pdf | Explaining Classes through Stable Word Attributions | Input saliency methods have recently become a popular tool for explaining predictions of deep learning models in NLP. Nevertheless, there has been little work investigating methods for aggregating prediction-level explanations to the class level, nor has a framework for evaluating such class explanations been establish... | ['Veronika Laippala', 'Filip Ginter', 'Amanda Myntti', 'Aki-Juhani Kyröläinen', 'Samuel Rönnqvist'] | null | null | null | null | findings-acl-2022-5 | ['xlm-r'] | ['natural-language-processing'] | [ 5.81475794e-01 8.86752844e-01 -6.31524980e-01 -7.84422755e-01
-7.86655843e-01 -4.98439193e-01 8.87410223e-01 4.54477221e-01
-9.25111026e-03 7.57515073e-01 7.12251842e-01 -7.37816393e-01
-6.29523516e-01 -3.34860504e-01 -6.54321194e-01 -2.48069808e-01
3.41861695e-01 6.48513377e-01 8.09799284e-02 -1.33234203... | [9.44970417022705, 6.6270270347595215] |
9e5a6336-37d0-485f-8957-a171ee441342 | fots-fast-oriented-text-spotting-with-a | 1801.01671 | null | http://arxiv.org/abs/1801.01671v2 | http://arxiv.org/pdf/1801.01671v2.pdf | FOTS: Fast Oriented Text Spotting with a Unified Network | Incidental scene text spotting is considered one of the most difficult and
valuable challenges in the document analysis community. Most existing methods
treat text detection and recognition as separate tasks. In this work, we
propose a unified end-to-end trainable Fast Oriented Text Spotting (FOTS)
network for simultan... | ['Yu Qiao', 'Shi Yan', 'Ding Liang', 'Xuebo Liu', 'Junjie Yan', 'Dagui Chen'] | 2018-01-05 | fots-fast-oriented-text-spotting-with-a-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Liu_FOTS_Fast_Oriented_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Liu_FOTS_Fast_Oriented_CVPR_2018_paper.pdf | cvpr-2018-6 | ['text-spotting'] | ['computer-vision'] | [ 2.98514992e-01 -3.38148355e-01 1.23738898e-02 -2.36720949e-01
-7.30059624e-01 -3.74416888e-01 7.46028960e-01 3.63887288e-03
-6.12873435e-01 3.04770544e-02 2.16305275e-02 -2.76054859e-01
2.69450605e-01 -4.63614464e-01 -5.62727511e-01 -5.38032472e-01
4.17511135e-01 5.80642760e-01 3.68348658e-01 7.56242648... | [11.952794075012207, 2.2958860397338867] |
f4bcc869-96c8-4ce5-9035-9b5a776830e5 | test-time-style-shifting-handling-arbitrary | 2306.04911 | null | https://arxiv.org/abs/2306.04911v2 | https://arxiv.org/pdf/2306.04911v2.pdf | Test-Time Style Shifting: Handling Arbitrary Styles in Domain Generalization | In domain generalization (DG), the target domain is unknown when the model is being trained, and the trained model should successfully work on an arbitrary (and possibly unseen) target domain during inference. This is a difficult problem, and despite active studies in recent years, it remains a great challenge. In this... | ['Jaekyun Moon', 'Soyeong Kim', 'Dong-Jun Han', 'Jungwuk Park'] | 2023-06-08 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [ 2.77258337e-01 -1.92746118e-01 -3.23702514e-01 -5.85024536e-01
-5.41739702e-01 -7.13180482e-01 3.51578414e-01 -1.48977175e-01
-6.49062991e-02 1.15585792e+00 -2.59952933e-01 -9.43528190e-02
-2.16165736e-01 -9.34465647e-01 -6.67962670e-01 -8.28982711e-01
3.19345564e-01 9.36974585e-01 4.72617656e-01 -1.20026879... | [10.16139030456543, 3.2389824390411377] |
e30f62c4-f011-4e6f-a91b-8fd40001209f | materials-property-prediction-with | 2211.02235 | null | https://arxiv.org/abs/2211.02235v2 | https://arxiv.org/pdf/2211.02235v2.pdf | Materials Property Prediction with Uncertainty Quantification: A Benchmark Study | Uncertainty quantification (UQ) has increasing importance in building robust high-performance and generalizable materials property prediction models. It can also be used in active learning to train better models by focusing on getting new training data from uncertain regions. There are several categories of UQ methods ... | ['Jianjun Hu', 'Sadman Sadeed Omee', 'Rongzhi Dong', 'Daniel Varivoda'] | 2022-11-04 | null | null | null | null | ['formation-energy', 'total-energy'] | ['miscellaneous', 'miscellaneous'] | [ 8.97963867e-02 1.30978882e-01 -4.62065727e-01 -4.30081666e-01
-1.11583638e+00 -2.50075191e-01 2.21249253e-01 6.19737685e-01
6.78997859e-02 1.45734537e+00 -2.05518231e-02 -2.20588505e-01
-4.47798759e-01 -1.28131831e+00 -9.86585379e-01 -1.09141755e+00
-8.21270496e-02 7.27109671e-01 2.49425232e-01 4.71591391... | [5.208134174346924, 5.38541316986084] |
55ff4b4b-95e6-46ac-9f8a-77c4fe256afc | minimum-barrier-salient-object-detection-at | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Zhang_Minimum_Barrier_Salient_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Zhang_Minimum_Barrier_Salient_ICCV_2015_paper.pdf | Minimum Barrier Salient Object Detection at 80 FPS | We propose a highly efficient, yet powerful, salient object detection method based on the Minimum Barrier Distance (MBD) Transform. The MBD transform is robust to pixel-value fluctuation, and thus can be effectively applied on raw pixels without region abstraction. We present an approximate MBD transform algorithm wit... | ['Radomir Mech', 'Brian Price', 'Xiaohui Shen', 'Zhe Lin', 'Stan Sclaroff', 'Jianming Zhang'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['video-salient-object-detection'] | ['computer-vision'] | [ 4.35142547e-01 -1.57782212e-01 -2.22284704e-01 1.67217806e-01
-8.51894975e-01 -3.92942846e-01 4.32754338e-01 3.04339021e-01
-4.36075628e-01 4.85366702e-01 -5.71700782e-02 -2.63942599e-01
5.01107275e-01 -6.08646572e-01 -7.25572228e-01 -7.83160985e-01
-8.30742717e-02 -2.32422575e-01 1.28047693e+00 -1.73302412... | [9.553237915039062, -0.7622674703598022] |
d81996a5-1d0e-4073-bb56-bf791423d742 | learning-to-reason-deductively-math-word | 2203.10316 | null | https://arxiv.org/abs/2203.10316v4 | https://arxiv.org/pdf/2203.10316v4.pdf | Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction | Solving math word problems requires deductive reasoning over the quantities in the text. Various recent research efforts mostly relied on sequence-to-sequence or sequence-to-tree models to generate mathematical expressions without explicitly performing relational reasoning between quantities in the given context. While... | ['Wei Lu', 'Jierui Li', 'Zhanming Jie'] | 2022-03-19 | null | https://aclanthology.org/2022.acl-long.410 | https://aclanthology.org/2022.acl-long.410.pdf | acl-2022-5 | ['math-word-problem-solving', 'relational-reasoning', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'natural-language-processing', 'reasoning', 'time-series'] | [ 4.61259753e-01 4.11364913e-01 -1.75750762e-01 -6.26674294e-01
-8.96947384e-01 -8.78760755e-01 8.24028909e-01 4.22534943e-01
-2.09126621e-02 8.59587908e-01 2.82374769e-01 -1.05601692e+00
-1.53871800e-03 -1.38557673e+00 -8.71163428e-01 -1.11363130e-02
2.58760631e-01 5.46044648e-01 -3.21362205e-02 -4.54669148... | [9.66939926147461, 7.4560065269470215] |
53ebc0c5-e1fd-4d0c-b429-cb4e9555cc0a | latent-space-unsupervised-semantic | 2207.11067 | null | https://arxiv.org/abs/2207.11067v2 | https://arxiv.org/pdf/2207.11067v2.pdf | Latent Space Unsupervised Semantic Segmentation | The development of compact and energy-efficient wearable sensors has led to an increase in the availability of biosignals. To analyze these continuously recorded, and often multidimensional, time series at scale, being able to conduct meaningful unsupervised data segmentation is an auspicious target. A common way to ac... | ['Ulysse Côté-Allard', 'Jim Tørresen', 'Knut J. Strømmen'] | 2022-07-22 | null | null | null | null | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 3.76215219e-01 -5.13926685e-01 -1.62652448e-01 -2.09785044e-01
-4.89126295e-01 -6.65380120e-01 2.84326613e-01 6.77890837e-01
-3.53967428e-01 4.30420399e-01 -2.90070832e-01 -7.12483525e-02
-2.97674835e-01 -6.52105153e-01 -2.79236853e-01 -6.63847148e-01
-1.04775995e-01 2.40646422e-01 2.58155435e-01 1.10412620... | [7.24359655380249, 3.2706849575042725] |
b1b5c56c-2e35-455a-ba6a-e784f9a878d3 | segment-anything-meets-semantic-communication | 2306.02094 | null | https://arxiv.org/abs/2306.02094v1 | https://arxiv.org/pdf/2306.02094v1.pdf | Segment Anything Meets Semantic Communication | In light of the diminishing returns of traditional methods for enhancing transmission rates, the domain of semantic communication presents promising new frontiers. Focusing on image transmission, this paper explores the application of foundation models, particularly the Segment Anything Model (SAM) developed by Meta AI... | ['Hyundong Shin', 'Chaoning Zhang', 'Brian Estadimas Arfeto', 'Shehbaz Tariq'] | 2023-06-03 | null | null | null | null | ['zero-shot-segmentation', 'image-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 9.62071896e-01 6.98789179e-01 -4.82801616e-01 -3.31304371e-01
-3.27378780e-01 -9.50172693e-02 4.96774435e-01 -5.33881225e-02
-4.50672805e-01 4.73983139e-01 2.27811322e-01 -4.35713291e-01
-1.59169137e-01 -1.02251720e+00 -4.30867136e-01 -2.31917337e-01
2.08916832e-02 3.34216088e-01 3.06340516e-01 -2.01159552... | [11.265408515930176, -1.4894294738769531] |
94d90c1d-8b91-4925-88b4-cd94bd380782 | ensemble-learning-of-myocardial-displacements | 2303.06744 | null | https://arxiv.org/abs/2303.06744v1 | https://arxiv.org/pdf/2303.06744v1.pdf | Ensemble Learning of Myocardial Displacements for Myocardial Infarction Detection in Echocardiography | Early detection and localization of myocardial infarction (MI) can reduce the severity of cardiac damage through timely treatment interventions. In recent years, deep learning techniques have shown promise for detecting MI in echocardiographic images. However, there has been no examination of how segmentation accuracy ... | ['Hieu Pham', 'Long Tran', 'Thuy Nguyen', 'Vinh Le', 'Phuong Tran', 'Bach Do', 'Hanh Van', 'Thanh Le', 'Quang Nguyen', 'Hung Pham', 'Dai Tran', 'Phi Nguyen', 'Nguyen Tuan'] | 2023-03-12 | null | null | null | null | ['myocardial-infarction-detection'] | ['medical'] | [ 1.79557726e-01 -4.29062545e-01 -1.89608693e-01 -2.74275869e-01
-1.13200057e+00 -4.81943488e-01 -1.56614661e-01 2.68752664e-01
-3.92060906e-01 4.38458651e-01 8.96042399e-03 -7.62179017e-01
-3.94235641e-01 -6.25409544e-01 -2.39850029e-01 -7.23893046e-01
-4.66563553e-01 4.06277269e-01 1.16742834e-01 4.13067907... | [14.199823379516602, -2.3406050205230713] |
4f20fd80-48ef-4b09-a1a0-4c56203834cf | data-augmentation-for-low-resource-named | 2108.11703 | null | https://arxiv.org/abs/2108.11703v1 | https://arxiv.org/pdf/2108.11703v1.pdf | Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslation | The state of art natural language processing systems relies on sizable training datasets to achieve high performance. Lack of such datasets in the specialized low resource domains lead to suboptimal performance. In this work, we adapt backtranslation to generate high quality and linguistically diverse synthetic data fo... | ['Stefan Langer', 'Usama Yaseen'] | 2021-08-26 | null | https://aclanthology.org/2021.icon-main.43 | https://aclanthology.org/2021.icon-main.43.pdf | icon-2021-12 | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [ 3.12702745e-01 -7.54690543e-02 -5.75887784e-02 -4.80514586e-01
-1.02133512e+00 -5.79059362e-01 6.33316815e-01 1.90660760e-01
-9.34791148e-01 1.43008375e+00 2.82290429e-01 -2.63116330e-01
4.13580805e-01 -6.32633269e-01 -6.31942868e-01 -2.00223982e-01
3.58520389e-01 6.56400859e-01 5.96888438e-02 -3.41040343... | [9.888971328735352, 9.64171314239502] |
889945a4-9a91-4d76-9a06-e4d3c315bb29 | word2box-capturing-set-theoretic-semantics-of | null | null | https://openreview.net/forum?id=NThz_6MqDuG | https://openreview.net/pdf?id=NThz_6MqDuG | Word2Box: Capturing Set-Theoretic Semantics of Words using BoxEmbeddings | Learning representations of words in a continuous space is perhaps the most fundamental task in NLP, a prerequisite for nearly all modern machine-learning techniques. Often the objective is to capture distributional similarity via vector dot product, however this is just one relation between word meanings we may wish t... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['word-similarity'] | ['natural-language-processing'] | [ 0.01374361 0.01143906 -0.35563344 -0.6269907 -0.36879414 -0.86890584
0.9689053 0.9416275 -0.52065617 0.21706575 0.5572718 -0.43636215
-0.3900696 -1.0151875 -0.31398875 -0.5263633 -0.28508645 0.4612023
-0.16546446 -0.72714186 0.2644587 0.67067295 -1.5689656 0.05955157
0.75490546 0.9156713 0.0... | [10.397768020629883, 8.76957893371582] |
954bb935-c091-4daf-838b-5c3cd917843b | crafting-a-multi-task-cnn-for-viewpoint | 1609.03894 | null | http://arxiv.org/abs/1609.03894v1 | http://arxiv.org/pdf/1609.03894v1.pdf | Crafting a multi-task CNN for viewpoint estimation | Convolutional Neural Networks (CNNs) were recently shown to provide
state-of-the-art results for object category viewpoint estimation. However
different ways of formulating this problem have been proposed and the competing
approaches have been explored with very different design choices. This paper
presents a compariso... | ['Renaud Marlet', 'Mathieu Aubry', 'Francisco Massa'] | 2016-09-13 | null | null | null | null | ['viewpoint-estimation'] | ['computer-vision'] | [ 1.09920837e-01 -3.48844901e-02 2.79974677e-02 -5.07318974e-01
-7.57914305e-01 -4.39337015e-01 9.14955437e-01 -2.74963737e-01
-5.66579163e-01 3.82922590e-01 -1.93332396e-02 -8.17249864e-02
5.68207689e-02 -4.41916645e-01 -8.20747554e-01 -5.65835416e-01
4.36984226e-02 3.00946236e-01 3.28494102e-01 -4.49227467... | [8.56373405456543, -0.43084219098091125] |
7318469a-e5ec-4374-88cd-b4b4e34b6ee9 | escoxlm-r-multilingual-taxonomy-driven-pre | 2305.12092 | null | https://arxiv.org/abs/2305.12092v1 | https://arxiv.org/pdf/2305.12092v1.pdf | ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain | The increasing number of benchmarks for Natural Language Processing (NLP) tasks in the computational job market domain highlights the demand for methods that can handle job-related tasks such as skill extraction, skill classification, job title classification, and de-identification. While some approaches have been deve... | ['Barbara Plank', 'Rob van der Goot', 'Mike Zhang'] | 2023-05-20 | null | null | null | null | ['de-identification', 'xlm-r'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.99996665e-01 1.94354206e-02 -7.07388878e-01 -4.06100661e-01
-8.32207263e-01 -6.61774278e-01 6.22070789e-01 3.56626242e-01
-8.98338318e-01 9.30335939e-01 4.90964472e-01 -4.79099929e-01
-5.76301396e-01 -4.19848025e-01 -4.86188710e-01 -3.62285338e-02
2.72211105e-01 1.13188672e+00 -7.17445314e-02 -5.84190190... | [10.018876075744629, 9.25311279296875] |
254bff46-539d-45a0-b08c-cd702c62f5d6 | memefier-dual-stage-modality-fusion-for-image | 2304.02906 | null | https://arxiv.org/abs/2304.02906v2 | https://arxiv.org/pdf/2304.02906v2.pdf | MemeFier: Dual-stage Modality Fusion for Image Meme Classification | Hate speech is a societal problem that has significantly grown through the Internet. New forms of digital content such as image memes have given rise to spread of hate using multimodal means, being far more difficult to analyse and detect compared to the unimodal case. Accurate automatic processing, analysis and unders... | ['Symeon Papadopoulos', 'Manos Schinas', 'Christos Koutlis'] | 2023-04-06 | null | null | null | null | ['meme-classification'] | ['natural-language-processing'] | [ 1.96595654e-01 -1.76390454e-01 -5.33503741e-02 6.43603057e-02
-5.52904010e-01 -7.16787398e-01 1.17933714e+00 1.76744759e-01
-4.26594734e-01 5.71376681e-01 5.29732227e-01 -4.95998524e-02
3.46968770e-01 -4.81214792e-01 -6.15042567e-01 -6.19515657e-01
4.12868291e-01 -2.10837345e-03 -3.51193473e-02 -4.25803453... | [8.473236083984375, 10.62991714477539] |
a10efc8f-3ce4-4b49-a546-c4dcc287ad11 | clue-a-chinese-language-understanding | 2004.05986 | null | https://arxiv.org/abs/2004.05986v3 | https://arxiv.org/pdf/2004.05986v3.pdf | CLUE: A Chinese Language Understanding Evaluation Benchmark | The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks. These comprehensive benchmarks have facilitated a broad range of research and applications in natural language processing (NLP). The problem, however... | ['Kyle Richardson', 'Zhe Zhao', 'Lu Li', 'Weijian Xie', 'Qianqian Dong', 'Yechen Xu', 'Zuoyu Tian', 'Zhenzhong Lan', 'Rongzhao Wang', 'Liang Xu', 'Junyi Li', 'He Zhou', 'Chenjie Cao', 'Zhengliang Yang', 'Yudong Li', 'Yiwen Zhang', 'Yiming Cui', 'Xuanwei Zhang', 'Weitang Liu', 'Jun Zeng', 'Cong Yue', 'Yin Tian', 'Shaowe... | 2020-04-13 | null | https://aclanthology.org/2020.coling-main.419 | https://aclanthology.org/2020.coling-main.419.pdf | coling-2020-8 | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 4.36894745e-01 -3.83823328e-02 -2.20981613e-01 -5.60155272e-01
-1.37430596e+00 -9.08298314e-01 5.21342516e-01 3.56772184e-01
-6.73303068e-01 7.78531492e-01 6.96785927e-01 -7.27406085e-01
4.92441386e-01 -4.96840596e-01 -8.15208852e-01 4.43452038e-02
1.45601839e-01 5.97763181e-01 -1.13637093e-02 -3.31506938... | [11.011070251464844, 9.38243579864502] |
75c930e9-688d-4f47-8d31-76aed7248613 | chord-recognition-music-and-audio-information | 2105.07019 | null | https://arxiv.org/abs/2105.07019v2 | https://arxiv.org/pdf/2105.07019v2.pdf | Chord Recognition- Music and Audio Information Retrieval | Music Information Retrieval (MIR) is a collaborative scientific study that help to build innovative information research themes, novel frameworks, and developing connected delivery mechanisms in addition to making the world's massive collection of music open for everyone. Modern rock music proved to be difficult to est... | ['Shah Riya Chiragkumar'] | 2021-05-14 | null | null | null | null | ['chord-recognition', 'music-information-retrieval'] | ['audio', 'music'] | [ 2.23070845e-01 -4.42206085e-01 -3.57121021e-01 9.78506058e-02
-4.10832077e-01 -8.16715777e-01 2.45298624e-01 7.35390931e-02
-4.27687705e-01 2.60447949e-01 1.86058164e-01 -2.68041760e-01
-5.91225505e-01 -8.13882649e-01 9.79439840e-02 -7.83890367e-01
7.00616241e-02 4.16909277e-01 3.16911489e-01 -4.50249970... | [15.930076599121094, 5.257870197296143] |
a1b7eff0-e992-401b-ae80-e36271034f85 | investigations-in-audio-captioning-addressing | 2211.06547 | null | https://arxiv.org/abs/2211.06547v2 | https://arxiv.org/pdf/2211.06547v2.pdf | Investigations in Audio Captioning: Addressing Vocabulary Imbalance and Evaluating Suitability of Language-Centric Performance Metrics | The analysis, processing, and extraction of meaningful information from sounds all around us is the subject of the broader area of audio analytics. Audio captioning is a recent addition to the domain of audio analytics, a cross-modal translation task that focuses on generating natural descriptions from sound events occ... | ['Dimitra Emmanouilidou', 'Sandeep Kothinti'] | 2022-11-12 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 9.02436554e-01 1.76507175e-01 2.59497792e-01 -2.43303820e-01
-1.36296785e+00 -4.69745755e-01 3.99098098e-01 3.54121923e-01
-3.09674948e-01 5.98499179e-01 6.99794948e-01 4.48307209e-02
-1.30814284e-01 -4.05112505e-01 -8.91268671e-01 -2.19178230e-01
-6.52133226e-02 3.78707826e-01 -2.98044868e-02 -1.36838257... | [15.26798152923584, 4.95896577835083] |
5b4d144d-64cb-46df-8711-90af78a63e49 | towards-mitigating-the-problem-of | 2210.11194 | null | https://arxiv.org/abs/2210.11194v1 | https://arxiv.org/pdf/2210.11194v1.pdf | Towards Mitigating the Problem of Insufficient and Ambiguous Supervision in Online Crowdsourcing Annotation | In real-world crowdsourcing annotation systems, due to differences in user knowledge and cultural backgrounds, as well as the high cost of acquiring annotation information, the supervision information we obtain might be insufficient and ambiguous. To mitigate the negative impacts, in this paper, we investigate a more g... | ['Shu-Tao Xia', 'Zimo Liu', 'Tianxiang Li', 'Mingyan Zhu', 'Bowen Zhao', 'Qian-Wei Wang'] | 2022-10-20 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 4.45177525e-01 2.29220644e-01 -4.49813664e-01 -7.35904515e-01
-1.17739046e+00 -7.39571273e-01 5.04417837e-01 1.28095835e-01
-5.59128165e-01 9.73527312e-01 -1.40305795e-02 2.08288476e-01
2.80484289e-01 -2.41524339e-01 -6.05024815e-01 -8.95980000e-01
5.87681890e-01 5.35850585e-01 3.08458030e-01 -1.28117334... | [9.50619888305664, 3.933006525039673] |
d947263f-a4d3-46c8-a993-79f7b8a639e6 | memory-guided-collaborative-attention-for | 2208.02960 | null | https://arxiv.org/abs/2208.02960v1 | https://arxiv.org/pdf/2208.02960v1.pdf | Memory-Guided Collaborative Attention for Nighttime Thermal Infrared Image Colorization | Nighttime thermal infrared (NTIR) image colorization, also known as translation of NTIR images into daytime color images (NTIR2DC), is a promising research direction to facilitate nighttime scene perception for humans and intelligent systems under unfavorable conditions (e.g., complete darkness). However, previously de... | ['Yong-Jie Li', 'Kai-Fu Yang', 'Yi-Jun Cao', 'Fu-Ya Luo'] | 2022-08-05 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 4.68418479e-01 -3.90493125e-01 9.92157310e-02 -3.52426201e-01
-4.61281925e-01 -4.39958036e-01 3.35145026e-01 -5.26909053e-01
-3.87754858e-01 6.08847857e-01 -4.71906178e-03 -2.69854873e-01
-3.51897404e-02 -6.87158823e-01 -6.79390132e-01 -1.06918430e+00
6.81657672e-01 -1.48203641e-01 -5.38401585e-03 -1.74274072... | [10.74649429321289, -2.207329511642456] |
509220a5-e973-4d30-bb08-ba4b5fab1229 | shadow-removal-by-a-lightness-guided-network | 2006.15617 | null | https://arxiv.org/abs/2006.15617v1 | https://arxiv.org/pdf/2006.15617v1.pdf | Shadow Removal by a Lightness-Guided Network with Training on Unpaired Data | Shadow removal can significantly improve the image visual quality and has many applications in computer vision. Deep learning methods based on CNNs have become the most effective approach for shadow removal by training on either paired data, where both the shadow and underlying shadow-free versions of an image are know... | ['Song Wang', 'Yang Mi', 'Mengyang Pu', 'Zhihao Liu', 'Hui Yin'] | 2020-06-28 | null | null | null | null | ['shadow-removal'] | ['computer-vision'] | [ 6.69017613e-01 3.16137671e-02 6.00044668e-01 -4.78746653e-01
-1.31044775e-01 -1.93359032e-01 3.60890001e-01 -4.11088794e-01
-3.95765334e-01 8.16482842e-01 -2.10238859e-01 -4.97066438e-01
4.94869828e-01 -5.12310803e-01 -6.33451998e-01 -1.06208837e+00
2.88000584e-01 1.28097028e-01 6.87319517e-01 -2.68948585... | [10.84246826171875, -4.104151725769043] |
1b2fec55-d7fc-4ec5-8f62-3f2fe61a1ce0 | an-efficient-point-of-gaze-estimator-for-low | 2106.05106 | null | https://arxiv.org/abs/2106.05106v1 | https://arxiv.org/pdf/2106.05106v1.pdf | An Efficient Point of Gaze Estimator for Low-Resolution Imaging Systems Using Extracted Ocular Features Based Neural Architecture | A user's eyes provide means for Human Computer Interaction (HCI) research as an important modal. The time to time scientific explorations of the eye has already seen an upsurge of the benefits in HCI applications from gaze estimation to the measure of attentiveness of a user looking at a screen for a given time period.... | ['Kavi Arya', 'Imon Mukherjee', 'Atul Sahay'] | 2021-06-09 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [ 1.60916314e-01 3.28429610e-01 1.54744938e-01 -3.23849499e-01
2.71413714e-01 -1.14040360e-01 -2.26119589e-02 -4.53921556e-01
-3.73754829e-01 6.71009004e-01 -1.33161634e-01 -2.37820566e-01
-5.61011195e-01 1.06702875e-02 -1.69953659e-01 -4.64226276e-01
5.06719761e-02 -1.50568336e-01 2.92930868e-03 -2.20600054... | [14.054485321044922, 0.17055562138557434] |
be214286-8644-445b-bf61-543fc7fa0243 | framewise-approach-in-multimodal-emotion | 1805.01369 | null | http://arxiv.org/abs/1805.01369v1 | http://arxiv.org/pdf/1805.01369v1.pdf | Framewise approach in multimodal emotion recognition in OMG challenge | In this report we described our approach achieves $53\%$ of unweighted
accuracy over $7$ emotions and $0.05$ and $0.09$ mean squared errors for
arousal and valence in OMG emotion recognition challenge. Our results were
obtained with ensemble of single modality models trained on voice and face data
from video separately... | ['Maxim Ryabov', 'Grigoriy Sterling', 'Andrey Belyaev'] | 2018-05-03 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 1.98118150e-01 4.90858465e-01 3.31668526e-01 -9.68432069e-01
-9.14170980e-01 -2.03123808e-01 3.43888015e-01 -1.28783107e-01
-5.03328800e-01 7.27673531e-01 3.14824820e-01 4.59136248e-01
3.47015083e-01 -5.58428347e-01 -7.02199519e-01 -3.89014274e-01
-4.17765945e-01 -2.98635185e-01 -6.88851297e-01 -3.13339889... | [13.354351043701172, 5.086041450500488] |
8bdb0192-7743-4924-b628-fcc7d592122f | unsupervised-features-extraction-for-binary | 1810.09683 | null | http://arxiv.org/abs/1810.09683v2 | http://arxiv.org/pdf/1810.09683v2.pdf | Unsupervised Features Extraction for Binary Similarity Using Graph Embedding Neural Networks | In this paper we consider the binary similarity problem that consists in
determining if two binary functions are similar only considering their compiled
form. This problem is know to be crucial in several application scenarios, such
as copyright disputes, malware analysis, vulnerability detection, etc. The
current stat... | ['Luca Massarelli', 'Roberto Baldoni', 'Leonardo Querzoni', 'Giuseppe Antonio Di Luna', 'Fabio Petroni'] | 2018-10-23 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [ 9.99958888e-02 -1.61516801e-01 -1.19879909e-01 -3.32136393e-01
-1.52123123e-02 -1.06971717e+00 7.95517683e-01 7.19109952e-01
-5.33108652e-01 3.60083729e-01 1.45346923e-02 -6.84436440e-01
-2.25068286e-01 -1.23512566e+00 -8.22163999e-01 -4.93268669e-01
-1.76958337e-01 3.85354698e-01 2.37373397e-01 -5.95752478... | [7.188471794128418, 7.798822402954102] |
dfaed6ca-f738-4900-a7fb-21ddd8ab6fe5 | learning-diverse-stochastic-human-action | 1912.10150 | null | https://arxiv.org/abs/1912.10150v1 | https://arxiv.org/pdf/1912.10150v1.pdf | Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions | Human-motion generation is a long-standing challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns. Most existing methods adopt sequence models such as RNN to directly model transitions in the original action space. Due to high dimensionality and potential noise, such modelin... | ['Yufan Zhou', 'Changyou Chen', 'Zhenyi Wang', 'Ping Yu', 'Yang Zhao', 'Ruiyi Zhang', 'Junsong Yuan'] | 2019-12-21 | learning-diverse-stochastic-human-action-1 | null | null | aaai-2019-12 | ['human-action-generation', 'action-generation'] | ['computer-vision', 'computer-vision'] | [ 8.41231048e-01 1.47002280e-01 -3.25266093e-01 1.21514753e-01
-9.53791440e-01 -4.24653053e-01 9.09739614e-01 -9.53896582e-01
7.22774640e-02 8.61906946e-01 6.24027073e-01 9.00149047e-02
3.40156525e-01 -8.35716307e-01 -9.70840156e-01 -9.03773427e-01
4.30186272e-01 4.83764023e-01 2.12792009e-01 -1.59815356... | [7.327515125274658, -0.12589320540428162] |
70e5e0a6-3da9-4b91-8998-ed163552e836 | specializing-multi-domain-nmt-via-penalizing | 2210.12910 | null | https://arxiv.org/abs/2210.12910v1 | https://arxiv.org/pdf/2210.12910v1.pdf | Specializing Multi-domain NMT via Penalizing Low Mutual Information | Multi-domain Neural Machine Translation (NMT) trains a single model with multiple domains. It is appealing because of its efficacy in handling multiple domains within one model. An ideal multi-domain NMT should learn distinctive domain characteristics simultaneously, however, grasping the domain peculiarity is a non-tr... | ['Cheonbok Park', 'Edward Choi', 'Hyunchang Cho', 'Hantae Kim', 'Jiyoung Lee'] | 2022-10-24 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 4.86974806e-01 -8.89287665e-02 -5.24654269e-01 -4.06810284e-01
-1.08016860e+00 -6.92776263e-01 9.09892499e-01 -2.68391490e-01
-4.64907765e-01 9.60349798e-01 2.64378358e-02 -3.28923941e-01
-1.15492091e-01 -4.15638119e-01 -7.19926178e-01 -5.78354120e-01
3.23001951e-01 1.00721300e+00 1.54048413e-01 -3.55365247... | [11.6347017288208, 10.274798393249512] |
a0c207ee-adce-4896-a9d0-a04bb038a37b | chinese-named-entity-recognition-augmented | 1912.08282 | null | https://arxiv.org/abs/1912.08282v2 | https://arxiv.org/pdf/1912.08282v2.pdf | Chinese Named Entity Recognition Augmented with Lexicon Memory | Inspired by a concept of content-addressable retrieval from cognitive science, we propose a novel fragment-based model augmented with a lexicon-based memory for Chinese NER, in which both the character-level and word-level features are combined to generate better feature representations for possible name candidates. It... | ['Xuanjing Huang', 'Yi Zhou', 'Xiaoqing Zheng'] | 2019-12-17 | null | null | null | null | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-2.80039221e-01 -4.00450647e-01 -1.19966544e-01 -4.01694365e-02
-8.31836939e-01 -6.78455055e-01 6.35957181e-01 4.90175247e-01
-8.64218831e-01 7.08680212e-01 5.42480409e-01 -1.26071423e-02
-5.14294624e-01 -1.16383410e+00 -1.32735536e-01 -3.19440424e-01
-2.33101360e-02 4.80109930e-01 4.33642983e-01 -4.79567647... | [9.739495277404785, 9.585725784301758] |
0b823695-94b9-4741-8e2e-ed6eb802e339 | occluded-human-mesh-recovery | 2203.13349 | null | https://arxiv.org/abs/2203.13349v1 | https://arxiv.org/pdf/2203.13349v1.pdf | Occluded Human Mesh Recovery | Top-down methods for monocular human mesh recovery have two stages: (1) detect human bounding boxes; (2) treat each bounding box as an independent single-human mesh recovery task. Unfortunately, the single-human assumption does not hold in images with multi-human occlusion and crowding. Consequently, top-down methods h... | ['Kris Kitani', 'Shashank Tripathi', 'Rawal Khirodkar'] | 2022-03-24 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Khirodkar_Occluded_Human_Mesh_Recovery_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Khirodkar_Occluded_Human_Mesh_Recovery_CVPR_2022_paper.pdf | cvpr-2022-1 | ['human-mesh-recovery'] | ['computer-vision'] | [ 2.19121054e-01 2.47214511e-01 6.74964488e-02 -1.95122391e-01
-7.19594777e-01 -2.72331890e-02 3.26585680e-01 -1.90655112e-01
-2.80310273e-01 6.43883526e-01 3.93694431e-01 2.54182398e-01
1.99616596e-01 -7.81138897e-01 -8.85414958e-01 -4.55765039e-01
3.20962310e-01 8.59072864e-01 6.83013260e-01 -4.27274138... | [7.11115837097168, -0.9794793128967285] |
973274a2-4bdc-4cf1-9185-76c03c550d1f | multimodal-semi-supervised-learning-for-text | 2205.03873 | null | https://arxiv.org/abs/2205.03873v1 | https://arxiv.org/pdf/2205.03873v1.pdf | Multimodal Semi-Supervised Learning for Text Recognition | Until recently, the number of public real-world text images was insufficient for training scene text recognizers. Therefore, most modern training methods rely on synthetic data and operate in a fully supervised manner. Nevertheless, the amount of public real-world text images has increased significantly lately, includi... | ['Ron Litman', 'Shai Mazor', 'Roy Ganz', 'Aviad Aberdam'] | 2022-05-08 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 6.51075780e-01 -2.06482723e-01 -3.86854351e-01 -4.46319699e-01
-9.09380257e-01 -5.56088448e-01 1.09699023e+00 -1.60455346e-01
-5.84928572e-01 3.44816476e-01 9.11067650e-02 -4.65908617e-01
4.86916125e-01 -4.36612308e-01 -8.26812029e-01 -5.74577451e-01
7.59351671e-01 7.14760125e-01 1.18841365e-01 -1.65324688... | [11.012587547302246, 1.6184743642807007] |
22c013d8-f01a-436b-8fd1-8488dcada0a4 | union-visual-translation-embedding-for-visual | 1905.11624 | null | https://arxiv.org/abs/1905.11624v3 | https://arxiv.org/pdf/1905.11624v3.pdf | Contextual Translation Embedding for Visual Relationship Detection and Scene Graph Generation | Relations amongst entities play a central role in image understanding. Due to the complexity of modeling (subject, predicate, object) relation triplets, it is crucial to develop a method that can not only recognize seen relations, but also generalize to unseen cases. Inspired by a previously proposed visual translation... | ['Zih-Siou Hung', 'Svetlana Lazebnik', 'Arun Mallya'] | 2019-05-28 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 3.55916172e-01 2.30404675e-01 -1.96854606e-01 -3.52062076e-01
-3.56063277e-01 -4.33554530e-01 8.60914350e-01 4.18239266e-01
-2.47900158e-01 3.54983747e-01 4.19536263e-01 -3.44261676e-01
-3.78151461e-02 -1.00016499e+00 -9.54776525e-01 -5.12967885e-01
-3.98561172e-02 6.37869656e-01 3.38397712e-01 -1.72165066... | [10.41025161743164, 1.6513782739639282] |
39d9eed7-6bb5-45e2-80f7-4f6e066b824c | learning-to-transfer-examples-for-partial | 1903.12230 | null | http://arxiv.org/abs/1903.12230v2 | http://arxiv.org/pdf/1903.12230v2.pdf | Learning to Transfer Examples for Partial Domain Adaptation | Domain adaptation is critical for learning in new and unseen environments.
With domain adversarial training, deep networks can learn disentangled and
transferable features that effectively diminish the dataset shift between the
source and target domains for knowledge transfer. In the era of Big Data, the
ready availabi... | ['Jian-Min Wang', 'Zhangjie Cao', 'Mingsheng Long', 'Qiang Yang', 'Kaichao You'] | 2019-03-28 | learning-to-transfer-examples-for-partial-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Cao_Learning_to_Transfer_Examples_for_Partial_Domain_Adaptation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Cao_Learning_to_Transfer_Examples_for_Partial_Domain_Adaptation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['partial-domain-adaptation'] | ['methodology'] | [ 5.85089147e-01 2.12062418e-01 -6.21455491e-01 -4.52203125e-01
-7.60389507e-01 -9.44251359e-01 5.19337833e-01 -1.03628121e-01
-4.16769087e-01 1.20018828e+00 5.46665564e-02 3.87387276e-02
-8.92970264e-02 -9.52843845e-01 -7.69657969e-01 -8.52386773e-01
2.02409998e-01 7.70203650e-01 2.48204753e-01 -4.23598230... | [10.345235824584961, 3.1135683059692383] |
a71d3389-8867-464a-903f-4d5ee910397a | self-supervised-interest-point-detection-and | 2306.01938 | null | https://arxiv.org/abs/2306.01938v1 | https://arxiv.org/pdf/2306.01938v1.pdf | Self-supervised Interest Point Detection and Description for Fisheye and Perspective Images | Keypoint detection and matching is a fundamental task in many computer vision problems, from shape reconstruction, to structure from motion, to AR/VR applications and robotics. It is a well-studied problem with remarkable successes such as SIFT, and more recent deep learning approaches. While great robustness is exhibi... | ['Gianfranco Doretto', 'Yu Gu', 'Shivang Patel', 'Marcela Mera-Trujillo'] | 2023-06-02 | null | null | null | null | ['interest-point-detection', 'keypoint-detection'] | ['computer-vision', 'computer-vision'] | [ 2.46140063e-01 -2.30261818e-01 -4.47780564e-02 -1.38537586e-01
-4.98872072e-01 -5.76389790e-01 9.42532480e-01 5.33297285e-02
-5.91952443e-01 2.15127960e-01 -2.78666884e-01 -1.91670768e-02
-2.37800121e-01 -6.12935781e-01 -8.11153054e-01 -6.00180984e-01
-7.56367743e-02 4.68389273e-01 5.43297946e-01 -2.76139528... | [7.837305068969727, -2.195344924926758] |
d753a1ba-5b1e-4d62-849e-7622700956a1 | dpdnet-a-robust-people-detector-using-deep | 2006.01053 | null | https://arxiv.org/abs/2006.01053v1 | https://arxiv.org/pdf/2006.01053v1.pdf | DPDnet: A Robust People Detector using Deep Learning with an Overhead Depth Camera | In this paper we propose a method based on deep learning that detects multiple people from a single overhead depth image with high reliability. Our neural network, called DPDnet, is based on two fully-convolutional encoder-decoder neural blocks based on residual layers. The Main Block takes a depth image as input and g... | ['Javier Macias-Guarasa', 'David Casillas-Perez', 'Cristina Losada-Gutierrez', 'Roberto Martin-Lopez', 'David Fuentes-Jimenez', 'Daniel Pizarro', 'Carlos A. Luna'] | 2020-06-01 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [-5.54712377e-02 3.07792783e-01 4.24472839e-01 -5.29257298e-01
-5.10249317e-01 9.37908143e-02 3.35755110e-01 2.61449605e-01
-9.08475518e-01 6.41636968e-01 -8.12922493e-02 2.84665853e-01
4.37060237e-01 -8.57987881e-01 -7.38381684e-01 -5.60335636e-01
-8.10671672e-02 8.11391354e-01 6.96998179e-01 1.96372971... | [7.986742973327637, -0.5949397087097168] |
52f10ba5-40bd-46c2-b156-969907d613d2 | adaptkeybert-an-attention-based-approach | 2211.07499 | null | https://arxiv.org/abs/2211.07499v2 | https://arxiv.org/pdf/2211.07499v2.pdf | AdaptKeyBERT: An Attention-Based approach towards Few-Shot & Zero-Shot Domain Adaptation of KeyBERT | Keyword extraction has been an important topic for modern natural language processing. With its applications ranging from ontology generation, fact verification in summarized text, and recommendation systems. While it has had significant data-intensive applications, it is often hampered when the data set is small. Down... | ['Supriti Vijay', 'Aman Priyanshu'] | 2022-11-14 | null | null | null | null | ['fact-verification', 'keyword-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.42302436e-02 9.01088677e-03 -4.77450222e-01 -3.61654222e-01
-1.10854650e+00 -6.31813705e-01 6.60932660e-01 4.44281757e-01
-5.63949883e-01 6.55776620e-01 4.08798277e-01 -4.43021089e-01
-1.04528949e-01 -8.12954485e-01 -5.92570961e-01 -2.41275787e-01
1.02329269e-01 3.98483634e-01 4.98688489e-01 -4.05692905... | [10.523185729980469, 8.009100914001465] |
14af5b47-8075-451d-b28a-0181d94755d6 | fusion-towards-automated-icd-coding-via | null | null | https://aclanthology.org/2021.findings-acl.184 | https://aclanthology.org/2021.findings-acl.184.pdf | Fusion: Towards Automated ICD Coding via Feature Compression | null | ['Fenglong Ma', 'Jimeng Sun', 'Lucas Glass', 'Cao Xiao', 'Junyu Luo'] | null | null | null | null | findings-acl-2021-8 | ['feature-compression'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.437716007232666, 3.7819583415985107] |
8720e5b7-4bb5-4f77-b1c1-6e24d04b8a6b | spatial-content-alignment-for-pose-transfer | 2103.16828 | null | https://arxiv.org/abs/2103.16828v1 | https://arxiv.org/pdf/2103.16828v1.pdf | Spatial Content Alignment For Pose Transfer | Due to unreliable geometric matching and content misalignment, most conventional pose transfer algorithms fail to generate fine-trained person images. In this paper, we propose a novel framework Spatial Content Alignment GAN (SCAGAN) which aims to enhance the content consistency of garment textures and the details of h... | ['Kin-Wai Lau', 'Jingjing Xiong', 'Yuzhi Zhao', 'Lai-Man Po', 'Wing-Yin Yu'] | 2021-03-31 | null | null | null | null | ['geometric-matching', 'pose-transfer'] | ['computer-vision', 'computer-vision'] | [ 3.56615543e-01 -6.73270896e-02 2.10479721e-01 -1.37974083e-01
-7.03024805e-01 -5.80462933e-01 6.01543427e-01 -6.56954169e-01
-7.28139803e-02 9.51355338e-01 4.55732197e-01 4.21644121e-01
2.67543346e-01 -7.12611318e-01 -9.10610080e-01 -5.43430924e-01
4.94709373e-01 2.74154425e-01 -1.79713771e-01 -1.62759528... | [11.966782569885254, -0.8384580612182617] |
24d47b0f-97c3-4ad9-929e-5e842d3ad6f3 | detclip-dictionary-enriched-visual-concept | 2209.09407 | null | https://arxiv.org/abs/2209.09407v2 | https://arxiv.org/pdf/2209.09407v2.pdf | DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection | Open-world object detection, as a more general and challenging goal, aims to recognize and localize objects described by arbitrary category names. The recent work GLIP formulates this problem as a grounding problem by concatenating all category names of detection datasets into sentences, which leads to inefficient inte... | ['Hang Xu', 'Chunjing Xu', 'Zhenguo Li', 'Wei zhang', 'Dan Xu', 'Xiaodan Liang', 'Youpeng Wen', 'Jianhua Han', 'Lewei Yao'] | 2022-09-20 | null | null | null | null | ['open-world-object-detection'] | ['computer-vision'] | [ 5.52486815e-02 -8.66012648e-02 -2.79588193e-01 -2.80016512e-01
-9.20003593e-01 -7.53619075e-01 4.56493616e-01 3.43606085e-01
-5.72668910e-01 3.90498489e-01 1.67077016e-02 -7.74828047e-02
3.58314425e-01 -6.32959247e-01 -6.32546127e-01 -6.01058066e-01
3.65016133e-01 5.33607960e-01 4.62864250e-01 -4.70637828... | [9.765652656555176, 1.578628420829773] |
26f40678-542c-422b-956b-f15d85fd30eb | learning-emotion-aware-contextual | null | null | https://openreview.net/forum?id=msFpRstzoXt | https://openreview.net/pdf?id=msFpRstzoXt | Learning Emotion-Aware Contextual Representations for Emotion-Cause Pair Extraction | Emotion Cause Pair Extraction (ECPE), the task expanded from the previous emotion cause extraction (ECE) task, focuses on extracting emotion-cause pairs in text. Two reasons have made ECPE a more challenging, but more applicable task in real world scenarios: 1) an ECPE model needs to identify both emotions and their co... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['emotion-cause-pair-extraction', 'emotion-cause-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.97832787e-01 -7.85198063e-02 -1.31178364e-01 -3.89259785e-01
-8.95340562e-01 -7.31718600e-01 5.71739018e-01 2.28066832e-01
-2.02466235e-01 6.61080360e-01 4.77211386e-01 5.26364483e-02
-1.00453518e-01 -5.22547781e-01 -5.68052471e-01 -3.64467651e-01
4.12638262e-02 -5.74720688e-02 -3.53102714e-01 -2.19906822... | [12.663707733154297, 6.22737979888916] |
3b21860f-82bd-43f1-8db4-9bb1da22a48a | hierarchical-kinematic-human-mesh-recovery | 2003.04232 | null | https://arxiv.org/abs/2003.04232v2 | https://arxiv.org/pdf/2003.04232v2.pdf | Hierarchical Kinematic Human Mesh Recovery | We consider the problem of estimating a parametric model of 3D human mesh from a single image. While there has been substantial recent progress in this area with direct regression of model parameters, these methods only implicitly exploit the human body kinematic structure, leading to sub-optimal use of the model prior... | ['Terrence Chen', 'Ren Li', 'Ziyan Wu', 'Jana Kosecka', 'Georgios Georgakis', 'Srikrishna Karanam'] | 2020-03-09 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2889_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123620749.pdf | eccv-2020-8 | ['human-mesh-recovery'] | ['computer-vision'] | [ 2.20057175e-01 3.62828732e-01 -1.90725163e-01 -1.16549596e-01
-5.00428915e-01 -1.76317871e-01 5.17133772e-01 -9.52636525e-02
-2.92489022e-01 5.81914127e-01 2.62561262e-01 2.49388710e-01
-2.06702486e-01 -4.79593396e-01 -8.89093935e-01 -5.19630373e-01
-2.23432794e-01 9.49336231e-01 3.33229870e-01 -3.99165392... | [7.061221122741699, -1.1859419345855713] |
4a4bf6ec-defa-47bb-aae9-023a91de69f6 | neural-oscillators-are-universal | 2305.08753 | null | https://arxiv.org/abs/2305.08753v1 | https://arxiv.org/pdf/2305.08753v1.pdf | Neural Oscillators are Universal | Coupled oscillators are being increasingly used as the basis of machine learning (ML) architectures, for instance in sequence modeling, graph representation learning and in physical neural networks that are used in analog ML devices. We introduce an abstract class of neural oscillators that encompasses these architectu... | ['Siddhartha Mishra', 'T. Konstantin Rusch', 'Samuel Lanthaler'] | 2023-05-15 | null | null | null | null | ['graph-representation-learning'] | ['methodology'] | [ 4.08188999e-01 1.82945549e-01 -2.04322502e-01 1.33222863e-01
2.27987200e-01 -7.12666094e-01 3.93456638e-01 9.22092795e-02
-2.01025277e-01 6.56061053e-01 -3.23104978e-01 -3.02899361e-01
-2.98587292e-01 -7.19585240e-01 -8.56124938e-01 -6.99307919e-01
-2.94110537e-01 1.30413160e-01 1.59475893e-01 -5.20509779... | [7.80021858215332, 3.2331788539886475] |
09172cab-4eb2-48c9-ba81-56c6437f061d | lossy-compression-of-multidimensional-medical | 2208.01602 | null | https://arxiv.org/abs/2208.01602v2 | https://arxiv.org/pdf/2208.01602v2.pdf | Lossy compression of multidimensional medical images using sinusoidal activation networks: an evaluation study | In this work, we evaluate how neural networks with periodic activation functions can be leveraged to reliably compress large multidimensional medical image datasets, with proof-of-concept application to 4D diffusion-weighted MRI (dMRI). In the medical imaging landscape, multidimensional MRI is a key area of research fo... | ['Marco Palombo', 'Derek K. Jones', 'Matteo Mancini'] | 2022-08-02 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 6.62528992e-01 -6.83815032e-03 -4.02654745e-02 -1.71403855e-01
-7.35681713e-01 -1.31639391e-01 5.15104830e-01 4.96046305e-01
-7.69744694e-01 6.28028512e-01 3.70499641e-01 -7.51272142e-02
-5.70845783e-01 -5.69975555e-01 -5.99277437e-01 -1.07487619e+00
-6.97055519e-01 5.70299745e-01 -4.06238139e-02 -3.33462581... | [13.531752586364746, -2.3992550373077393] |
4d9b03dd-cc7f-4585-ad16-0199dae15b05 | hierarchical-transformer-for-survival | 2211.16632 | null | https://arxiv.org/abs/2211.16632v1 | https://arxiv.org/pdf/2211.16632v1.pdf | Hierarchical Transformer for Survival Prediction Using Multimodality Whole Slide Images and Genomics | Learning good representation of giga-pixel level whole slide pathology images (WSI) for downstream tasks is critical. Previous studies employ multiple instance learning (MIL) to represent WSIs as bags of sampled patches because, for most occasions, only slide-level labels are available, and only a tiny region of the WS... | ['Junzhou Huang', 'Jiawen Yao', 'Xinliang Zhu', 'Chunyuan Li'] | 2022-11-29 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 3.72949511e-01 3.22911404e-02 -3.48585635e-01 -1.88152432e-01
-1.54713929e+00 -2.84916788e-01 1.13869786e-01 5.89174330e-01
-2.03032140e-02 7.33705819e-01 1.28712431e-01 -4.77821603e-02
-2.10410461e-01 -8.42213273e-01 -8.83105159e-01 -1.44015348e+00
-2.11925954e-01 4.74706560e-01 1.74671367e-01 -2.60709245... | [15.163360595703125, -2.877103090286255] |
44f4a2af-e10f-4329-b8ca-5f1f4c77dff1 | squeezing-large-scale-diffusion-models-for | 2307.01193 | null | https://arxiv.org/abs/2307.01193v1 | https://arxiv.org/pdf/2307.01193v1.pdf | Squeezing Large-Scale Diffusion Models for Mobile | The emergence of diffusion models has greatly broadened the scope of high-fidelity image synthesis, resulting in notable advancements in both practical implementation and academic research. With the active adoption of the model in various real-world applications, the need for on-device deployment has grown considerably... | ['HyungJun Kim', 'Jae-Joon Kim', 'Hyesung Jeon', 'Dongwon Jo', 'Yulhwa Kim', 'Taesu Kim', 'Daehyun Ahn', 'Minkyu Kim', 'Jiwoong Choi'] | 2023-07-03 | null | null | null | null | ['image-generation'] | ['computer-vision'] | [ 5.96015491e-02 -5.63152850e-01 -1.53091222e-01 8.54056329e-02
-2.65957445e-01 -5.45597434e-01 5.65737128e-01 -4.47890997e-01
-3.53765875e-01 5.09013116e-01 9.20272395e-02 -7.94969559e-01
-1.98508427e-02 -7.66971171e-01 -2.83200979e-01 -5.82114875e-01
-1.91120028e-01 9.08641890e-03 2.98576772e-01 2.36048903... | [11.077330589294434, -0.4837866425514221] |
a1b5b3e9-4fe5-4f81-8e4f-2524be25dd00 | knowledge-graph-embeddings-in-the-biomedical | 2305.19979 | null | https://arxiv.org/abs/2305.19979v1 | https://arxiv.org/pdf/2305.19979v1.pdf | Knowledge Graph Embeddings in the Biomedical Domain: Are They Useful? A Look at Link Prediction, Rule Learning, and Downstream Polypharmacy Tasks | Knowledge graphs are powerful tools for representing and organising complex biomedical data. Several knowledge graph embedding algorithms have been proposed to learn from and complete knowledge graphs. However, a recent study demonstrates the limited efficacy of these embedding algorithms when applied to biomedical kno... | ['Ajitha Rajan', 'Antonio Vergari', 'Pasquale Minervini', 'Javier Antonio Alfaro', 'Piyush Borole', 'Wolf De Wulf', 'Dominik Grabarczyk', 'Aryo Pradipta Gema'] | 2023-05-31 | null | null | null | null | ['graph-embedding', 'link-prediction', 'knowledge-graph-embedding', 'knowledge-graph-embeddings', 'knowledge-graphs', 'knowledge-graph-embeddings'] | ['graphs', 'graphs', 'graphs', 'graphs', 'knowledge-base', 'methodology'] | [ 1.74658984e-01 7.82787204e-01 -5.10459960e-01 -1.19319849e-01
-2.24635318e-01 -3.65740597e-01 3.16539139e-01 9.33018327e-01
-3.65729898e-01 7.57910311e-01 5.32333493e-01 -5.09166241e-01
-6.14503622e-01 -8.54473293e-01 -5.24936616e-01 -4.20122713e-01
-4.87238586e-01 6.32514775e-01 5.90527151e-03 -1.38337836... | [8.031500816345215, 7.392233848571777] |
fa51bba6-8aee-457d-9de2-1f1640ce6555 | corl-research-oriented-deep-offline | 2210.07105 | null | https://arxiv.org/abs/2210.07105v3 | https://arxiv.org/pdf/2210.07105v3.pdf | CORL: Research-oriented Deep Offline Reinforcement Learning Library | CORL is an open-source library that provides thoroughly benchmarked single-file implementations of both deep offline and offline-to-online reinforcement learning algorithms. It emphasizes a simple developing experience with a straightforward codebase and a modern analysis tracking tool. In CORL, we isolate methods impl... | ['Sergey Kolesnikov', 'Vladislav Kurenkov', 'Dmitry Akimov', 'Alexander Nikulin', 'Denis Tarasov'] | 2022-10-13 | null | null | null | null | ['d4rl'] | ['robots'] | [-6.27624989e-01 -2.55358011e-01 -2.77817667e-01 -5.48543215e-01
-9.91872072e-01 -7.38269925e-01 5.22362471e-01 5.07960737e-01
-4.80020970e-01 8.01659107e-01 -7.82843605e-02 -5.32270133e-01
-4.34951812e-01 -3.05350333e-01 -6.60666823e-01 -5.74847162e-01
-5.74663818e-01 4.10923392e-01 2.92800784e-01 -2.70300899... | [4.111735820770264, 1.623763918876648] |
c5ea6dd3-93ab-4e9d-94e8-50b7fe54b649 | vizinspect-pro-automated-optical-inspection | 2205.13095 | null | https://arxiv.org/abs/2205.13095v1 | https://arxiv.org/pdf/2205.13095v1.pdf | VizInspect Pro -- Automated Optical Inspection (AOI) solution | Traditional vision based Automated Optical Inspection (referred to as AOI in paper) systems present multiple challenges in factory settings including inability to scale across multiple product lines, requirement of vendor programming expertise, little tolerance to variations and lack of cloud connectivity for aggregate... | ['Debashis Mondal', 'Haotian Xu', 'Sanjit Menon', 'Faraz Waseem'] | 2022-05-26 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [-4.58545417e-01 -3.76768969e-02 9.95281279e-01 -2.35575318e-01
1.10908985e-01 -7.42500961e-01 -3.02641630e-01 2.00286344e-01
5.19108176e-01 -7.01499805e-02 -6.02784514e-01 -4.03521746e-01
-1.02762365e+00 -4.95863765e-01 -2.82499939e-01 -4.03211832e-01
-1.02999806e-02 6.82508409e-01 7.14156553e-02 -5.19806445... | [7.261680603027344, 2.031715154647827] |
12afad55-e9de-4ae6-9445-ba0a80555dfa | the-limitations-of-cross-language-word | 1806.02253 | null | http://arxiv.org/abs/1806.02253v1 | http://arxiv.org/pdf/1806.02253v1.pdf | The Limitations of Cross-language Word Embeddings Evaluation | The aim of this work is to explore the possible limitations of existing
methods of cross-language word embeddings evaluation, addressing the lack of
correlation between intrinsic and extrinsic cross-language evaluation methods.
To prove this hypothesis, we construct English-Russian datasets for extrinsic
and intrinsic ... | ['Roman Suvorov', 'Ilya Sochenkov', 'Amir Bakarov'] | 2018-06-06 | the-limitations-of-cross-language-word-1 | https://aclanthology.org/S18-2010 | https://aclanthology.org/S18-2010.pdf | semeval-2018-6 | ['embeddings-evaluation'] | ['natural-language-processing'] | [-3.75980943e-01 6.45571873e-02 -2.73206264e-01 -5.17087042e-01
-3.50176364e-01 -6.52949810e-01 1.00206792e+00 6.47592783e-01
-1.25429654e+00 6.69110537e-01 5.52168310e-01 -3.35220397e-01
-1.59324229e-01 -7.07689822e-01 -2.68494338e-01 -6.39880672e-02
5.69127262e-01 5.49139023e-01 2.62286067e-01 -5.58111608... | [10.752846717834473, 9.596521377563477] |
9fef98d8-76c2-468e-b002-5355cab3a649 | heart-rate-variability-as-a-predictive | 2005.08031 | null | https://arxiv.org/abs/2005.08031v3 | https://arxiv.org/pdf/2005.08031v3.pdf | Heart Rate Variability as a Predictive Biomarker of Thermal Comfort | Thermal comfort is an assessment of one's satisfaction with the surroundings; yet, most mechanisms that are used to provide thermal comfort are based on approaches that preclude physiological, psychological, and personal psychophysics that are precursors to thermal comfort. This leads to many people feeling either cold... | ['Guillaume Lopez', 'Yuta Suzuki', 'Kizito Nkurikiyeyezu'] | 2020-05-16 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [-7.76761919e-02 -1.14899419e-01 1.78644862e-02 -4.88873184e-01
2.70451158e-01 -3.91604900e-01 1.10449456e-01 2.21142352e-01
-3.13561141e-01 7.80629516e-01 8.22202116e-02 -1.91555038e-01
2.17228860e-01 -6.58347905e-01 2.79640734e-01 -6.67735457e-01
1.05443045e-01 -1.65207714e-01 -4.72738594e-01 -3.14741611... | [13.74846076965332, 3.021015167236328] |
8f434662-0b67-4dbd-9b4a-baf4dde9b68e | learning-state-aware-visual-representations | 2209.13583 | null | https://arxiv.org/abs/2209.13583v1 | https://arxiv.org/pdf/2209.13583v1.pdf | Learning State-Aware Visual Representations from Audible Interactions | We propose a self-supervised algorithm to learn representations from egocentric video data. Recently, significant efforts have been made to capture humans interacting with their own environments as they go about their daily activities. In result, several large egocentric datasets of interaction-rich multi-modal data ha... | ['Abhinav Gupta', 'Unnat Jain', 'Pedro Morgado', 'Himangi Mittal'] | 2022-09-27 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 3.15584809e-01 -2.42161781e-01 -2.49966979e-01 -5.58184266e-01
-3.84566635e-01 -3.60811949e-01 6.57827437e-01 4.86936197e-02
-1.51495457e-01 4.78336722e-01 1.08215487e+00 7.41318524e-01
-1.54010952e-01 -5.50187290e-01 -9.26910639e-01 -2.97185570e-01
-4.94349182e-01 2.14158073e-01 3.12951319e-02 -2.74915516... | [8.316567420959473, 0.5888233780860901] |
7a794f08-bd3c-49cb-b437-56bcb232dbd3 | deep-learning-inversion-a-next-generation | 1902.06267 | null | http://arxiv.org/abs/1902.06267v1 | http://arxiv.org/pdf/1902.06267v1.pdf | Deep-learning inversion: a next generation seismic velocity-model building method | Seismic velocity is one of the most important parameters used in seismic
exploration. Accurate velocity models are key prerequisites for reverse-time
migration and other high-resolution seismic imaging techniques. Such velocity
information has traditionally been derived by tomography or full-waveform
inversion (FWI), w... | ['Jianwei Ma', 'Fangshu Yang'] | 2019-02-17 | null | null | null | null | ['seismic-imaging'] | ['miscellaneous'] | [ 3.96238975e-02 -1.31524652e-01 2.55132586e-01 -1.66288212e-01
-8.57908607e-01 -3.39798741e-02 4.35833216e-01 -3.02818239e-01
-6.24745846e-01 7.02290177e-01 -1.39459401e-01 -4.49851036e-01
-4.26672429e-01 -1.10678458e+00 -7.40276158e-01 -1.13228011e+00
-2.74619460e-01 6.01092398e-01 3.87264341e-01 -1.73229620... | [6.853077411651611, 2.547147750854492] |
92c2b9cc-579d-4370-8a5e-7968e3ea2358 | improving-universal-sound-separation-using | 1911.07951 | null | https://arxiv.org/abs/1911.07951v1 | https://arxiv.org/pdf/1911.07951v1.pdf | Improving Universal Sound Separation Using Sound Classification | Deep learning approaches have recently achieved impressive performance on both audio source separation and sound classification. Most audio source separation approaches focus only on separating sources belonging to a restricted domain of source classes, such as speech and music. However, recent work has demonstrated th... | ['Scott Wisdom', 'Efthymios Tzinis', 'John R. Hershey', 'Daniel P. W. Ellis', 'Aren Jansen'] | 2019-11-18 | null | null | null | null | ['audio-source-separation', 'sound-classification'] | ['audio', 'audio'] | [ 3.36756855e-01 -2.85502285e-01 1.12158405e-02 -5.95128462e-02
-1.46614897e+00 -1.00024641e+00 2.25408003e-01 1.58082813e-01
-6.84238225e-02 4.66098368e-01 4.23260421e-01 -1.24921165e-01
-2.63681114e-01 -2.66452044e-01 -4.28608388e-01 -8.99314761e-01
-1.32284328e-01 2.16254115e-01 1.21766806e-01 5.15321568... | [15.357918739318848, 5.47518253326416] |
8dfcb46e-e2ed-4192-bc7d-66d3a2d1c73c | a-novel-dataset-and-a-two-stage-mitosis | 2301.07627 | null | https://arxiv.org/abs/2301.07627v1 | https://arxiv.org/pdf/2301.07627v1.pdf | A novel dataset and a two-stage mitosis nuclei detection method based on hybrid anchor branch | Mitosis detection is one of the challenging problems in computational pathology, and mitotic count is an important index of cancer grading for pathologists. However, current counts of mitotic nuclei rely on pathologists looking microscopically at the number of mitotic nuclei in hot spots, which is subjective and time-c... | ['Xiaonan Luo', 'Rushi Lan', 'Lingqi Zeng', 'Xipeng Pan', 'Bingbing Li', 'Hao Xu', 'Huadeng Wang'] | 2023-01-18 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [-6.78843586e-03 1.15038320e-01 -4.33506489e-01 7.02124387e-02
-8.74984264e-01 -4.53192174e-01 2.80066550e-01 5.43028593e-01
-6.47567809e-01 7.54405081e-01 -6.72949702e-02 -3.79362911e-01
1.67398855e-01 -9.06793177e-01 -1.75448254e-01 -1.17473221e+00
3.31137300e-01 3.23879391e-01 5.33924162e-01 3.61007266... | [15.053014755249023, -3.0807580947875977] |
a696bae4-1871-4ae6-9b08-c9b50fdf1888 | action-conditioned-3d-human-motion-synthesis | 2104.05670 | null | https://arxiv.org/abs/2104.05670v2 | https://arxiv.org/pdf/2104.05670v2.pdf | Action-Conditioned 3D Human Motion Synthesis with Transformer VAE | We tackle the problem of action-conditioned generation of realistic and diverse human motion sequences. In contrast to methods that complete, or extend, motion sequences, this task does not require an initial pose or sequence. Here we learn an action-aware latent representation for human motions by training a generativ... | ['Gül Varol', 'Michael J. Black', 'Mathis Petrovich'] | 2021-04-12 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Petrovich_Action-Conditioned_3D_Human_Motion_Synthesis_With_Transformer_VAE_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Petrovich_Action-Conditioned_3D_Human_Motion_Synthesis_With_Transformer_VAE_ICCV_2021_paper.pdf | iccv-2021-1 | ['human-action-generation'] | ['computer-vision'] | [ 5.32690227e-01 1.46825835e-01 -1.21182665e-01 -2.98816651e-01
-8.81673276e-01 -4.98427093e-01 8.11160207e-01 -1.09715235e+00
-2.62936860e-01 6.81456447e-01 8.04214716e-01 1.98855445e-01
3.36081386e-01 -5.46551943e-01 -1.00981987e+00 -8.41663301e-01
1.80578470e-01 7.83545792e-01 1.49799973e-01 -4.30493057... | [7.304480075836182, -0.1261584311723709] |
d81af21e-531b-40a2-b814-d5787c2488b2 | schnet-a-continuous-filter-convolutional | 1706.08566 | null | http://arxiv.org/abs/1706.08566v5 | http://arxiv.org/pdf/1706.08566v5.pdf | SchNet: A continuous-filter convolutional neural network for modeling quantum interactions | Deep learning has the potential to revolutionize quantum chemistry as it is
ideally suited to learn representations for structured data and speed up the
exploration of chemical space. While convolutional neural networks have proven
to be the first choice for images, audio and video data, the atoms in molecules
are not ... | ['Klaus-Robert Müller', 'Pieter-Jan Kindermans', 'Kristof T. Schütt', 'Huziel E. Sauceda', 'Alexandre Tkatchenko', 'Stefan Chmiela'] | 2017-06-26 | schnet-a-continuous-filter-convolutional-1 | http://papers.nips.cc/paper/6700-schnet-a-continuous-filter-convolutional-neural-network-for-modeling-quantum-interactions | http://papers.nips.cc/paper/6700-schnet-a-continuous-filter-convolutional-neural-network-for-modeling-quantum-interactions.pdf | neurips-2017-12 | ['formation-energy'] | ['miscellaneous'] | [ 3.16858031e-02 -2.77545750e-01 -1.72889456e-01 -4.80373114e-01
-5.85193217e-01 -6.31392241e-01 7.91435897e-01 5.23912072e-01
-5.63914239e-01 9.75855529e-01 -5.63106872e-02 -5.81967771e-01
-2.61387751e-02 -1.02368426e+00 -1.10629463e+00 -1.00100315e+00
-5.99675894e-01 4.87935632e-01 -1.17906690e-01 -2.82762170... | [5.225337982177734, 5.494143962860107] |
93a93fac-6eb1-4234-9abe-e95de1bce438 | offline-reinforcement-learning-with-value-1 | 2110.09796 | null | https://arxiv.org/abs/2110.09796v1 | https://arxiv.org/pdf/2110.09796v1.pdf | Offline Reinforcement Learning with Value-based Episodic Memory | Offline reinforcement learning (RL) shows promise of applying RL to real-world problems by effectively utilizing previously collected data. Most existing offline RL algorithms use regularization or constraints to suppress extrapolation error for actions outside the dataset. In this paper, we adopt a different framework... | ['Bin Liang', 'Qianchuan Zhao', 'Chongjie Zhang', 'Jun Yang', 'Qihan Liu', 'Hao Hu', 'Yiqin Yang', 'Xiaoteng Ma'] | 2021-10-19 | offline-reinforcement-learning-with-value | https://openreview.net/forum?id=RCZqv9NXlZ | https://openreview.net/pdf?id=RCZqv9NXlZ | iclr-2022-4 | ['d4rl'] | ['robots'] | [-2.18807146e-01 2.16681600e-01 -7.41528928e-01 -2.65028000e-01
-8.63463581e-01 -4.99981165e-01 3.30192983e-01 1.07732967e-01
-6.61531389e-01 1.18019974e+00 2.14714766e-01 -2.07579032e-01
-4.30088520e-01 -5.49838841e-01 -1.01467264e+00 -7.24228203e-01
-5.41845024e-01 1.77844584e-01 3.69544467e-03 -1.46659836... | [4.089904308319092, 2.2021965980529785] |
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