paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
068d65db-2012-4a5d-ac52-53f70b90f58b | a-cap-anticipation-captioning-with | 2304.06602 | null | https://arxiv.org/abs/2304.06602v1 | https://arxiv.org/pdf/2304.06602v1.pdf | A-CAP: Anticipation Captioning with Commonsense Knowledge | Humans possess the capacity to reason about the future based on a sparse collection of visual cues acquired over time. In order to emulate this ability, we introduce a novel task called Anticipation Captioning, which generates a caption for an unseen oracle image using a sparsely temporally-ordered set of images. To ta... | ['Hideki Nakayama', 'Akihiro Sugimoto', 'Quoc-An Luong', 'Duc Minh Vo'] | 2023-04-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Vo_A-Cap_Anticipation_Captioning_With_Commonsense_Knowledge_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Vo_A-Cap_Anticipation_Captioning_With_Commonsense_Knowledge_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-storytelling'] | ['natural-language-processing'] | [ 6.85383856e-01 5.69933593e-01 -1.66354305e-03 -5.22268891e-01
-7.17937350e-01 -6.20409667e-01 1.06493175e+00 -1.26115292e-01
-1.71660319e-01 6.67702079e-01 7.55928278e-01 -1.45177305e-01
5.16869545e-01 -3.09318602e-01 -1.27388728e+00 -5.20875938e-02
2.24916533e-01 4.35167789e-01 -1.22177057e-01 -6.16451465... | [10.900456428527832, 0.9940805435180664] |
bb3af8c9-e7e2-43ef-9655-92eb46bcad40 | learning-to-relate-to-previous-turns-in | 2306.02553 | null | https://arxiv.org/abs/2306.02553v1 | https://arxiv.org/pdf/2306.02553v1.pdf | Learning to Relate to Previous Turns in Conversational Search | Conversational search allows a user to interact with a search system in multiple turns. A query is strongly dependent on the conversation context. An effective way to improve retrieval effectiveness is to expand the current query with historical queries. However, not all the previous queries are related to, and useful ... | ['Yang Liu', 'Peng Li', 'Yutao Zhu', 'Kelong Mao', 'Kaiyu Huang', 'Jian-Yun Nie', 'Fengran Mo'] | 2023-06-05 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 1.05490178e-01 -2.27439731e-01 -4.81814444e-01 -5.12611628e-01
-1.31036055e+00 -8.36434841e-01 7.59127855e-01 5.46071976e-02
-5.08565426e-01 7.14932561e-01 6.15089893e-01 -1.40014244e-02
-1.91300213e-01 -5.80161393e-01 -4.46274608e-01 -4.89588916e-01
3.33006948e-01 7.93205857e-01 4.57808942e-01 -5.24311662... | [11.961304664611816, 7.713310718536377] |
22e5f242-7aa2-414b-8235-ef6b761c5011 | self-prompting-large-language-models-for-open | 2212.08635 | null | https://arxiv.org/abs/2212.08635v2 | https://arxiv.org/pdf/2212.08635v2.pdf | Self-Prompting Large Language Models for Zero-Shot Open-Domain QA | Open-Domain Question Answering (ODQA) aims at answering factoid questions without explicitly providing specific background documents. In a zero-shot setting, this task is more challenging since no data is available to train customized models like Retriever-Readers. Recently, Large Language Models (LLMs) like GPT-3 have... | ['Hai Zhao', 'Zhuosheng Zhang', 'Junlong Li'] | 2022-12-16 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [ 1.37728602e-01 4.91989285e-01 3.34416665e-02 -2.54535377e-01
-1.41536844e+00 -8.16747963e-01 6.26356602e-01 2.32272387e-01
-4.40992147e-01 7.38756895e-01 4.04712588e-01 -7.16234326e-01
-1.33817524e-01 -9.65137482e-01 -8.28790665e-01 -2.61790633e-01
4.13457960e-01 1.00860786e+00 8.84256124e-01 -1.15440559... | [11.248703956604004, 7.976012229919434] |
9b7c5b98-2340-45b8-95cc-6da2a079fd99 | cholectriplet2022-show-me-a-tool-and-tell-me | 2302.06294 | null | https://arxiv.org/abs/2302.06294v1 | https://arxiv.org/pdf/2302.06294v1.pdf | CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection | Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. The benefit is that this formalization helps to obtain a more detailed understanding of tool-tissue interaction which can be used to develop b... | ['Nicolas Padoy', 'Didier Mutter', 'Cristians Gonzalez', 'Barbara Seeliger', 'Pietro Mascagni', 'Nassir Navab', 'Lena Maier-Hein', 'Eduard Vazquez', 'Estevão Lima', 'Jan-Hinrich Nölke', 'Jaime Fonseca', 'Thuy Nuong Tran', 'Sudarshan Regmi', 'Pedro Morais', 'Patrick Godau', 'Max Berniker', 'Shrawan Kumar Thapa', 'Debdoo... | 2023-02-13 | null | null | null | null | ['action-triplet-detection', 'action-triplet-recognition', 'surgical-tool-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.59230277e-01 3.07061493e-01 -6.61856294e-01 4.45197560e-02
-9.78279650e-01 -7.16131032e-01 6.03557646e-01 2.85788298e-01
-4.42774594e-01 2.84368128e-01 5.63995123e-01 -5.27685583e-01
-5.00107408e-01 -6.10701479e-02 -5.02689481e-01 -8.21472943e-01
-3.09075862e-01 5.58423638e-01 -1.41138390e-01 5.28763458... | [14.062952995300293, -3.354719400405884] |
a3eb2492-71f3-4aaf-a9dc-e45732f9c195 | textless-direct-speech-to-speech-translation | 2211.00115 | null | https://arxiv.org/abs/2211.00115v1 | https://arxiv.org/pdf/2211.00115v1.pdf | Textless Direct Speech-to-Speech Translation with Discrete Speech Representation | Research on speech-to-speech translation (S2ST) has progressed rapidly in recent years. Many end-to-end systems have been proposed and show advantages over conventional cascade systems, which are often composed of recognition, translation and synthesis sub-systems. However, most of the end-to-end systems still rely on ... | ['Chung-Cheng Chiu', 'Ye Jia', 'Xinjian Li'] | 2022-10-31 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [ 3.28194618e-01 1.25412136e-01 -3.39099139e-01 -4.58457500e-01
-1.37961483e+00 -3.84572804e-01 7.30401993e-01 -3.81954938e-01
-5.03903925e-01 6.61691606e-01 1.94968686e-01 -7.99966872e-01
8.34040225e-01 -2.24187687e-01 -7.46610701e-01 -4.91589874e-01
7.48076022e-01 7.45906591e-01 4.10417002e-03 -4.73144084... | [14.517621994018555, 7.15815544128418] |
f1a48bd4-66f5-4647-b33c-1f823783192b | measuring-and-modeling-the-motor-system-with | 2103.11775 | null | https://arxiv.org/abs/2103.11775v1 | https://arxiv.org/pdf/2103.11775v1.pdf | Measuring and modeling the motor system with machine learning | The utility of machine learning in understanding the motor system is promising a revolution in how to collect, measure, and analyze data. The field of movement science already elegantly incorporates theory and engineering principles to guide experimental work, and in this review we discuss the growing use of machine le... | ['Mackenzie W. Mathis', 'Alexander Mathis', 'Alessandro Marin Vargas', 'Sébastien B. Hausmann'] | 2021-03-22 | null | null | null | null | ['markerless-motion-capture'] | ['computer-vision'] | [ 9.01549682e-02 -1.62857711e-01 -9.23147440e-01 1.67324111e-01
-3.47126514e-01 -4.38678116e-01 3.07677805e-01 -2.44555265e-01
-9.94638145e-01 7.89014399e-01 4.40386027e-01 -3.11647832e-01
-3.50977719e-01 -2.34890372e-01 -6.35638177e-01 -3.37239057e-01
-4.35844541e-01 9.62949917e-02 1.61320284e-01 -5.13195336... | [6.949415683746338, 0.06248697638511658] |
c2735268-09ac-4a32-aee2-db767bbf9d42 | controllable-continuous-gaze-redirection | 2010.04513 | null | https://arxiv.org/abs/2010.04513v1 | https://arxiv.org/pdf/2010.04513v1.pdf | Controllable Continuous Gaze Redirection | In this work, we present interpGaze, a novel framework for controllable gaze redirection that achieves both precise redirection and continuous interpolation. Given two gaze images with different attributes, our goal is to redirect the eye gaze of one person into any gaze direction depicted in the reference image or to ... | ['Wensen Feng', 'Jing-Hao Xue', 'Yujiu Yang', 'Weihao Xia'] | 2020-10-09 | null | null | null | null | ['gaze-redirection'] | ['computer-vision'] | [ 3.85604143e-01 8.96709040e-02 -3.01927447e-01 -6.72976017e-01
-2.76846915e-01 -3.23305458e-01 4.08053517e-01 -7.73581505e-01
2.38583405e-02 5.72218299e-01 3.41267318e-01 -1.18908323e-02
-4.19294052e-02 -3.81823689e-01 -5.80972254e-01 -8.62650990e-01
2.72877663e-01 -6.97880751e-03 -6.40305653e-02 -4.99324948... | [14.054010391235352, 0.014761364087462425] |
5a7c10f9-776b-4c59-a509-50915047b04e | design-process-is-a-reinforcement-learning | 2211.03136 | null | https://arxiv.org/abs/2211.03136v1 | https://arxiv.org/pdf/2211.03136v1.pdf | Design Process is a Reinforcement Learning Problem | While reinforcement learning has been used widely in research during the past few years, it found fewer real-world applications than supervised learning due to some weaknesses that the RL algorithms suffer from, such as performance degradation in transitioning from the simulator to the real world. Here, we argue the de... | ['Benjamin Dillunberger', 'Reza kakooee'] | 2022-11-06 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [-3.10724258e-01 7.80696794e-02 -2.08664641e-01 -9.79301855e-02
-4.97361869e-01 -7.26216674e-01 2.03240305e-01 -2.29431540e-01
9.39765275e-02 7.94226170e-01 -3.42640765e-02 -8.19938004e-01
-2.20732823e-01 -8.44728351e-01 -4.61345464e-01 -5.07572532e-01
-2.16856048e-01 5.52312613e-01 3.00157100e-01 -2.84327090... | [4.174316883087158, 1.6894373893737793] |
ded8b1b5-9ad3-4f43-9c08-3bb653c069f5 | mixnerf-modeling-a-ray-with-mixture-density | 2302.08788 | null | https://arxiv.org/abs/2302.08788v2 | https://arxiv.org/pdf/2302.08788v2.pdf | MixNeRF: Modeling a Ray with Mixture Density for Novel View Synthesis from Sparse Inputs | Neural Radiance Field (NeRF) has broken new ground in the novel view synthesis due to its simple concept and state-of-the-art quality. However, it suffers from severe performance degradation unless trained with a dense set of images with different camera poses, which hinders its practical applications. Although previou... | ['Nojun Kwak', 'Yeonjin Chang', 'Donghoon Han', 'Seunghyeon Seo'] | 2023-02-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Seo_MixNeRF_Modeling_a_Ray_With_Mixture_Density_for_Novel_View_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Seo_MixNeRF_Modeling_a_Ray_With_Mixture_Density_for_Novel_View_CVPR_2023_paper.pdf | cvpr-2023-1 | ['philosophy'] | ['miscellaneous'] | [ 5.79523556e-02 -5.59687257e-01 3.28507572e-02 -4.12956834e-01
-6.55558527e-01 -4.23862934e-01 5.85223556e-01 -6.70359612e-01
-1.27485484e-01 5.54493964e-01 3.04274797e-01 2.04897132e-02
5.25281839e-02 -1.03687763e+00 -8.93012464e-01 -1.04512787e+00
5.46211421e-01 2.03319103e-01 1.14681326e-01 -2.93339342... | [9.716585159301758, -2.8491177558898926] |
545706e8-5885-4f1f-8561-68b229f45e54 | grammatical-error-detection-based-on-machine | null | null | https://aclanthology.org/W16-4918 | https://aclanthology.org/W16-4918.pdf | Grammatical Error Detection Based on Machine Learning for Mandarin as Second Language Learning | Mandarin is not simple language for foreigner. Even using Mandarin as the mother tongue, they have to spend more time to learn when they were child. The following issues are the reason why causes learning problem. First, the word is envolved by Hieroglyphic. So a character can express meanings independently, but become... | ['Chan-Kun Yeh', 'Tsung-Wei Hsu', 'Jui-Feng Yeh'] | 2016-12-01 | null | null | null | ws-2016-12 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-1.87073052e-01 -1.95665926e-01 -5.72683811e-02 -2.42084846e-01
-2.33300939e-01 -3.56409192e-01 6.06281720e-02 -1.81891650e-01
-5.43754041e-01 9.04037833e-01 2.36812234e-01 -3.73930991e-01
3.34254727e-02 -8.42881620e-01 -7.20631003e-01 -5.85840046e-01
2.87291497e-01 1.53978437e-01 2.81334370e-01 -5.53595722... | [11.006808280944824, 10.77342700958252] |
83f92a54-a87c-4e77-bd31-cba082a00bed | hierarchically-fusing-long-and-short-term | 2304.02089 | null | https://arxiv.org/abs/2304.02089v1 | https://arxiv.org/pdf/2304.02089v1.pdf | Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search | Estimating Click-Through Rate (CTR) is a vital yet challenging task in personalized product search. However, existing CTR methods still struggle in the product search settings due to the following three challenges including how to more effectively extract users' short-term interests with respect to multiple aspects, ho... | ['Qi Rao', 'Jing Zhang', 'Hong Wen', 'Qijie Shen'] | 2023-04-04 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [ 8.36042091e-02 -1.23042285e-01 -6.92407966e-01 -5.77847958e-01
-7.37164319e-01 -3.82963777e-01 8.49878132e-01 -1.53292179e-01
-2.91387647e-01 4.89332616e-01 7.07734764e-01 -8.29697326e-02
-5.03521681e-01 -7.70983040e-01 -3.93570930e-01 -3.47072810e-01
-4.72811945e-02 2.66997397e-01 2.22633481e-02 -4.04479802... | [10.16010856628418, 5.576891899108887] |
9f612edc-6e97-4345-9ae2-10cb6923ea54 | indoor-sound-source-localization-with | 1712.07814 | null | http://arxiv.org/abs/1712.07814v1 | http://arxiv.org/pdf/1712.07814v1.pdf | Indoor Sound Source Localization with Probabilistic Neural Network | It is known that adverse environments such as high reverberation and low
signal-to-noise ratio (SNR) pose a great challenge to indoor sound source
localization. To address this challenge, in this paper, we propose a sound
source localization algorithm based on probabilistic neural network, namely
Generalized cross corr... | ['Susanto Rahardja', 'Jiajia Chen', 'Yingxiang Sun', 'Chau Yuen'] | 2017-12-21 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 3.22545110e-03 -7.98017740e-01 7.48681664e-01 -1.81829497e-01
-1.14052987e+00 -5.97017407e-01 1.31393358e-01 1.01740912e-01
-3.99524361e-01 8.99951756e-01 4.44221273e-02 -5.62126756e-01
-5.23566723e-01 -6.96632087e-01 -3.44340265e-01 -1.05071878e+00
-5.16116023e-01 -2.41474643e-01 1.86778829e-01 1.59058809... | [15.182759284973145, 5.710694789886475] |
64d07c76-3263-4a20-b651-912cbdc62908 | ominacs-online-ml-based-iot-network-attack | 2302.09225 | null | https://arxiv.org/abs/2302.09225v2 | https://arxiv.org/pdf/2302.09225v2.pdf | OMINACS: Online ML-Based IoT Network Attack Detection and Classification System | Several Machine Learning (ML) methodologies have been proposed to improve security in Internet Of Things (IoT) networks and reduce the damage caused by the action of malicious agents. However, detecting and classifying attacks with high accuracy and precision is still a major challenge. This paper proposes an online at... | ['Antônio Abelém', 'Diego Abreu'] | 2023-02-18 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 1.05189867e-02 -5.78456283e-01 -2.21464187e-01 -2.33771130e-02
-4.54245508e-03 -5.00699282e-01 5.59587479e-01 4.68825281e-01
-2.43896008e-01 4.15830195e-01 -3.06613266e-01 -7.50315964e-01
-1.67227224e-01 -1.32986116e+00 -1.35365343e-02 -5.95262349e-01
-1.20820897e-02 3.36895794e-01 4.22827423e-01 5.45520103... | [5.207535743713379, 7.193223476409912] |
efd7dd0f-4529-48bf-9ccc-cd9ad547b6b9 | instancemotseg-real-time-instance-motion | 2008.07008 | null | https://arxiv.org/abs/2008.07008v4 | https://arxiv.org/pdf/2008.07008v4.pdf | Monocular Instance Motion Segmentation for Autonomous Driving: KITTI InstanceMotSeg Dataset and Multi-task Baseline | Moving object segmentation is a crucial task for autonomous vehicles as it can be used to segment objects in a class agnostic manner based on their motion cues. It enables the detection of unseen objects during training (e.g., moose or a construction truck) based on their motion and independent of their appearance. Alt... | ['Ahmad El-Sallab', 'Muhammad Helmi', 'Waleed Hamdy', 'Senthil Yogamani', 'Eslam Mohamed', 'Hazem Rashed', 'Mennatullah Siam', 'Mahmoud Ewaisha'] | 2020-08-16 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 2.21973322e-02 1.89049855e-01 -5.39215624e-01 -3.17537725e-01
-8.12077582e-01 -7.97450662e-01 4.54724967e-01 -1.10144891e-01
-6.80544913e-01 4.27161872e-01 -3.46444309e-01 -5.03114760e-01
2.60829151e-01 -7.95317471e-01 -1.16998947e+00 -6.25910342e-01
4.81059328e-02 6.64210021e-01 1.09922194e+00 -2.47493714... | [8.189046859741211, -1.4365565776824951] |
148f2f14-0a5d-44ff-b232-24ceb248e84e | rrf102-meeting-the-trec-covid-challenge-with | 2010.002 | null | https://arxiv.org/abs/2010.00200v1 | https://arxiv.org/pdf/2010.00200v1.pdf | RRF102: Meeting the TREC-COVID Challenge with a 100+ Runs Ensemble | In this paper, we report the results of our participation in the TREC-COVID challenge. To meet the challenge of building a search engine for rapidly evolving biomedical collection, we propose a simple yet effective weighted hierarchical rank fusion approach, that ensembles together 102 runs from (a) lexical and semanti... | ['Ryan Mcdonald', 'Michael Bendersky', 'Honglei Zhuang', 'Keith Hall', 'Shuguang Han', 'Ji Ma'] | 2020-10-01 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [ 1.92012697e-01 -2.53833890e-01 6.80494383e-02 -4.09446090e-01
-1.70321655e+00 -6.93091631e-01 8.66233051e-01 4.17283416e-01
-1.02228773e+00 8.78559530e-01 6.73445225e-01 -2.29436353e-01
-6.02209508e-01 3.78457978e-02 -1.49296923e-02 -4.55363899e-01
-3.37874830e-01 9.81153548e-01 5.98228633e-01 -5.66456616... | [8.823161125183105, 8.613633155822754] |
7b798cbd-c90f-4869-9770-261c1c4ba3e0 | classification-of-us-supreme-court-cases | 2304.08649 | null | https://arxiv.org/abs/2304.08649v2 | https://arxiv.org/pdf/2304.08649v2.pdf | Classification of US Supreme Court Cases using BERT-Based Techniques | Models based on bidirectional encoder representations from transformers (BERT) produce state of the art (SOTA) results on many natural language processing (NLP) tasks such as named entity recognition (NER), part-of-speech (POS) tagging etc. An interesting phenomenon occurs when classifying long documents such as those ... | ['John E. Ortega', 'Adam Meyers', 'Shubham Vatsal'] | 2023-04-17 | null | null | null | null | ['part-of-speech-tagging'] | ['natural-language-processing'] | [-9.04061273e-02 9.76898894e-02 -8.73509496e-02 -5.76258957e-01
-1.09000039e+00 -7.62591660e-01 1.03782785e+00 5.67624092e-01
-7.52967775e-01 1.08733356e+00 5.89931011e-01 -9.58909929e-01
-2.58008510e-01 -9.88148212e-01 -4.38217342e-01 -3.00086021e-01
1.24769087e-03 7.10081518e-01 2.13143393e-01 -3.10299337... | [9.8367919921875, 9.649561882019043] |
d5018a6f-e3fc-4294-bd70-bc285e1984b2 | bitiimt-a-bilingual-text-infilling-method-for | null | null | https://aclanthology.org/2022.acl-long.138 | https://aclanthology.org/2022.acl-long.138.pdf | BiTIIMT: A Bilingual Text-infilling Method for Interactive Machine Translation | Interactive neural machine translation (INMT) is able to guarantee high-quality translations by taking human interactions into account. Existing IMT systems relying on lexical constrained decoding (LCD) enable humans to translate in a flexible translation order beyond the left-to-right. However, they typically suffer f... | ['Jiajun Chen', 'Shuming Shi', 'ShuJian Huang', 'Qu Cui', 'Guoping Huang', 'Lemao Liu', 'Yanling Xiao'] | null | null | null | null | acl-2022-5 | ['text-infilling'] | ['natural-language-processing'] | [ 5.89174688e-01 5.22451755e-03 -3.18952292e-01 -5.06045341e-01
-1.03612578e+00 -6.33046269e-01 5.89464247e-01 -3.88965160e-01
-4.06420648e-01 8.96454751e-01 2.98449337e-01 -1.02845597e+00
4.77632016e-01 -4.74479795e-01 -1.00724828e+00 -2.21277550e-01
6.13351583e-01 7.70603061e-01 -1.43228635e-01 -4.97719198... | [11.683707237243652, 10.270033836364746] |
1b37ad41-57b9-43f5-9b50-df59154ccb9e | distribution-aligned-feature-clustering-for | 2301.06685 | null | https://arxiv.org/abs/2301.06685v1 | https://arxiv.org/pdf/2301.06685v1.pdf | Distribution Aligned Feature Clustering for Zero-Shot Sketch-Based Image Retrieval | Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a challenging cross-modal retrieval task. In prior arts, the retrieval is conducted by sorting the distance between the query sketch and each image in the gallery. However, the domain gap and the zero-shot setting make neural networks hard to generalize. This paper ta... | ['Huimin Ma', 'Jiansheng Chen', 'Fangzheng Zhao', 'Kun Song', 'Yuchen Wu'] | 2023-01-17 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [-4.01680730e-02 -6.47819221e-01 -5.33115804e-01 -2.12660730e-01
-1.50192046e+00 -8.10405016e-01 9.40916896e-01 -8.59447867e-02
-2.41093189e-01 3.13531369e-01 1.06946230e-02 3.63816172e-01
-4.48100269e-01 -4.60702151e-01 -6.60749257e-01 -7.02487111e-01
3.68513912e-01 6.41534805e-01 3.60998847e-02 -3.71798314... | [11.590350151062012, 0.6940599083900452] |
0c4a531a-e181-410d-aae5-679e036127dc | f-siamese-tracker-a-frustum-based-double | 2010.1151 | null | https://arxiv.org/abs/2010.11510v1 | https://arxiv.org/pdf/2010.11510v1.pdf | F-Siamese Tracker: A Frustum-based Double Siamese Network for 3D Single Object Tracking | This paper presents F-Siamese Tracker, a novel approach for single object tracking prominently characterized by more robustly integrating 2D and 3D information to reduce redundant search space. A main challenge in 3D single object tracking is how to reduce search space for generating appropriate 3D candidates. Instead ... | ['Wanlong Li', 'Feng Wen', 'Yong liu', 'Chujuan Zhang', 'Xin Kong', 'Jinhao Cui', 'Hao Zou'] | 2020-10-22 | null | null | null | null | ['3d-single-object-tracking'] | ['computer-vision'] | [-2.95043707e-01 -1.96450204e-01 -1.69890940e-01 2.97966599e-01
-8.83296132e-01 -9.17015612e-01 4.89214540e-01 -4.11575511e-02
-4.55763578e-01 2.67855018e-01 -4.45621550e-01 -6.15528338e-02
3.61221880e-02 -3.73098820e-01 -8.12862635e-01 -5.66721559e-01
-1.02306642e-01 7.72167087e-01 9.59814787e-01 -1.07718602... | [6.634721755981445, -2.2930946350097656] |
59f77d95-c897-404f-8086-39458f28ce64 | breast-cancer-detection-using-artificial | 2203.04308 | null | https://arxiv.org/abs/2203.04308v1 | https://arxiv.org/pdf/2203.04308v1.pdf | Breast cancer detection using artificial intelligence techniques: A systematic literature review | Cancer is one of the most dangerous diseases to humans, and yet no permanent cure has been developed for it. Breast cancer is one of the most common cancer types. According to the National Breast Cancer foundation, in 2020 alone, more than 276,000 new cases of invasive breast cancer and more than 48,000 non-invasive ca... | ['Omar Elgendy', 'Yaman Afadar', 'Qassim Nasir', 'Manar Abu Talib', 'Ali Bou Nassif'] | 2022-03-08 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 2.64752775e-01 3.33093047e-01 -8.01281691e-01 -2.41021007e-01
-5.77205420e-01 -1.04454540e-01 2.71611009e-02 7.12180436e-01
-5.10034204e-01 6.14708662e-01 1.44855872e-01 -6.39885783e-01
-2.83406992e-02 -9.40032601e-01 -1.83990479e-01 -9.66165721e-01
4.51500863e-02 6.77341163e-01 -8.85239616e-02 -1.52012020... | [15.2711763381958, -2.834515333175659] |
d2f3a3ec-5e46-4423-ab0c-c266534c52d4 | improving-rouge-for-timeline-summarization | null | null | https://aclanthology.org/E17-2046 | https://aclanthology.org/E17-2046.pdf | Improving ROUGE for Timeline Summarization | Current evaluation metrics for timeline summarization either ignore the temporal aspect of the task or require strict date matching. We introduce variants of ROUGE that allow alignment of daily summaries via temporal distance or semantic similarity. We argue for the suitability of these variants in a theoretical analys... | ['Katja Markert', 'Sebastian Martschat'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['timeline-summarization'] | ['natural-language-processing'] | [-8.20564292e-03 -1.28527030e-01 -4.70364481e-01 -5.88821769e-01
-7.74666727e-01 -8.73648584e-01 1.21887589e+00 6.86571836e-01
-4.45464909e-01 8.06446552e-01 1.00173652e+00 -3.04072589e-01
-6.45519614e-01 -4.80386764e-01 -1.26621962e-01 -8.04757923e-02
-5.66392004e-01 2.98755467e-01 3.68840575e-01 -3.19040388... | [12.503496170043945, 9.482003211975098] |
73cef685-5956-4837-85e2-cd7dc285e699 | a-high-fidelity-synthetic-face-framework-for | 2007.08364 | null | https://arxiv.org/abs/2007.08364v1 | https://arxiv.org/pdf/2007.08364v1.pdf | A high fidelity synthetic face framework for computer vision | Analysis of faces is one of the core applications of computer vision, with tasks ranging from landmark alignment, head pose estimation, expression recognition, and face recognition among others. However, building reliable methods requires time-consuming data collection and often even more time-consuming manual annotati... | ['Tadas Baltrusaitis', 'Thomas J. Cashman', 'Marek Kowalski', 'Virginia Estellers', 'Erroll Wood', 'Sebastian Dziadzio', 'Jamie Shotton', 'Charlie Hewitt', 'Matthew Johnson'] | 2020-07-16 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [ 2.61713862e-01 2.99441218e-01 3.57700497e-01 -6.25774801e-01
-6.14966631e-01 -6.64921820e-01 7.36772835e-01 -9.89426151e-02
-1.67594358e-01 7.28079736e-01 -1.17204100e-01 1.49544939e-01
3.65203954e-02 -2.89160192e-01 -4.13001478e-01 -6.30718768e-01
6.78723454e-02 6.97613716e-01 -1.54303685e-01 -1.76050097... | [13.3320951461792, 0.16273164749145508] |
39ac33f1-3201-4a36-abbe-7d0ba46ddfbd | a-deep-neural-architecture-for-harmonizing-3 | 2303.00175 | null | https://arxiv.org/abs/2303.00175v2 | https://arxiv.org/pdf/2303.00175v2.pdf | A Deep Neural Architecture for Harmonizing 3-D Input Data Analysis and Decision Making in Medical Imaging | Harmonizing the analysis of data, especially of 3-D image volumes, consisting of different number of slices and annotated per volume, is a significant problem in training and using deep neural networks in various applications, including medical imaging. Moreover, unifying the decision making of the networks over differ... | ['Stefanos Kollias', 'Anastasios Arsenos', 'Dimitrios Kollias'] | 2023-03-01 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 2.53532261e-01 1.40328750e-01 -2.66295373e-01 -9.23908472e-01
-6.50594592e-01 -4.29345667e-01 6.99918196e-02 5.68858683e-01
-4.67824996e-01 6.57628298e-01 8.72228146e-02 -5.00913918e-01
-5.13494492e-01 -6.72042131e-01 -2.81244636e-01 -8.10983658e-01
-2.98414677e-01 9.04896617e-01 1.00029435e-03 1.17803842... | [14.798783302307129, -2.271860122680664] |
faec938a-e1d1-489f-a76d-3fb768375665 | move2hear-active-audio-visual-source | 2105.07142 | null | https://arxiv.org/abs/2105.07142v2 | https://arxiv.org/pdf/2105.07142v2.pdf | Move2Hear: Active Audio-Visual Source Separation | We introduce the active audio-visual source separation problem, where an agent must move intelligently in order to better isolate the sounds coming from an object of interest in its environment. The agent hears multiple audio sources simultaneously (e.g., a person speaking down the hall in a noisy household) and it mus... | ['Kristen Grauman', 'Ziad Al-Halah', 'Sagnik Majumder'] | 2021-05-15 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Majumder_Move2Hear_Active_Audio-Visual_Source_Separation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Majumder_Move2Hear_Active_Audio-Visual_Source_Separation_ICCV_2021_paper.pdf | iccv-2021-1 | ['audio-source-separation'] | ['audio'] | [ 3.22025210e-01 2.26225868e-01 4.45919275e-01 2.69104421e-01
-1.30785906e+00 -8.73955786e-01 2.67230690e-01 1.79896399e-01
-4.88320917e-01 4.23477739e-01 1.57173917e-01 1.00455374e-01
-2.22901657e-01 -2.54784137e-01 -6.86171293e-01 -9.48613524e-01
-4.50750560e-01 4.54639018e-01 2.30525017e-01 -7.54513545... | [4.290910243988037, 0.9776192903518677] |
171e4250-f51f-468c-b44b-1311d5fce6fa | a-few-shot-sequential-approach-for-object | 2007.01899 | null | https://arxiv.org/abs/2007.01899v2 | https://arxiv.org/pdf/2007.01899v2.pdf | A Few-Shot Sequential Approach for Object Counting | In this work, we address the problem of few-shot multi-class object counting with point-level annotations. The proposed technique leverages a class agnostic attention mechanism that sequentially attends to objects in the image and extracts their relevant features. This process is employed on an adapted prototypical-bas... | ['Negar Rostamzadeh', 'Pegah Kamousi', 'Negin Sokhandan', 'Eniola Alese', 'Alejandro Posada'] | 2020-07-03 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 5.81526041e-01 -2.63874114e-01 -1.60030320e-01 -2.90187776e-01
-8.47871423e-01 -3.66330147e-01 8.42098773e-01 6.61763430e-01
-9.30634856e-01 5.13113976e-01 -3.04982901e-01 1.43551022e-01
4.75616455e-02 -7.97805071e-01 -6.10122502e-01 -5.84929764e-01
2.15420216e-01 7.30711818e-01 8.71824503e-01 1.88830987... | [9.037787437438965, 0.5627545714378357] |
2933ca2b-5a4e-4ac5-8d62-d12baae78deb | the-missing-data-encoder-cross-channel-image | 1905.01861 | null | https://arxiv.org/abs/1905.01861v1 | https://arxiv.org/pdf/1905.01861v1.pdf | The Missing Data Encoder: Cross-Channel Image Completion\\with Hide-And-Seek Adversarial Network | Image completion is the problem of generating whole images from fragments only. It encompasses inpainting (generating a patch given its surrounding), reverse inpainting/extrapolation (generating the periphery given the central patch) as well as colorization (generating one or several channels given other ones). In this... | ['Matthieu Cord', 'Patrick Perez', 'Arnaud Dapogny'] | 2019-05-06 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 5.80285251e-01 4.54580963e-01 3.58134329e-01 -2.41190985e-01
-9.01108623e-01 -7.17281163e-01 7.17590570e-01 -1.65072992e-01
-2.36492306e-01 7.30461836e-01 2.22424716e-02 2.91411448e-02
3.93951893e-01 -8.72266710e-01 -1.30986607e+00 -9.68155444e-01
-8.83224159e-02 4.23861951e-01 -1.57048792e-01 -1.44415066... | [11.798995971679688, -0.6073986291885376] |
7ea7b4d1-90f2-409a-a70b-8f8beda2377b | risk-perspective-exploration-in | 2206.1417 | null | https://arxiv.org/abs/2206.14170v2 | https://arxiv.org/pdf/2206.14170v2.pdf | Risk Perspective Exploration in Distributional Reinforcement Learning | Distributional reinforcement learning demonstrates state-of-the-art performance in continuous and discrete control settings with the features of variance and risk, which can be used to explore. However, the exploration method employing the risk property is hard to find, although numerous exploration methods in Distribu... | ['Se-Young Yun', 'Joonkee Kim', 'Jihwan Oh'] | 2022-06-28 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-3.88774693e-01 1.88002810e-01 -8.23761225e-01 -1.51762828e-01
-1.12571704e+00 -3.12562197e-01 5.64837396e-01 1.76383302e-01
-7.25428700e-01 1.30289114e+00 5.22231795e-02 -3.77450228e-01
-7.72643149e-01 -9.20522690e-01 -3.74669045e-01 -9.82570410e-01
-8.78509641e-01 5.91407835e-01 -3.85807902e-01 -1.58330202... | [4.150386810302734, 2.5610995292663574] |
5f3da63f-86a0-4f45-9505-40e5d45de9cc | marginalized-average-attentional-network-for-1 | 1905.08586 | null | https://arxiv.org/abs/1905.08586v1 | https://arxiv.org/pdf/1905.08586v1.pdf | Marginalized Average Attentional Network for Weakly-Supervised Learning | In weakly-supervised temporal action localization, previous works have failed to locate dense and integral regions for each entire action due to the overestimation of the most salient regions. To alleviate this issue, we propose a marginalized average attentional network (MAAN) to suppress the dominant response of the ... | ['Dit-yan Yeung', 'Ivor W. Tsang', 'Yueming Lyu', 'Yuan Yuan', 'Xi Shen'] | 2019-05-21 | marginalized-average-attentional-network-for | https://openreview.net/forum?id=HkljioCcFQ | https://openreview.net/pdf?id=HkljioCcFQ | iclr-2019-5 | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 3.67815465e-01 -8.21223706e-02 -4.42130208e-01 -1.74618959e-01
-9.47002709e-01 -1.57385454e-01 2.45395049e-01 -3.40165824e-01
-4.28019434e-01 5.14313161e-01 4.44041640e-01 3.16743582e-01
-2.88343191e-01 -4.48302329e-01 -9.54738736e-01 -1.06614614e+00
-2.27722958e-01 1.68254420e-01 6.84395015e-01 1.59637466... | [8.50052547454834, 0.6609737277030945] |
9a594cd8-98da-4887-a97f-8e6b33c60d8f | visualization-and-interpretation-of-latent | 1903.1157 | null | http://arxiv.org/abs/1903.11570v1 | http://arxiv.org/pdf/1903.11570v1.pdf | Visualization and Interpretation of Latent Spaces for Controlling Expressive Speech Synthesis through Audio Analysis | The field of Text-to-Speech has experienced huge improvements last years
benefiting from deep learning techniques. Producing realistic speech becomes
possible now. As a consequence, the research on the control of the
expressiveness, allowing to generate speech in different styles or manners, has
attracted increasing at... | ['Thierry Dutoit', 'Noé Tits', 'Kevin El Haddad', 'Fengna Wang', 'Vincent Pagel'] | 2019-03-27 | null | null | null | null | ['learning-network-representations', 'emotional-speech-synthesis', 'expressive-speech-synthesis'] | ['methodology', 'speech', 'speech'] | [ 3.16085108e-02 3.54011178e-01 -1.28510550e-01 -4.61064786e-01
-2.48085290e-01 -4.34004664e-01 9.98260617e-01 4.81952392e-02
-8.58586356e-02 6.91586852e-01 1.79621905e-01 -8.20767358e-02
-2.11140290e-01 -8.33287418e-01 -4.20972884e-01 -9.44271684e-01
1.55016541e-01 5.61930358e-01 3.89055698e-04 -4.12282467... | [15.028047561645508, 6.327342510223389] |
305d9cbd-bd7f-4441-8827-1a8afad13105 | private-meeting-summarization-without | 2305.15894 | null | https://arxiv.org/abs/2305.15894v1 | https://arxiv.org/pdf/2305.15894v1.pdf | Private Meeting Summarization Without Performance Loss | Meeting summarization has an enormous business potential, but in addition to being a hard problem, roll-out is challenged by privacy concerns. We explore the problem of meeting summarization under differential privacy constraints and find, to our surprise, that while differential privacy leads to slightly lower perform... | ['Anders Søgaard', 'Seolhwa Lee'] | 2023-05-25 | null | null | null | null | ['meeting-summarization'] | ['natural-language-processing'] | [ 3.27989399e-01 6.26736820e-01 -8.30591172e-02 -4.83486116e-01
-1.44405150e+00 -7.21864045e-01 3.45708609e-01 6.92419827e-01
-3.70229840e-01 1.02726960e+00 7.92152226e-01 -3.30552459e-01
-8.70121047e-02 -4.88042057e-01 -4.72423136e-01 -4.48593736e-01
-3.38983864e-01 5.88835180e-01 -2.37287283e-01 -3.84306125... | [6.637302875518799, 4.925988674163818] |
1df71a30-5933-4456-bc00-de6641f9c689 | semantic-preserving-adversarial-text-attacks | 2108.10015 | null | https://arxiv.org/abs/2108.10015v2 | https://arxiv.org/pdf/2108.10015v2.pdf | Semantic-Preserving Adversarial Text Attacks | Deep neural networks (DNNs) are known to be vulnerable to adversarial images, while their robustness in text classification is rarely studied. Several lines of text attack methods have been proposed in the literature, including character-level, word-level, and sentence-level attacks. However, it is still a challenge to... | ['DaCheng Tao', 'Wei Liu', 'James Bailey', 'Weifeng Liu', 'Xinghao Yang'] | 2021-08-23 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 2.11934656e-01 -4.15369689e-01 5.24252243e-02 -2.90848762e-01
-1.97005346e-01 -6.63318694e-01 5.42816043e-01 2.55806118e-01
-7.90838778e-01 5.76713622e-01 2.36629829e-01 -4.50669348e-01
4.31915186e-02 -1.06906319e+00 -5.08876860e-01 -6.06914341e-01
5.94727457e-01 1.42628968e-01 5.01432478e-01 -3.97963554... | [5.979241847991943, 8.08846664428711] |
9016f2cf-ed36-4d29-84b9-111a0f147419 | detecting-incongruity-between-news-headline | 1811.07066 | null | http://arxiv.org/abs/1811.07066v2 | http://arxiv.org/pdf/1811.07066v2.pdf | Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder | Some news headlines mislead readers with overrated or false information, and
identifying them in advance will better assist readers in choosing proper news
stories to consume. This research introduces million-scale pairs of news
headline and body text dataset with incongruity label, which can uniquely be
utilized for d... | ['Kunwoo Park', 'Hongjun Lim', 'Seunghyun Yoon', 'Seungpil Won', 'Kyomin Jung', 'Meeyoung Cha', 'Joongbo Shin'] | 2018-11-17 | null | null | null | null | ['incongruity-detection'] | ['natural-language-processing'] | [-3.03375963e-02 1.78201169e-01 -5.33236623e-01 -4.33787256e-01
-8.55749905e-01 -5.90880215e-01 6.55079365e-01 5.56968153e-01
-3.10053855e-01 7.46254921e-01 1.20246816e+00 -2.05181316e-01
1.62569463e-01 -8.76699746e-01 -9.46996033e-01 -1.79233432e-01
2.89509326e-01 5.29420376e-01 1.25943378e-01 -4.82155174... | [12.20760726928711, 9.369093894958496] |
382f27c4-ee33-4617-93b4-964cdca0634d | a-scale-independent-multi-objective | 2302.04179 | null | https://arxiv.org/abs/2302.04179v4 | https://arxiv.org/pdf/2302.04179v4.pdf | A Scale-Independent Multi-Objective Reinforcement Learning with Convergence Analysis | Many sequential decision-making problems need optimization of different objectives which possibly conflict with each other. The conventional way to deal with a multi-task problem is to establish a scalar objective function based on a linear combination of different objectives. However, for the case of having conflictin... | ['Mohsen Amidzadeh'] | 2023-02-08 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 2.45959327e-01 -2.37263158e-01 1.18724637e-01 -1.52935073e-01
-7.71338761e-01 -1.89520463e-01 2.52787143e-01 5.12497962e-01
-8.15728009e-01 1.22312200e+00 -2.16088787e-01 -6.43812940e-02
-8.89654815e-01 -4.18310523e-01 -3.04638714e-01 -1.09637725e+00
8.62368196e-03 7.14531124e-01 2.05238964e-02 -2.82560199... | [4.367411136627197, 2.423182725906372] |
9323b692-9302-483d-92f7-d08d3a9c5dc4 | are-transformers-effective-for-time-series | 2205.13504 | null | https://arxiv.org/abs/2205.13504v3 | https://arxiv.org/pdf/2205.13504v3.pdf | Are Transformers Effective for Time Series Forecasting? | Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task. Despite the growing performance over the past few years, we question the validity of this line of research in this work. Specifically, Transformers is arguably the most successful solution to extract t... | ['Qiang Xu', 'Lei Zhang', 'Muxi Chen', 'Ailing Zeng'] | 2022-05-26 | null | null | null | null | ['temporal-relation-extraction'] | ['natural-language-processing'] | [ 5.84353507e-02 -1.73343346e-01 -2.97916979e-01 -3.82349342e-01
-6.82562947e-01 -7.73157895e-01 6.70193911e-01 1.96018681e-01
-2.72812671e-03 3.16040188e-01 4.17713881e-01 -7.71002948e-01
-4.06911075e-01 -5.79186618e-01 -6.51604474e-01 -6.05311155e-01
-6.31723106e-01 -1.23939281e-02 2.29219105e-02 -2.69637644... | [7.003968715667725, 2.935858726501465] |
7bccb47f-5bfa-444d-9361-14440b35c04a | consistency-training-with-virtual-adversarial | 2104.07284 | null | https://arxiv.org/abs/2104.07284v2 | https://arxiv.org/pdf/2104.07284v2.pdf | Consistency Training with Virtual Adversarial Discrete Perturbation | Consistency training regularizes a model by enforcing predictions of original and perturbed inputs to be similar. Previous studies have proposed various augmentation methods for the perturbation but are limited in that they are agnostic to the training model. Thus, the perturbed samples may not aid in regularization du... | ['Jaewoo Kang', 'Gyuwan Kim', 'Jungsoo Park'] | 2021-04-15 | null | https://aclanthology.org/2022.naacl-main.414 | https://aclanthology.org/2022.naacl-main.414.pdf | naacl-2022-7 | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [ 4.69299912e-01 5.05182743e-01 -2.99900711e-01 -5.52331626e-01
-7.28474915e-01 -6.10547006e-01 7.01453745e-01 2.35626027e-01
-3.21834236e-01 8.80737901e-01 1.92305237e-01 -2.86549747e-01
4.23670590e-01 -6.85241699e-01 -1.09071064e+00 -4.89602655e-01
4.78239208e-01 3.69890094e-01 9.32239369e-02 -2.51441479... | [10.442755699157715, 7.887442111968994] |
43e5a6be-a1db-4471-97bd-6fb0d8f8cc8c | privacy-against-real-time-speech-emotion | 2211.09273 | null | https://arxiv.org/abs/2211.09273v1 | https://arxiv.org/pdf/2211.09273v1.pdf | Privacy against Real-Time Speech Emotion Detection via Acoustic Adversarial Evasion of Machine Learning | Emotional Surveillance is an emerging area with wide-reaching privacy concerns. These concerns are exacerbated by ubiquitous IoT devices with multiple sensors that can support these surveillance use cases. The work presented here considers one such use case: the use of a speech emotion recognition (SER) classifier tied... | ['Asif Salekin', 'Avery Gump', 'Yi Xiao', 'Brian Testa'] | 2022-11-17 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [ 4.13521320e-01 5.16781092e-01 4.85885710e-01 -1.84508011e-01
-1.05092752e+00 -1.00647783e+00 3.34552974e-01 -2.69469440e-01
-3.21328372e-01 5.20298600e-01 -7.35315681e-02 -4.07425761e-01
2.62196213e-02 -4.55969006e-01 -7.44777203e-01 -9.22602117e-01
-3.52330118e-01 -1.06803171e-01 -2.99862474e-02 -3.02094162... | [13.909370422363281, 5.793196201324463] |
af1e211e-1fe3-4eb4-9dc1-8a6fccb27228 | a-pilot-study-of-text-to-sql-semantic-parsing | 2010.01891 | null | https://arxiv.org/abs/2010.01891v1 | https://arxiv.org/pdf/2010.01891v1.pdf | A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese | Semantic parsing is an important NLP task. However, Vietnamese is a low-resource language in this research area. In this paper, we present the first public large-scale Text-to-SQL semantic parsing dataset for Vietnamese. We extend and evaluate two strong semantic parsing baselines EditSQL (Zhang et al., 2019) and IRNet... | ['Dat Quoc Nguyen', 'Mai Hoang Dao', 'Anh Tuan Nguyen'] | 2020-10-05 | null | https://aclanthology.org/2020.findings-emnlp.364 | https://aclanthology.org/2020.findings-emnlp.364.pdf | findings-of-the-association-for-computational | ['vietnamese-word-segmentation'] | ['natural-language-processing'] | [-1.24744773e-01 4.70047832e-01 -3.29959184e-01 -6.61415577e-01
-1.36487865e+00 -8.90892446e-01 2.81412333e-01 3.08472365e-01
-7.42257655e-01 9.72353637e-01 6.18272662e-01 -3.54482144e-01
2.82112032e-01 -9.25682604e-01 -9.09118593e-01 -8.20696354e-02
1.60143390e-01 7.62267113e-01 3.65478605e-01 -3.76958698... | [10.570528030395508, 9.60124683380127] |
11e3d928-2538-4dd9-b6d4-aa73935cc982 | identification-of-novel-classes-for-improving | 2303.10422 | null | https://arxiv.org/abs/2303.10422v1 | https://arxiv.org/pdf/2303.10422v1.pdf | Identification of Novel Classes for Improving Few-Shot Object Detection | Conventional training of deep neural networks requires a large number of the annotated image which is a laborious and time-consuming task, particularly for rare objects. Few-shot object detection (FSOD) methods offer a remedy by realizing robust object detection using only a few training samples per class. An unexplore... | ['Mohammad Rostami', 'Zeyu Shangguan'] | 2023-03-18 | null | null | null | null | ['robust-object-detection', 'few-shot-object-detection'] | ['computer-vision', 'computer-vision'] | [ 2.82433540e-01 4.03347723e-02 -2.87899584e-01 -3.46814483e-01
-5.34167767e-01 -2.85474002e-01 3.92268121e-01 5.79461306e-02
-5.02600491e-01 6.39986634e-01 -4.42393899e-01 2.12233812e-01
2.68354356e-01 -8.26558590e-01 -6.56395137e-01 -8.42325747e-01
1.97905943e-01 4.12533671e-01 1.18741167e+00 2.66552478... | [9.313141822814941, 1.3877068758010864] |
88108cc7-ced0-4b35-a472-86a008f2ef8d | extreme-q-learning-maxent-rl-without-entropy | 2301.02328 | null | https://arxiv.org/abs/2301.02328v2 | https://arxiv.org/pdf/2301.02328v2.pdf | Extreme Q-Learning: MaxEnt RL without Entropy | Modern Deep Reinforcement Learning (RL) algorithms require estimates of the maximal Q-value, which are difficult to compute in continuous domains with an infinite number of possible actions. In this work, we introduce a new update rule for online and offline RL which directly models the maximal value using Extreme Valu... | ['Stefano Ermon', 'Matthieu Geist', 'Joey Hejna', 'Divyansh Garg'] | 2023-01-05 | null | null | null | null | ['d4rl'] | ['robots'] | [-3.89231175e-01 2.21108675e-01 -4.59626198e-01 -1.42908603e-01
-1.01981139e+00 -7.01338112e-01 3.96289438e-01 1.46317989e-01
-7.57082939e-01 1.20305383e+00 -1.18925437e-01 -5.81681013e-01
-3.96003902e-01 -6.22175932e-01 -8.90578985e-01 -6.93177938e-01
-2.22076356e-01 5.42004108e-01 -3.53059977e-01 -1.83752209... | [4.142472267150879, 2.4391841888427734] |
a4f561cc-a4de-4ba1-b952-a0186f2dc6dc | dynamic-adaptive-threshold-based-learning-for | 2208.10221 | null | https://arxiv.org/abs/2208.10221v1 | https://arxiv.org/pdf/2208.10221v1.pdf | Dynamic Adaptive Threshold based Learning for Noisy Annotations Robust Facial Expression Recognition | The real-world facial expression recognition (FER) datasets suffer from noisy annotations due to crowd-sourcing, ambiguity in expressions, the subjectivity of annotators and inter-class similarity. However, the recent deep networks have strong capacity to memorize the noisy annotations leading to corrupted feature embe... | ['S Balasubramanian', 'Bobbili Veerendra Raj Kumar', 'Naveen Siva Kumar Badveeti', 'Darshan Gera'] | 2022-08-22 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 1.55655205e-01 1.39173329e-01 1.47848189e-01 -9.94394064e-01
-8.15145195e-01 -2.83421069e-01 2.91070461e-01 -2.77824461e-01
-6.73908830e-01 9.86151040e-01 3.22269127e-02 7.76236773e-01
1.27721995e-01 -3.95272672e-01 -7.17368126e-01 -9.75332797e-01
-3.99203151e-02 2.44882286e-01 2.93957070e-02 -2.70459503... | [13.640731811523438, 1.7090647220611572] |
211055d0-6197-418c-a8f6-7873338672f0 | spatially-selective-deep-non-linear-filters | 2211.0242 | null | https://arxiv.org/abs/2211.02420v2 | https://arxiv.org/pdf/2211.02420v2.pdf | Spatially Selective Deep Non-linear Filters for Speaker Extraction | In a scenario with multiple persons talking simultaneously, the spatial characteristics of the signals are the most distinct feature for extracting the target signal. In this work, we develop a deep joint spatial-spectral non-linear filter that can be steered in an arbitrary target direction. For this we propose a simp... | ['Timo Gerkmann', 'Kristina Tesch'] | 2022-11-04 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 6.37548491e-02 -2.10246876e-01 1.48974478e-01 -3.94619018e-01
-1.05142975e+00 -6.91594779e-01 5.38943946e-01 -5.00480771e-01
-4.35946822e-01 6.65062368e-01 4.52651143e-01 -8.08292255e-03
-2.40378588e-01 -2.75853485e-01 -4.16447014e-01 -9.45217550e-01
2.34659016e-02 2.36802369e-01 3.71850818e-01 -1.47879705... | [15.15219783782959, 5.705580234527588] |
e52ee6f7-6417-44bf-9730-29b630e5776c | modernizing-old-photos-using-multiple | 2304.04461 | null | https://arxiv.org/abs/2304.04461v1 | https://arxiv.org/pdf/2304.04461v1.pdf | Modernizing Old Photos Using Multiple References via Photorealistic Style Transfer | This paper firstly presents old photo modernization using multiple references by performing stylization and enhancement in a unified manner. In order to modernize old photos, we propose a novel multi-reference-based old photo modernization (MROPM) framework consisting of a network MROPM-Net and a novel synthetic data g... | ['Munchurl Kim', 'Jae-Ho Lee', 'Hyeonjun Sim', 'Soo Ye Kim', 'Agus Gunawan'] | 2023-04-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Gunawan_Modernizing_Old_Photos_Using_Multiple_References_via_Photorealistic_Style_Transfer_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gunawan_Modernizing_Old_Photos_Using_Multiple_References_via_Photorealistic_Style_Transfer_CVPR_2023_paper.pdf | cvpr-2023-1 | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 3.79850715e-01 -1.09216154e-01 -4.59487103e-02 -2.90013969e-01
-4.67816800e-01 -4.62524652e-01 8.79570663e-01 -6.36724412e-01
-5.91429949e-01 8.20423722e-01 3.22494119e-01 6.61710799e-02
3.67526948e-01 -8.44841897e-01 -9.43606198e-01 -5.45435965e-01
6.74013257e-01 1.62186682e-01 1.37545839e-01 -3.59265476... | [11.484576225280762, -0.6673908829689026] |
cda56a79-63f1-45a1-951e-f4fd3b270142 | group-activity-recognition-using-self | 2303.12149 | null | https://arxiv.org/abs/2303.12149v3 | https://arxiv.org/pdf/2303.12149v3.pdf | SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition | In this paper, we propose a new, simple, and effective Self-supervised Spatio-temporal Transformers (SPARTAN) approach to Group Activity Recognition (GAR) using unlabeled video data. Given a video, we create local and global Spatio-temporal views with varying spatial patch sizes and frame rates. The proposed self-super... | ['Khoa Luu', 'Page Daniel Dobbs', 'Xin Li', 'Han-Seok Seo', 'Alexander H Nelson', 'Pha Nguyen', 'Naga VS Raviteja Chappa'] | 2023-03-06 | null | null | null | null | ['group-activity-recognition'] | ['computer-vision'] | [ 9.90496427e-02 -4.00791645e-01 -6.82879508e-01 -2.78444678e-01
-4.99979585e-01 -4.49917436e-01 7.92568266e-01 1.69172026e-02
-1.94750056e-01 4.78520334e-01 3.77569258e-01 1.20064151e-02
-4.88343388e-01 -5.46286106e-01 -6.98320150e-01 -6.09626114e-01
-3.41541946e-01 2.13380456e-02 4.02536333e-01 7.85450563... | [8.369343757629395, 0.6959756016731262] |
1cd669a6-0da9-4607-9a30-fab2bc36a1aa | exploiting-deep-generative-prior-for | 2003.13659 | null | https://arxiv.org/abs/2003.13659v4 | https://arxiv.org/pdf/2003.13659v4.pdf | Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation | Learning a good image prior is a long-term goal for image restoration and manipulation. While existing methods like deep image prior (DIP) capture low-level image statistics, there are still gaps toward an image prior that captures rich image semantics including color, spatial coherence, textures, and high-level concep... | ['Ping Luo', 'Dahua Lin', 'Xingang Pan', 'Chen Change Loy', 'Bo Dai', 'Xiaohang Zhan'] | 2020-03-30 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3265_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123470256.pdf | eccv-2020-8 | ['image-morphing'] | ['computer-vision'] | [ 4.64270055e-01 2.21158043e-02 2.79613473e-02 -1.88057363e-01
-7.28914082e-01 -7.02850997e-01 5.90218067e-01 -5.28371215e-01
1.20345511e-01 7.94252396e-01 3.08563560e-01 7.89294392e-02
-2.36538728e-03 -1.04667747e+00 -1.07368636e+00 -1.05655837e+00
3.88636023e-01 7.95333907e-02 2.07601637e-02 -4.01208878... | [11.426637649536133, -1.097342610359192] |
a1cfce8b-ae48-4a50-9114-d57a86e3b5c4 | scarcenet-animal-pose-estimation-with-scarce | 2303.15023 | null | https://arxiv.org/abs/2303.15023v1 | https://arxiv.org/pdf/2303.15023v1.pdf | ScarceNet: Animal Pose Estimation with Scarce Annotations | Animal pose estimation is an important but under-explored task due to the lack of labeled data. In this paper, we tackle the task of animal pose estimation with scarce annotations, where only a small set of labeled data and unlabeled images are available. At the core of the solution to this problem setting is the use o... | ['Gim Hee Lee', 'Chen Li'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_ScarceNet_Animal_Pose_Estimation_With_Scarce_Annotations_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_ScarceNet_Animal_Pose_Estimation_With_Scarce_Annotations_CVPR_2023_paper.pdf | cvpr-2023-1 | ['animal-pose-estimation', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 1.70362368e-01 2.27360040e-01 -1.55770749e-01 -5.89917302e-01
-9.92031872e-01 -5.96253991e-01 2.97840863e-01 5.14167622e-02
-7.85707414e-01 9.76856530e-01 -2.03449726e-01 3.50842535e-01
1.97059974e-01 -3.83613437e-01 -1.12178266e+00 -6.09596491e-01
8.87696594e-02 6.98671818e-01 3.85224581e-01 -1.59518287... | [9.310317993164062, 1.2673923969268799] |
a212a2aa-b86c-40d0-9e2e-d9d8f98baaaf | autoencoder-based-anomaly-detection-and | 2210.08011 | null | https://arxiv.org/abs/2210.08011v1 | https://arxiv.org/pdf/2210.08011v1.pdf | Autoencoder based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems | Anomaly detection in large industrial cooling systems is very challenging due to the high data dimensionality, inconsistent sensor recordings, and lack of labels. The state of the art for automated anomaly detection in these systems typically relies on expert knowledge and thresholds. However, data is viewed isolated a... | ['Jana Kemnitz', 'Clemens Heitzinger', 'Daniel Schall', 'Bernhard Haslhofer', 'Thomas Kaufmann', 'Peter Holzner', 'Andreas Stiftinger', 'Leopold Schoeffl', 'Denis Katic', 'Robin Heel', 'Stephanie Holly'] | 2022-10-14 | null | null | null | null | ['fault-localization'] | ['computer-code'] | [ 1.8434735e-02 -4.2365436e-02 7.0848107e-01 -1.8053477e-01
-3.7960792e-01 -4.9673966e-01 6.3287131e-02 7.5829875e-01
5.8617599e-02 4.1838205e-01 -3.7075537e-01 -2.4953972e-01
-7.6377821e-01 -6.1941713e-01 -5.9335494e-01 -7.8966224e-01
-5.5710804e-01 5.1804447e-01 -1.6115341e-01 -1.5419528e-01
2.3510309e-01... | [6.885104179382324, 2.471644639968872] |
75e2bf62-1c8c-479a-8ce2-89106827be3b | advancing-incremental-few-shot-semantic | 2305.10868 | null | https://arxiv.org/abs/2305.10868v1 | https://arxiv.org/pdf/2305.10868v1.pdf | Advancing Incremental Few-shot Semantic Segmentation via Semantic-guided Relation Alignment and Adaptation | Incremental few-shot semantic segmentation (IFSS) aims to incrementally extend a semantic segmentation model to novel classes according to only a few pixel-level annotated data, while preserving its segmentation capability on previously learned base categories. This task faces a severe semantic-aliasing issue between b... | ['Qi Tian', 'Richang Hong', 'Shijie Hao', 'Yanrong Guo', 'Xin Chen', 'Yuan Zhou'] | 2023-05-18 | null | null | null | null | ['few-shot-image-segmentation', 'incremental-learning'] | ['computer-vision', 'methodology'] | [ 5.35927892e-01 1.39243707e-01 -1.81815431e-01 -6.27868593e-01
-3.74484986e-01 -3.65994394e-01 3.64289075e-01 2.93142706e-01
-4.98178661e-01 4.42212045e-01 8.77418220e-02 2.50284702e-01
-1.64970383e-02 -8.13569069e-01 -5.86638749e-01 -7.59885132e-01
5.23198783e-01 2.58251369e-01 8.21497560e-01 -1.11908048... | [9.619535446166992, 1.4217885732650757] |
47cf3a73-50ba-4505-a9d6-1789665b6fd0 | a-multi-purpose-and-large-scale-speech-corpus | 1912.03627 | null | https://arxiv.org/abs/1912.03627v1 | https://arxiv.org/pdf/1912.03627v1.pdf | A Multi Purpose and Large Scale Speech Corpus in Persian and English for Speaker and Speech Recognition: the DeepMine Database | DeepMine is a speech database in Persian and English designed to build and evaluate text-dependent, text-prompted, and text-independent speaker verification, as well as Persian speech recognition systems. It contains more than 1850 speakers and 540 thousand recordings overall, more than 480 hours of speech are transcri... | ['Jan "Honza\'\' Černocký', 'Lukáš Burget', 'Hossein Zeinali'] | 2019-12-08 | null | null | null | null | ['text-independent-speaker-verification', 'text-dependent-speaker-verification'] | ['speech', 'speech'] | [-1.67693213e-01 2.30878871e-02 -1.77972570e-01 -9.41310585e-01
-1.36209190e+00 -5.03792167e-01 7.69750059e-01 -1.95960596e-01
-5.78718960e-01 4.74025428e-01 6.05427146e-01 -6.23041570e-01
5.32982588e-01 -2.53601419e-03 -1.94013923e-01 -7.09287047e-01
4.81799468e-02 9.39004481e-01 -3.41769814e-01 -4.59412634... | [14.281731605529785, 6.335227012634277] |
7951f3c6-7a80-46e6-823a-87892386de8f | deep-learning-for-chemometric-and-non | 1910.00391 | null | https://arxiv.org/abs/1910.00391v4 | https://arxiv.org/pdf/1910.00391v4.pdf | Deep learning for Chemometric and non-translational data | We propose a novel method to train deep convolutional neural networks which learn from multiple data sets of varying input sizes through weight sharing. This is an advantage in chemometrics where individual measurements represent exact chemical compounds and thus signals cannot be translated or resized without disturbi... | ['Jacob Søgaard Larsen', 'Line Clemmensen'] | 2019-10-01 | null | null | null | null | ['small-data'] | ['computer-vision'] | [ 6.91507041e-01 -1.59695596e-01 1.45695716e-01 -3.39312881e-01
-7.40234792e-01 -7.17616558e-01 5.89769125e-01 5.45538843e-01
-1.10275137e+00 1.24703813e+00 -2.16813549e-01 -1.89017564e-01
-1.13173105e-01 -9.20907855e-01 -1.25614679e+00 -9.59982574e-01
-2.01769963e-01 4.81322974e-01 1.41258081e-02 2.08973251... | [5.223744869232178, 5.728835105895996] |
479f6c30-373b-4f70-93eb-53b52e3d7248 | 3d-dynamic-scene-graphs-actionable-spatial | 2002.06289 | null | https://arxiv.org/abs/2002.06289v2 | https://arxiv.org/pdf/2002.06289v2.pdf | 3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans | We present a unified representation for actionable spatial perception: 3D Dynamic Scene Graphs. Scene graphs are directed graphs where nodes represent entities in the scene (e.g. objects, walls, rooms), and edges represent relations (e.g. inclusion, adjacency) among nodes. Dynamic scene graphs (DSGs) extend this notion... | ['Luca Carlone', 'Antoni Rosinol', 'Jingnan Shi', 'Arjun Gupta', 'Marcus Abate'] | 2020-02-15 | null | null | null | null | ['robot-task-planning'] | ['robots'] | [-4.56945337e-02 3.50706428e-01 3.39479148e-01 -2.17530847e-01
3.08498502e-01 -6.00303531e-01 8.64702106e-01 3.42160583e-01
-3.59513789e-01 7.35039115e-01 1.56269863e-01 -1.31890863e-01
-3.33634287e-01 -8.95940423e-01 -8.16223681e-01 -3.26583028e-01
-6.28431082e-01 9.45857942e-01 9.15072799e-01 -4.68535602... | [4.8268232345581055, 0.46816226840019226] |
7f3e5114-0926-4f13-8bb0-158f3cfaab09 | 3d-carigan-an-end-to-end-solution-to-3d | 2003.06841 | null | https://arxiv.org/abs/2003.06841v2 | https://arxiv.org/pdf/2003.06841v2.pdf | 3D-CariGAN: An End-to-End Solution to 3D Caricature Generation from Face Photos | Caricature is a type of artistic style of human faces that attracts considerable attention in the entertainment industry. So far a few 3D caricature generation methods exist and all of them require some caricature information (e.g., a caricature sketch or 2D caricature) as input. This kind of input, however, is difficu... | ['Juyong Zhang', 'MinJing Yu', 'Ran Yi', 'Yanan sun', 'Mengfei Xia', 'Yong-Jin Liu', 'Yu-Kun Lai', 'Zipeng Ye'] | 2020-03-15 | null | null | null | null | ['caricature'] | ['computer-vision'] | [ 6.48509711e-02 3.20190668e-01 2.44368225e-01 -4.87800419e-01
-2.11560562e-01 -4.89060223e-01 3.89805645e-01 -7.67913640e-01
1.65989593e-01 3.23021084e-01 1.06060430e-01 3.05118598e-02
2.49020815e-01 -7.33134806e-01 -1.03604531e+00 -2.95774788e-01
3.55793297e-01 7.15203404e-01 -5.18616140e-01 -2.85426199... | [12.740900039672852, -0.2006881982088089] |
0a5a7279-117f-4a79-8503-f981e583f6e9 | conformal-prediction-with-missing-values | 2306.02732 | null | https://arxiv.org/abs/2306.02732v1 | https://arxiv.org/pdf/2306.02732v1.pdf | Conformal Prediction with Missing Values | Conformal prediction is a theoretically grounded framework for constructing predictive intervals. We study conformal prediction with missing values in the covariates -- a setting that brings new challenges to uncertainty quantification. We first show that the marginal coverage guarantee of conformal prediction holds on... | ['Yaniv Romano', 'Julie Josse', 'Aymeric Dieuleveut', 'Margaux Zaffran'] | 2023-06-05 | null | null | null | null | ['imputation', 'prediction-intervals', 'imputation', 'imputation'] | ['computer-vision', 'miscellaneous', 'miscellaneous', 'time-series'] | [ 4.21319455e-01 5.71879804e-01 -5.25521278e-01 -7.30057418e-01
-1.38288176e+00 -5.24435222e-01 3.13093774e-02 2.88622767e-01
1.85873643e-01 1.28844893e+00 5.82601190e-01 -3.24530184e-01
-6.87487781e-01 -9.52734470e-01 -1.10958743e+00 -6.23223662e-01
-2.00758114e-01 7.10452616e-01 -3.46491337e-01 1.02965035... | [7.768102169036865, 4.618338584899902] |
6c3e07f7-071d-4c0e-83ef-a22b074157ac | augmentation-methods-on-monophonic-audio-for | 1911.12505 | null | https://arxiv.org/abs/1911.12505v2 | https://arxiv.org/pdf/1911.12505v2.pdf | Augmentation Methods on Monophonic Audio for Instrument Classification in Polyphonic Music | Instrument classification is one of the fields in Music Information Retrieval (MIR) that has attracted a lot of research interest. However, the majority of that is dealing with monophonic music, while efforts on polyphonic material mainly focus on predominant instrument recognition. In this paper, we propose an approac... | ['Petros Maragos', 'Christos Garoufis', 'Agelos Kratimenos', 'Kleanthis Avramidis', 'Athanasia Zlatintsi'] | 2019-11-28 | null | null | null | null | ['instrument-recognition'] | ['audio'] | [ 3.97634625e-01 -2.33232960e-01 -1.73719719e-01 -4.94679734e-02
-9.94257152e-01 -8.33416998e-01 5.09660423e-01 2.96247274e-01
-3.63741279e-01 3.54031175e-01 2.99941182e-01 1.17535934e-01
-3.46712351e-01 -4.38531339e-01 -4.29875970e-01 -6.64195597e-01
-6.68612197e-02 1.64289266e-01 -2.23608926e-01 -2.99476027... | [15.844988822937012, 5.247359752655029] |
189b1beb-2d68-4f2d-b5c2-4a145ff68b34 | optimistic-temporal-difference-learning-for | 2111.1109 | null | https://arxiv.org/abs/2111.11090v1 | https://arxiv.org/pdf/2111.11090v1.pdf | Optimistic Temporal Difference Learning for 2048 | Temporal difference (TD) learning and its variants, such as multistage TD (MS-TD) learning and temporal coherence (TC) learning, have been successfully applied to 2048. These methods rely on the stochasticity of the environment of 2048 for exploration. In this paper, we propose to employ optimistic initialization (OI) ... | ['I-Chen Wu', 'Lung-Pin Chen', 'Hung Guei'] | 2021-11-22 | null | null | null | null | ['2048'] | ['playing-games'] | [-2.92095721e-01 4.10796255e-02 -4.67300355e-01 1.40530691e-01
-9.89637852e-01 -2.39786997e-01 6.85853124e-01 4.29120027e-02
-7.26338148e-01 9.63801026e-01 1.67627603e-01 -4.07928109e-01
-1.65354628e-02 -6.43619835e-01 -5.66267908e-01 -9.76904452e-01
-7.72291780e-01 2.94011861e-01 6.29043758e-01 -6.49297908... | [3.864392042160034, 1.8637675046920776] |
9358e140-115d-47a9-86cf-ad03bce23bf8 | event-event-relation-extraction-using | null | null | https://openreview.net/forum?id=USuyAFWEuY | https://openreview.net/pdf?id=USuyAFWEuY | Event-Event Relation Extraction using Probabilistic Box Embedding | To understand a story with multiple events, it is important to capture the proper relations across these events. However, existing event relation extraction (ERE) framework regards it as a multi-class classification task and do not guarantee any coherence between different relation types, such as anti-symmetry. If a ph... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['event-relation-extraction'] | ['natural-language-processing'] | [ 3.04066449e-01 4.69549298e-01 -4.95656967e-01 -4.66254801e-01
-3.24468821e-01 -6.90699577e-01 9.59686816e-01 6.53674066e-01
-1.09380849e-01 1.02577889e+00 4.54453826e-01 -4.90353197e-01
-2.94705093e-01 -1.10923648e+00 -6.41801476e-01 -9.47094858e-02
-2.10075476e-03 4.32763875e-01 5.60762823e-01 -1.50055990... | [9.160270690917969, 9.08932113647461] |
faf1c000-d36c-40e1-bb5f-0a8d78238364 | degradation-noise-aware-deep-unfolding | 2305.04047 | null | https://arxiv.org/abs/2305.04047v1 | https://arxiv.org/pdf/2305.04047v1.pdf | Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising | Hyperspectral imaging (HI) has emerged as a powerful tool in diverse fields such as medical diagnosis, industrial inspection, and agriculture, owing to its ability to detect subtle differences in physical properties through high spectral resolution. However, hyperspectral images (HSIs) are often quite noisy because of ... | ['Wilfried Philips', 'Hiep Luong', 'Hongyan zhang', 'Shaoguang Huang', 'Kai Feng', 'JieZhang Cao', 'Haijin Zeng'] | 2023-05-06 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [ 5.96969903e-01 -3.91599983e-01 2.94773340e-01 -1.84137598e-01
-6.81457818e-01 -2.71281719e-01 5.93255572e-02 -1.97158217e-01
6.54219314e-02 5.72687984e-01 1.47157252e-01 4.14351039e-02
-4.97810423e-01 -8.74347150e-01 -5.46444058e-01 -1.34326899e+00
1.97156847e-01 -7.07674921e-02 1.44627839e-01 -1.78881407... | [10.341517448425293, -1.9601426124572754] |
7875d2da-c468-486d-81f6-dffae6bf142e | continual-reasoning-non-monotonic-reasoning | 2305.02171 | null | https://arxiv.org/abs/2305.02171v1 | https://arxiv.org/pdf/2305.02171v1.pdf | Continual Reasoning: Non-Monotonic Reasoning in Neurosymbolic AI using Continual Learning | Despite the extensive investment and impressive recent progress at reasoning by similarity, deep learning continues to struggle with more complex forms of reasoning such as non-monotonic and commonsense reasoning. Non-monotonicity is a property of non-classical reasoning typically seen in commonsense reasoning, whereby... | ["Artur S. d'Avila Garcez", 'Sofoklis Kyriakopoulos'] | 2023-05-03 | null | null | null | null | ['tensor-networks', 'relational-reasoning'] | ['methodology', 'natural-language-processing'] | [ 2.27205917e-01 4.22522098e-01 -1.32714063e-01 -6.30200028e-01
-1.58851102e-01 -3.90019000e-01 8.31357300e-01 1.43525884e-01
-4.19911057e-01 9.40989733e-01 3.78044210e-02 -6.98808730e-01
-8.64487171e-01 -1.12378776e+00 -7.33016849e-01 -3.29119533e-01
-1.56816289e-01 6.87888324e-01 3.38791639e-01 -7.20301628... | [9.099285125732422, 7.105953693389893] |
b7d90785-4972-4805-b1f2-c88abcbe4dab | exploring-the-state-of-the-art-language | 2211.01736 | null | https://arxiv.org/abs/2211.01736v2 | https://arxiv.org/pdf/2211.01736v2.pdf | Transformers on Multilingual Clause-Level Morphology | This paper describes our winning systems in MRL: The 1st Shared Task on Multilingual Clause-level Morphology (EMNLP 2022 Workshop) designed by KUIS AI NLP team. We present our work for all three parts of the shared task: inflection, reinflection, and analysis. We mainly explore transformers with two approaches: (i) tra... | ['Deniz Yuret', 'Gözde Gül Şahin', 'Müge Kural', 'Tilek Chubakov', 'Emre Can Acikgoz'] | 2022-11-03 | null | null | null | null | ['lemmatization', 'morphological-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [-6.35815784e-02 3.14963609e-01 -1.84180841e-01 -5.18984675e-01
-1.24913967e+00 -9.27088797e-01 5.72912276e-01 4.56857890e-01
-8.37968826e-01 7.59428561e-01 3.66104841e-01 -7.59977102e-01
1.72193170e-01 -4.92392838e-01 -8.88602853e-01 -2.54883885e-01
-1.26479805e-01 1.13264155e+00 4.03273525e-03 -7.42111444... | [10.637496948242188, 9.9209623336792] |
4bba99f6-e09b-45b2-b7e8-8bc456bb28a2 | learning-c-to-x86-translation-an-experiment | 2108.07639 | null | https://arxiv.org/abs/2108.07639v2 | https://arxiv.org/pdf/2108.07639v2.pdf | Learning C to x86 Translation: An Experiment in Neural Compilation | Deep learning has had a significant impact on many fields. Recently, code-to-code neural models have been used in code translation, code refinement and decompilation. However, the question of whether these models can automate compilation has yet to be investigated. In this work, we explore neural compilation, building ... | ["Michael F. P. O'Boyle", 'Jordi Armengol-Estapé'] | 2021-08-17 | null | https://openreview.net/forum?id=444ug_EYXet | https://openreview.net/pdf?id=444ug_EYXet | neurips-workshop-aiplans-2021-12 | ['code-translation'] | ['computer-code'] | [-5.45461569e-03 -1.76935513e-02 -3.86010885e-01 -5.68240106e-01
-5.63061059e-01 -6.56337976e-01 4.67603445e-01 1.28999084e-01
1.50374714e-02 6.75398171e-01 2.95633107e-01 -1.08252501e+00
4.67933506e-01 -7.51822710e-01 -1.09765780e+00 -1.27855957e-01
-3.75772230e-02 6.84077144e-02 -1.93294555e-01 -2.86695927... | [7.765370845794678, 7.764753818511963] |
16e3e0ff-27c6-4996-bccd-a7467a5738ef | automatic-word-association-norms-awan | null | null | https://aclanthology.org/2020.cogalex-1.17 | https://aclanthology.org/2020.cogalex-1.17.pdf | Automatic Word Association Norms (AWAN) | Word Association Norms (WAN) are collections that present stimuli words and the set of their associated responses. The corpus is widely used in diverse areas of expertise. In order to reduce the effort to have a good quality resource that can be reproduced in many languages with minimum sources, a methodology to build ... | ['Helena Gomez-Adorno', 'Gemma Bel-Enguix', 'Gerardo Sierra Martínez', 'Jorge Reyes-Magaña'] | null | null | null | null | coling-cogalex-2020-12 | ['learning-word-embeddings', 'reverse-dictionary'] | ['methodology', 'natural-language-processing'] | [-5.93524426e-02 -1.10853612e-01 -8.79122987e-02 -1.70963526e-01
-2.41791233e-01 -4.56517667e-01 8.48277926e-01 6.87092185e-01
-1.04943335e+00 5.52506804e-01 5.85027397e-01 -2.16602281e-01
-2.95391172e-01 -9.34667528e-01 8.96053240e-02 -3.60544950e-01
-3.60875949e-03 5.34013450e-01 3.75342309e-01 -6.22648478... | [10.43235969543457, 8.915478706359863] |
4f394287-343d-445d-9ba7-7673e93d735d | stereogan-bridging-synthetic-to-real-domain | 2005.01927 | null | https://arxiv.org/abs/2005.01927v1 | https://arxiv.org/pdf/2005.01927v1.pdf | StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching | Large-scale synthetic datasets are beneficial to stereo matching but usually introduce known domain bias. Although unsupervised image-to-image translation networks represented by CycleGAN show great potential in dealing with domain gap, it is non-trivial to generalize this method to stereo matching due to the problem o... | ['Hongsheng Li', 'Chengxi Yang', 'Wenxiu Sun', 'Rui Liu', 'Xiaogang Wang'] | 2020-05-05 | stereogan-bridging-synthetic-to-real-domain-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Liu_StereoGAN_Bridging_Synthetic-to-Real_Domain_Gap_by_Joint_Optimization_of_Domain_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_StereoGAN_Bridging_Synthetic-to-Real_Domain_Gap_by_Joint_Optimization_of_Domain_CVPR_2020_paper.pdf | cvpr-2020-6 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 3.59451503e-01 6.63631558e-02 -1.75982654e-01 -4.26707298e-01
-7.10759938e-01 -3.81978959e-01 6.77303195e-01 -6.12320602e-01
-9.54568312e-02 9.15448189e-01 2.41721407e-01 5.02275134e-06
1.15262054e-01 -8.27563941e-01 -8.99572551e-01 -7.27312922e-01
6.09459519e-01 2.66777962e-01 1.23945564e-01 -4.24901366... | [8.92290210723877, -2.301494598388672] |
fa082731-bb5f-42fe-8356-75fd5eb93442 | decoupling-pseudo-label-disambiguation-and | 2305.17699 | null | https://arxiv.org/abs/2305.17699v1 | https://arxiv.org/pdf/2305.17699v1.pdf | Decoupling Pseudo Label Disambiguation and Representation Learning for Generalized Intent Discovery | Generalized intent discovery aims to extend a closed-set in-domain intent classifier to an open-world intent set including in-domain and out-of-domain intents. The key challenges lie in pseudo label disambiguation and representation learning. Previous methods suffer from a coupling of pseudo label disambiguation and re... | ['Weiran Xu', 'Yunsen Xian', 'Jingang Wang', 'Pei Wang', 'Chen Zeng', 'Keqing He', 'Xiaoshuai Song', 'Yutao Mou'] | 2023-05-28 | null | null | null | null | ['pseudo-label', 'intent-discovery'] | ['miscellaneous', 'natural-language-processing'] | [ 3.80379528e-01 -2.56911889e-02 -5.54634035e-01 -5.02685845e-01
-8.77856612e-01 -8.04351807e-01 7.74915516e-01 2.17549697e-01
-1.56027079e-01 5.74970007e-01 4.48018163e-01 3.53246406e-02
-2.52907276e-01 -4.68354166e-01 -1.30589381e-01 -4.83532161e-01
2.12490223e-02 5.63620329e-01 -2.02407733e-01 -1.32147029... | [9.705689430236816, 3.865482807159424] |
1ba02195-00fa-4d7c-8c2e-c6e1073f07be | pa-gm-position-aware-learning-of-embedding | 2301.01932 | null | https://arxiv.org/abs/2301.01932v1 | https://arxiv.org/pdf/2301.01932v1.pdf | PA-GM: Position-Aware Learning of Embedding Networks for Deep Graph Matching | Graph matching can be formalized as a combinatorial optimization problem, where there are corresponding relationships between pairs of nodes that can be represented as edges. This problem becomes challenging when there are potential ambiguities present due to nodes and edges with high similarity, and there is a need to... | ['Zhihong Zhang', 'Lichi Zhang', 'Yuxing Dai', 'Dongdong Chen'] | 2023-01-05 | null | null | null | null | ['graph-matching'] | ['graphs'] | [-1.45551890e-01 7.21082911e-02 -5.05317390e-01 -4.85919625e-01
-5.98487556e-01 -5.39417863e-01 2.48169780e-01 5.24847150e-01
-6.95111752e-02 7.44431242e-02 4.10408705e-01 -1.94475427e-01
-3.68700683e-01 -1.20868027e+00 -5.71103990e-01 -3.81024897e-01
-2.68098563e-01 3.67365122e-01 -5.65511473e-02 -2.06053078... | [7.163776397705078, 6.40528678894043] |
8585f9b8-b3c4-486f-acda-ad2b256b4177 | a-personal-model-of-trumpery-deception | 1811.01938 | null | http://arxiv.org/abs/1811.01938v1 | http://arxiv.org/pdf/1811.01938v1.pdf | A personal model of trumpery: Deception detection in a real-world high-stakes setting | Language use reveals information about who we are and how we feel1-3. One of
the pioneers in text analysis, Walter Weintraub, manually counted which types
of words people used in medical interviews and showed that the frequency of
first-person singular pronouns (i.e., I, me, my) was a reliable indicator of
depression, ... | ['Sophie van der Zee', 'Ronald Poppe', 'Aurelien Baillon', 'Alice Havrileck'] | 2018-11-05 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [-1.72634602e-01 3.42416018e-02 -5.61330557e-01 -5.50108075e-01
-7.90146708e-01 -6.74433529e-01 7.06509829e-01 7.05569267e-01
-8.46180499e-01 7.09953547e-01 7.69662917e-01 -6.12548947e-01
3.04603964e-01 -6.93927705e-01 -2.49539968e-02 -1.96744218e-01
2.43184999e-01 2.14774400e-01 -4.65091884e-01 -3.26456934... | [8.64136791229248, 10.387162208557129] |
fcb5d25a-604b-4146-ba03-f41059254fbf | generalizing-multimodal-pre-training-into | 2206.11091 | null | https://arxiv.org/abs/2206.11091v1 | https://arxiv.org/pdf/2206.11091v1.pdf | Generalizing Multimodal Pre-training into Multilingual via Language Acquisition | English-based Vision-Language Pre-training (VLP) has achieved great success in various downstream tasks. Some efforts have been taken to generalize this success to non-English languages through Multilingual Vision-Language Pre-training (M-VLP). However, due to the large number of languages, M-VLP models often require h... | ['Qin Jin', 'Anwen Hu', 'Liang Zhang'] | 2022-05-29 | null | null | null | null | ['video-text-retrieval', 'language-acquisition'] | ['computer-vision', 'natural-language-processing'] | [-1.39309257e-01 -3.30008924e-01 -2.74917483e-01 -4.41951752e-01
-1.21239591e+00 -6.29375279e-01 9.16072488e-01 -1.02611706e-01
-1.04700077e+00 5.39483964e-01 8.86862502e-02 -7.81312108e-01
5.69412768e-01 -4.82596070e-01 -1.09148216e+00 -2.23426774e-01
4.74165589e-01 5.08595228e-01 1.99508648e-02 -2.64538914... | [11.123297691345215, 1.5858856439590454] |
4ba94ca3-075e-4ac6-a9b1-4a1137e317c3 | quasi-score-matching-estimation-for-spatial | 2305.19721 | null | https://arxiv.org/abs/2305.19721v1 | https://arxiv.org/pdf/2305.19721v1.pdf | Quasi-Score Matching Estimation for Spatial Autoregressive Model with Random Weights Matrix and Regressors | With the rapid advancements in technology for data collection, the application of the spatial autoregressive (SAR) model has become increasingly prevalent in real-world analysis, particularly when dealing with large datasets. However, the commonly used quasi-maximum likelihood estimation (QMLE) for the SAR model is not... | ['Tao Zou', 'Xuan Liang'] | 2023-05-31 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [ 6.86225966e-02 -1.03252083e-01 -3.40307534e-01 -2.75137752e-01
-6.83956921e-01 -1.46340251e-01 4.35989708e-01 7.50893578e-02
-3.42957884e-01 8.54229867e-01 1.34216640e-02 -6.69613659e-01
-7.92403817e-01 -8.61585259e-01 -6.79507256e-01 -8.58785331e-01
-3.65913957e-01 1.11345567e-01 6.09227782e-03 1.39060035... | [7.0577569007873535, 4.435070037841797] |
6a6ea55e-6c2b-4353-88aa-4dfc501b7d12 | fha-kitchens-a-novel-dataset-for-fine-grained | 2306.10858 | null | https://arxiv.org/abs/2306.10858v1 | https://arxiv.org/pdf/2306.10858v1.pdf | FHA-Kitchens: A Novel Dataset for Fine-Grained Hand Action Recognition in Kitchen Scenes | A typical task in the field of video understanding is hand action recognition, which has a wide range of applications. Existing works either mainly focus on full-body actions, or the defined action categories are relatively coarse-grained. In this paper, we propose FHA-Kitchens, a novel dataset of fine-grained hand act... | ['DaCheng Tao', 'Yonggang Wen', 'Bo Du', 'Han Hu', 'Yong Luo', 'Jing Zhang', 'YongQian Li', 'Ting Zhe'] | 2023-06-19 | null | null | null | null | ['action-recognition-in-videos', 'video-understanding', 'domain-generalization'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 4.9363843e-01 -2.6690355e-01 -6.1807913e-01 -5.7312835e-02
-5.8866650e-01 -6.9790280e-01 3.9695442e-01 -5.5426037e-01
-1.3674207e-02 3.7605420e-01 6.5979034e-01 1.1934051e-01
-2.7543488e-01 -5.8610958e-01 -7.0935941e-01 -8.9777654e-01
6.8585940e-02 3.7238947e-01 3.1756294e-01 6.1984800e-02
2.1343444e-01... | [7.915399551391602, 0.4045765995979309] |
af31d262-177a-4224-9399-81323047211f | flipping-coins-to-estimate-pseudocounts-for | 2306.03186 | null | https://arxiv.org/abs/2306.03186v1 | https://arxiv.org/pdf/2306.03186v1.pdf | Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning | We propose a new method for count-based exploration in high-dimensional state spaces. Unlike previous work which relies on density models, we show that counts can be derived by averaging samples from the Rademacher distribution (or coin flips). This insight is used to set up a simple supervised learning objective which... | ['George Konidaris', 'Akhil Bagaria', 'Sam Lobel'] | 2023-06-05 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-2.36787694e-03 6.13626912e-02 -5.74254870e-01 6.61037639e-02
-9.65777457e-01 -6.91487253e-01 7.01142550e-01 1.36888623e-01
-8.39063168e-01 1.44740641e+00 3.67102437e-02 -9.33178186e-01
-2.49117211e-01 -8.64792049e-01 -6.94295228e-01 -7.89022326e-01
-5.59394658e-01 9.98503208e-01 -1.22198820e-01 -2.15164348... | [4.01582145690918, 1.9450535774230957] |
c66d2dca-e942-46f7-8e2a-9d30c211f290 | automated-heartbeat-classification-using-3-d | null | null | https://xueshu.baidu.com/usercenter/paper/show?paperid=18c6ee969fc95c965365f1209b341c19&site=xueshu_se | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8734061 | Automated Heartbeat Classification Using 3-D Inputs Based on Convolutional Neural Network With Multi-Fields of View | A high-performance method of automated heartbeat classification based on Convolutional
Neural Network (CNN) is proposed in this paper. To make full use of the electrocardiogram information
acquired from different parts of the human body, we present a novel 3-D data structure as the input of
the CNN. The 3-D structur... | ['AND ZHENYAN LIU2', 'ZHIJIAN CHEN 1', 'YIN XU 1', 'FEITENG LI 1'] | 2019-06-10 | null | null | null | ieeexplore-2019-6 | ['heartbeat-classification'] | ['medical'] | [-1.15053624e-01 -3.63813728e-01 1.49443507e-01 -2.25901380e-01
-4.31421578e-01 -3.39485705e-01 -2.34331384e-01 1.91983938e-01
-2.82570660e-01 6.81588829e-01 -9.72338170e-02 -1.70107409e-02
-1.98499262e-01 -7.13513434e-01 4.70282137e-02 -6.79352820e-01
-3.37191731e-01 -1.41056567e-01 -1.66931406e-01 8.32439438... | [14.285323143005371, 3.2646384239196777] |
f988f80f-db0a-4ae4-b9ee-071be8ec057a | weakly-supervised-video-anomaly-detection-1 | 2212.08506 | null | https://arxiv.org/abs/2212.08506v1 | https://arxiv.org/pdf/2212.08506v1.pdf | Weakly Supervised Video Anomaly Detection Based on Cross-Batch Clustering Guidance | Weakly supervised video anomaly detection (WSVAD) is a challenging task since only video-level labels are available for training. In previous studies, the discriminative power of the learned features is not strong enough, and the data imbalance resulting from the mini-batch training strategy is ignored. To address thes... | ['Yanning Zhang', 'Peng Wang', 'Shizhou Zhang', 'Xin Zhang', 'Congqi Cao'] | 2022-12-16 | null | null | null | null | ['video-anomaly-detection'] | ['computer-vision'] | [-1.08709149e-01 -3.44600141e-01 -2.98007429e-01 -6.71650410e-01
-5.57453275e-01 -1.93555042e-01 2.18164340e-01 2.78590083e-01
-2.58655965e-01 2.70977527e-01 2.17578515e-01 -5.88499643e-02
-5.42836674e-02 -5.13325095e-01 -6.62758529e-01 -7.43957698e-01
-1.72487691e-01 -1.15091816e-01 4.97105062e-01 2.08512858... | [7.828383445739746, 1.6434059143066406] |
503717e7-e425-419d-93a7-a2457e13ee09 | combating-covid-19-using-generative | 2205.07236 | null | https://arxiv.org/abs/2205.07236v1 | https://arxiv.org/pdf/2205.07236v1.pdf | Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review | This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes the different GANs methods and the lungs images datasets for COVID-19. It attempts to answer the questions related to applications of GANs, ... | ['Zubair Shah', 'Hazrat Ali'] | 2022-05-15 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 2.36403152e-01 3.99857253e-01 -2.28224456e-01 -7.80258775e-02
-7.77773440e-01 -2.90706784e-01 2.27384523e-01 -5.26999652e-01
-2.50985742e-01 8.20226192e-01 4.38330323e-01 -2.28813812e-01
1.29756974e-02 -8.05541813e-01 -3.79827619e-01 -1.06166399e+00
3.80737811e-01 7.42099881e-01 3.67637128e-02 1.09708987... | [14.705095291137695, -1.9458351135253906] |
8c1ff591-7db5-48d2-b113-f70e6b143beb | from-phonemes-to-images-levels-of | 1610.03342 | null | http://arxiv.org/abs/1610.03342v1 | http://arxiv.org/pdf/1610.03342v1.pdf | From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning | We present a model of visually-grounded language learning based on stacked
gated recurrent neural networks which learns to predict visual features given
an image description in the form of a sequence of phonemes. The learning task
resembles that faced by human language learners who need to discover both
structure and m... | ['Grzegorz Chrupała', 'Lieke Gelderloos'] | 2016-10-11 | from-phonemes-to-images-levels-of-1 | https://aclanthology.org/C16-1124 | https://aclanthology.org/C16-1124.pdf | coling-2016-12 | ['grounded-language-learning'] | ['natural-language-processing'] | [ 5.59346199e-01 1.47670224e-01 -5.52075617e-02 -7.86448658e-01
-9.02425766e-01 -7.61964262e-01 7.45572388e-01 1.31614313e-01
-4.25700098e-01 5.06129682e-01 7.59419858e-01 -3.30732405e-01
2.85445333e-01 -7.14063346e-01 -9.43893254e-01 -7.06863105e-01
-8.53860304e-02 2.28975996e-01 -7.01770931e-02 -2.61697680... | [10.413030624389648, 1.797534465789795] |
4f6780ff-515f-4997-94f8-cfb4b2e18a32 | modeling-content-emotion-duality-via | 2209.12495 | null | https://arxiv.org/abs/2209.12495v1 | https://arxiv.org/pdf/2209.12495v1.pdf | Modeling Content-Emotion Duality via Disentanglement for Empathetic Conversation | The task of empathetic response generation aims to understand what feelings a speaker expresses on his/her experiences and then reply to the speaker appropriately. To solve the task, it is essential to model the content-emotion duality of a dialogue, which is composed of the content view (i.e., what personal experience... | ['Wenjie Li', 'Hinrich Schütze', 'Jiashuo Wang', 'Peiqin Lin'] | 2022-09-26 | null | null | null | null | ['empathetic-response-generation'] | ['natural-language-processing'] | [-2.47762039e-01 3.68477821e-01 -8.49337783e-03 -6.45962059e-01
-3.72172207e-01 -3.96700054e-01 9.22499418e-01 -2.62062415e-03
2.27594879e-02 7.50554144e-01 1.29656208e+00 4.47063565e-01
2.83199757e-01 -8.59960556e-01 2.74491072e-01 -7.48174906e-01
5.46520591e-01 4.47924674e-01 -7.49407291e-01 -8.21740270... | [13.183658599853516, 7.596688747406006] |
672c7963-511d-4d9c-a276-453c6898ccf7 | transliteration-of-foreign-words-in-burmese | 2110.03163 | null | https://arxiv.org/abs/2110.03163v2 | https://arxiv.org/pdf/2110.03163v2.pdf | Transliteration of Foreign Words in Burmese | This manuscript provides general descriptions on transliteration of foreign words in the Burmese language. Phenomena caused by phonetic and orthographic issues are discussed. Based on this work, we expect to gradually establish prescriptive guidelines to normalize the transliteration on modern words in Burmese. | ['Chenchen Ding'] | 2021-10-07 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [-3.29812676e-01 -3.55488211e-01 -1.80632904e-01 -3.44977647e-01
-3.16398501e-01 -5.81656039e-01 5.42673826e-01 1.32810488e-01
-7.52271116e-01 9.47329938e-01 6.68658197e-01 -9.58815515e-01
8.29808563e-02 -3.75268549e-01 -4.64174747e-01 -3.08767945e-01
4.49659377e-01 3.31788391e-01 -1.04123829e-02 -8.23015928... | [11.130743980407715, 10.314467430114746] |
3f5c07c4-d59a-40d4-902c-34a1d3337b55 | temporal-word-analogies-identifying-lexical | null | null | https://aclanthology.org/P17-2071 | https://aclanthology.org/P17-2071.pdf | Temporal Word Analogies: Identifying Lexical Replacement with Diachronic Word Embeddings | This paper introduces the concept of temporal word analogies: pairs of words which occupy the same semantic space at different points in time. One well-known property of word embeddings is that they are able to effectively model traditional word analogies ({``}word $w_1$ is to word $w_2$ as word $w_3$ is to word $w_4${... | ['Terrence Szymanski'] | 2017-07-01 | null | null | null | acl-2017-7 | ['diachronic-word-embeddings'] | ['natural-language-processing'] | [-2.45073423e-01 -2.79839545e-01 -3.36916327e-01 -2.33355761e-01
-4.74806249e-01 -6.88803971e-01 1.05549407e+00 7.93851614e-01
-1.05247247e+00 5.03169656e-01 4.58731085e-01 -7.67709851e-01
-6.41379416e-01 -9.34475243e-01 -4.39716399e-01 -2.21593633e-01
-4.80185807e-01 3.44121188e-01 6.17064759e-02 -7.49318898... | [10.232377052307129, 8.880875587463379] |
26d85902-5002-4d21-8876-2dca628ff571 | attributed-network-embedding-model-for | 2209.09448 | null | https://arxiv.org/abs/2209.09448v2 | https://arxiv.org/pdf/2209.09448v2.pdf | Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes | The spread of COVID-19 revealed that transmission risk patterns are not homogenous across different cities and communities, and various heterogeneous features can influence the spread trajectories. Hence, for predictive pandemic monitoring, it is essential to explore latent heterogeneous features in cities and communit... | ['Ali Mostafavi', 'Chao Fan', 'Qingchun Li', 'Bo Li', 'Junwei Ma'] | 2022-09-20 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-4.23519254e-01 4.70357761e-03 -3.01559925e-01 2.83329841e-03
-8.68565217e-02 -5.62706590e-01 1.06228817e+00 7.68932700e-01
-1.85874701e-01 3.14837515e-01 1.01656353e+00 -5.54547310e-01
-5.70635259e-01 -1.42187059e+00 -3.40401791e-02 -6.15369678e-01
-9.70063865e-01 5.39229035e-01 -1.86716184e-01 -5.55578947... | [6.588459491729736, 2.3084113597869873] |
99883a6d-4beb-4a2b-b947-db848e65ceb0 | a-robust-completed-local-binary-pattern-rclbp | 2112.04021 | null | https://arxiv.org/abs/2112.04021v1 | https://arxiv.org/pdf/2112.04021v1.pdf | A Robust Completed Local Binary Pattern (RCLBP) for Surface Defect Detection | In this paper, we present a Robust Completed Local Binary Pattern (RCLBP) framework for a surface defect detection task. Our approach uses a combination of Non-Local (NL) means filter with wavelet thresholding and Completed Local Binary Pattern (CLBP) to extract robust features which are fed into classifiers for surfac... | ['Daniel Opoku', 'Abdollah Homaifar', 'Shamila Nateghi', 'Mahmoud Nabil Mahmoud', 'Abenezer Girma', 'Nana Kankam Gyimah'] | 2021-12-07 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 5.25019228e-01 -6.30297124e-01 4.88061160e-01 -2.70810425e-01
-8.71046484e-01 9.11755860e-02 2.35506132e-01 3.80873770e-01
-2.39544630e-01 2.90818840e-01 -9.65611115e-02 1.28926471e-01
-3.59316409e-01 -1.01263142e+00 -2.32778922e-01 -1.12156308e+00
1.90127507e-01 -1.79434329e-01 9.21820462e-01 -2.59653360... | [7.497071266174316, 1.6320879459381104] |
4241b691-cf84-46aa-b8eb-d7ca8b5a9c8c | learning-how-to-robustly-estimate-camera-pose | 2304.08023 | null | https://arxiv.org/abs/2304.08023v1 | https://arxiv.org/pdf/2304.08023v1.pdf | Learning How To Robustly Estimate Camera Pose in Endoscopic Videos | Purpose: Surgical scene understanding plays a critical role in the technology stack of tomorrow's intervention-assisting systems in endoscopic surgeries. For this, tracking the endoscope pose is a key component, but remains challenging due to illumination conditions, deforming tissues and the breathing motion of organs... | ['Raphael Sznitman', 'Maximilian Allan', 'Thomas Kurmann', 'Daniel Candinas', 'Mathias Gallardo', 'Christopher Hahne', 'Michel Hayoz'] | 2023-04-17 | null | null | null | null | ['simultaneous-localization-and-mapping', '3d-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 1.58318996e-01 3.41148257e-01 -4.04159911e-02 2.57272199e-02
-8.10439348e-01 -1.06755900e+00 6.54836446e-02 3.77486722e-04
-6.07041180e-01 2.58722067e-01 3.18036109e-01 -3.48989397e-01
-1.97667792e-01 -1.91629410e-01 -9.17231023e-01 -7.71744251e-01
1.61811598e-02 8.45963880e-02 7.73201734e-02 -2.00663283... | [13.908585548400879, -3.1758763790130615] |
d2a40c27-8249-4c83-a20a-99e454f54a9c | automatic-generation-of-realistic-training | null | null | https://elib.dlr.de/147156/ | https://elib.dlr.de/147156/1/Final%20version%20V2opt.pdf | Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images | This paper proposes a novel approach to automat-
ically generate labeled training data for predicting parallel-jaw
grasps from stereo-matched depth images. We generate realistic
depth images using Semi-Global Matching to compute disparity
maps from synthetic data, which allows producing images that
mimic the typic... | ['Màximo A. Roa', 'Jochen Steil', 'Michael Suppa', 'Elena Gambaro', 'Carlos X. Garcia', 'Justus Drögemüller'] | 2021-12-07 | null | null | null | ieee-int-conf-advanced-robotics-2021-12 | ['grasp-generation', 'stereo-matching-1', 'robotic-grasping', 'grasp-rectangle-generation'] | ['computer-vision', 'computer-vision', 'robots', 'robots'] | [ 4.77595210e-01 3.46210569e-01 4.28948790e-01 -4.36644614e-01
-6.74831271e-01 -5.69333315e-01 1.94553092e-01 1.96064591e-01
-1.18770845e-01 4.93322760e-01 1.95132568e-02 1.19376339e-01
2.07440138e-01 -9.72874582e-01 -1.27615941e+00 -4.51582640e-01
-1.98498309e-01 9.31391597e-01 6.66485488e-01 -1.08468369... | [5.920238494873047, -0.9474340677261353] |
b180cdf6-f9de-4518-be72-39c57a8c11a2 | methodology-for-jointly-assessing-myocardial | 2306.15281 | null | https://arxiv.org/abs/2306.15281v1 | https://arxiv.org/pdf/2306.15281v1.pdf | Methodology for Jointly Assessing Myocardial Infarct Extent and Regional Contraction in 3-D CMRI | Automated extraction of quantitative parameters from Cardiac Magnetic Resonance Images (CMRI) is crucial for the management of patients with myocardial infarct. This work proposes a post-processing procedure to jointly analyze Cine and Delayed-Enhanced (DE) acquisitions in order to provide an automatic quantification o... | ['F. Frouin', 'E. Mousseaux', 'E. Roullot', 'M. Lefort', 'R. El Berbari', 'C. Constantinides', 'C. Pellot-Barakat', 'Y. Chenoune'] | 2023-06-27 | null | null | null | null | ['management'] | ['miscellaneous'] | [ 2.86093295e-01 -1.96400687e-01 1.58256233e-01 -1.21881917e-01
-4.46768582e-01 -7.15113640e-01 1.96602643e-01 3.69096726e-01
-7.29169548e-01 4.65778917e-01 7.45198578e-02 -2.99009115e-01
-2.73576379e-01 -5.42196989e-01 1.18603587e-01 -7.68630922e-01
-5.86246431e-01 7.01289058e-01 4.31158155e-01 2.30871409... | [14.110881805419922, -2.462512254714966] |
7b984b38-23f8-4820-b394-80453b5d5ac8 | pash-at-trec-2021-deep-learning-track | 2205.11245 | null | https://arxiv.org/abs/2205.11245v3 | https://arxiv.org/pdf/2205.11245v3.pdf | PASH at TREC 2021 Deep Learning Track: Generative Enhanced Model for Multi-stage Ranking | This paper describes the PASH participation in TREC 2021 Deep Learning Track. In the recall stage, we adopt a scheme combining sparse and dense retrieval method. In the multi-stage ranking phase, point-wise and pair-wise ranking strategies are used one after another based on model continual pre-trained on general knowl... | ['Wenfeng Xie', 'Xin Tang', 'Jun Wang', 'Guotong Xie', 'Peng Gao', 'Rui Fang', 'Xianbin Ye', 'Tuozhen Liu', 'Yongquan Lai', 'Hao Chen', 'Yixuan Qiao'] | 2022-05-18 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [-3.89891714e-01 -3.67842257e-01 3.61393914e-02 -4.84089047e-01
-1.70312381e+00 -6.57447815e-01 1.24902391e+00 3.54313612e-01
-9.33806300e-01 8.11901033e-01 7.27286994e-01 2.43825931e-02
-7.12880790e-01 -6.69354200e-01 -5.66847563e-01 -3.58485937e-01
-1.31829619e-01 1.14548695e+00 3.20522815e-01 -5.75228214... | [11.496105194091797, 7.6093525886535645] |
6102ac67-f41f-4ff4-9611-f15104e7e147 | deconvolutional-paragraph-representation | 1708.04729 | null | http://arxiv.org/abs/1708.04729v3 | http://arxiv.org/pdf/1708.04729v3.pdf | Deconvolutional Paragraph Representation Learning | Learning latent representations from long text sequences is an important
first step in many natural language processing applications. Recurrent Neural
Networks (RNNs) have become a cornerstone for this challenging task. However,
the quality of sentences during RNN-based decoding (reconstruction) decreases
with the leng... | ['Lawrence Carin', 'Yizhe Zhang', 'Zhe Gan', 'Ricardo Henao', 'Guoyin Wang', 'Dinghan Shen'] | 2017-08-16 | deconvolutional-paragraph-representation-1 | http://papers.nips.cc/paper/7005-deconvolutional-paragraph-representation-learning | http://papers.nips.cc/paper/7005-deconvolutional-paragraph-representation-learning.pdf | neurips-2017-12 | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [ 3.92557323e-01 5.27051091e-02 -2.36251980e-01 -3.19920689e-01
-9.31153476e-01 -5.66811383e-01 6.99999213e-01 4.64542508e-02
-4.42486584e-01 8.83275688e-01 9.41666603e-01 -4.54795927e-01
3.27964932e-01 -5.66075265e-01 -7.06298888e-01 -7.00987399e-01
3.88731539e-01 4.65780914e-01 -2.73878038e-01 -1.73344612... | [12.095929145812988, 9.210652351379395] |
98568a5d-a361-44fa-81eb-512885c51708 | transformer-based-deep-image-matching-for | 2105.14432 | null | https://arxiv.org/abs/2105.14432v2 | https://arxiv.org/pdf/2105.14432v2.pdf | TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification | Transformers have recently gained increasing attention in computer vision. However, existing studies mostly use Transformers for feature representation learning, e.g. for image classification and dense predictions, and the generalizability of Transformers is unknown. In this work, we further investigate the possibility... | ['Ling Shao', 'Shengcai Liao'] | 2021-05-30 | transmatcher-deep-image-matching-through | http://proceedings.neurips.cc/paper/2021/hash/0f49c89d1e7298bb9930789c8ed59d48-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/0f49c89d1e7298bb9930789c8ed59d48-Paper.pdf | neurips-2021-12 | ['generalizable-person-re-identification'] | ['computer-vision'] | [ 1.57181293e-01 -2.87169456e-01 -2.30289325e-02 -4.10700262e-01
-7.53493309e-01 -3.36393476e-01 6.60919487e-01 1.62453264e-01
-6.09920621e-01 3.09602559e-01 4.04397361e-02 -2.01012671e-01
5.96195161e-02 -8.52444589e-01 -7.71924973e-01 -5.76019824e-01
4.83875155e-01 1.94798797e-01 1.85555339e-01 1.77900381... | [14.733733177185059, 0.8423817157745361] |
88786650-56f2-4801-a0a5-7bff7b321ae9 | gradient-based-geometry-learning-for-fan-beam | 2212.02177 | null | https://arxiv.org/abs/2212.02177v1 | https://arxiv.org/pdf/2212.02177v1.pdf | Gradient-Based Geometry Learning for Fan-Beam CT Reconstruction | Incorporating computed tomography (CT) reconstruction operators into differentiable pipelines has proven beneficial in many applications. Such approaches usually focus on the projection data and keep the acquisition geometry fixed. However, precise knowledge of the acquisition geometry is essential for high quality rec... | ['Andreas Maier', 'Michael Manhart', 'Felix Denzinger', 'Jonas Utz', 'Mingxuan Gu', 'Laura Pfaff', 'Linda-Sophie Schneider', 'Maximilian Rohleder', 'Manuela Meier', 'Lukas Folle', 'Noah Maul', 'Fabian Wagner', 'Mareike Thies'] | 2022-12-05 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 3.46372545e-01 2.90698409e-01 2.05146566e-01 -5.31846702e-01
-8.52814019e-01 -2.18954280e-01 3.46046448e-01 -1.51682764e-01
-6.72250032e-01 3.79954100e-01 9.39391181e-02 -2.56499708e-01
-2.56305993e-01 -6.03471279e-01 -7.84945667e-01 -8.93332899e-01
-4.58460748e-02 5.55034876e-01 2.13550746e-01 -4.01451476... | [13.485292434692383, -2.666454315185547] |
96166548-c2b6-4486-8215-dd676a2c6192 | incorporating-commonsense-knowledge-into | 2010.10044 | null | https://arxiv.org/abs/2010.10044v1 | https://arxiv.org/pdf/2010.10044v1.pdf | Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks | Abstractive dialogue summarization is the task of capturing the highlights of a dialogue and rewriting them into a concise version. In this paper, we present a novel multi-speaker dialogue summarizer to demonstrate how large-scale commonsense knowledge can facilitate dialogue understanding and summary generation. In de... | ['Ting Liu', 'Bing Qin', 'Xiaocheng Feng', 'Xiachong Feng'] | 2020-10-20 | null | https://aclanthology.org/2021.ccl-1.86 | https://aclanthology.org/2021.ccl-1.86.pdf | ccl-2021-8 | ['dialogue-understanding'] | ['natural-language-processing'] | [ 1.66557267e-01 9.32894289e-01 -2.28906676e-01 -4.01613832e-01
-7.35168159e-01 -5.28130472e-01 9.53785181e-01 2.39340544e-01
7.33250752e-02 1.16050076e+00 1.39000249e+00 -1.20209329e-01
3.67783576e-01 -7.10908234e-01 -2.06172243e-01 2.88534351e-02
2.52076834e-01 5.54755092e-01 4.17229444e-01 -1.07923508... | [12.65335750579834, 8.60495662689209] |
b171fab3-68a4-40bd-8e5f-015ed1f5c7cc | unsupervised-flow-aligned-sequence-to | 2205.10195 | null | https://arxiv.org/abs/2205.10195v2 | https://arxiv.org/pdf/2205.10195v2.pdf | Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration | How to properly model the inter-frame relation within the video sequence is an important but unsolved challenge for video restoration (VR). In this work, we propose an unsupervised flow-aligned sequence-to-sequence model (S2SVR) to address this problem. On the one hand, the sequence-to-sequence model, which has proven ... | ['Luc van Gool', 'Yulun Zhang', 'Xueyi Zou', 'Youliang Yan', 'Haoqian Wang', 'Yuanhao Cai', 'Xiaowan Hu', 'Jing Lin'] | 2022-05-20 | null | null | null | null | ['video-super-resolution', 'video-enhancement', 'video-restoration'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.84036809e-01 -3.54654282e-01 -2.12572813e-01 -1.63581640e-01
-5.55950463e-01 -4.41926658e-01 3.52009118e-01 -5.53985834e-01
-2.18452543e-01 7.89607048e-01 6.05938256e-01 -1.75706238e-01
4.30378579e-02 -2.93860614e-01 -6.32791340e-01 -6.14423096e-01
6.85603023e-02 -3.07443976e-01 2.24391103e-01 -1.47297367... | [11.029869079589844, -1.8439075946807861] |
341c6844-e8cb-4093-b651-0e3baa3e5c01 | fingerspelling-recognition-in-the-wild-with | 1908.10546 | null | https://arxiv.org/abs/1908.10546v1 | https://arxiv.org/pdf/1908.10546v1.pdf | Fingerspelling recognition in the wild with iterative visual attention | Sign language recognition is a challenging gesture sequence recognition problem, characterized by quick and highly coarticulated motion. In this paper we focus on recognition of fingerspelling sequences in American Sign Language (ASL) videos collected in the wild, mainly from YouTube and Deaf social media. Most previou... | ['Karen Livescu', 'Greg Shakhnarovich', 'Diane Brentari', 'Jonathan Keane', 'Bowen Shi', 'Aurora Martinez Del Rio'] | 2019-08-28 | fingerspelling-recognition-in-the-wild-with-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Shi_Fingerspelling_Recognition_in_the_Wild_With_Iterative_Visual_Attention_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Shi_Fingerspelling_Recognition_in_the_Wild_With_Iterative_Visual_Attention_ICCV_2019_paper.pdf | iccv-2019-10 | ['hand-detection'] | ['computer-vision'] | [ 3.13301802e-01 -3.63144636e-01 -2.71832436e-01 -2.50345349e-01
-9.24183369e-01 -8.42905343e-01 6.01502776e-01 -7.82824874e-01
-1.04950166e+00 3.40559691e-01 6.84399724e-01 3.95663194e-02
2.16292202e-01 6.03055209e-02 -4.68226582e-01 -5.13963699e-01
6.59761503e-02 5.01667917e-01 9.25947607e-01 -1.48775056... | [9.136438369750977, -6.463534832000732] |
94c246a1-063c-45e5-b68c-725ab7f95885 | ensemble-of-multi-view-learning-classifiers | 1811.10068 | null | http://arxiv.org/abs/1811.10068v1 | http://arxiv.org/pdf/1811.10068v1.pdf | Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection | The adoption of large-scale iris recognition systems around the world has
brought to light the importance of detecting presentation attack images
(textured contact lenses and printouts). This work presents a new approach in
iris Presentation Attack Detection (PAD), by exploring combinations of
Convolutional Neural Netw... | ['Adam Czajka', 'Kevin Bowyer', 'Andrey Kuehlkamp', 'Anderson Rocha', 'Allan Pinto'] | 2018-11-25 | null | null | null | null | ['cross-domain-iris-presentation-attack'] | ['computer-vision'] | [ 5.48278868e-01 -1.32157892e-01 -2.16250539e-01 -2.34580502e-01
-6.61503494e-01 -5.85065663e-01 9.06488299e-01 2.03046069e-01
-3.28187793e-01 1.62071973e-01 5.35904109e-01 -4.63214040e-01
-5.57137072e-01 -4.06250000e-01 -4.65316385e-01 -4.46221888e-01
-2.47615278e-01 9.78335962e-02 -5.03701493e-02 9.65492427... | [3.7397189140319824, -3.634243965148926] |
47f35413-ec9a-4748-a944-65e907eac64c | reinforced-swin-convs-transformer-for | 2205.00434 | null | https://arxiv.org/abs/2205.00434v1 | https://arxiv.org/pdf/2205.00434v1.pdf | Reinforced Swin-Convs Transformer for Underwater Image Enhancement | Underwater Image Enhancement (UIE) technology aims to tackle the challenge of restoring the degraded underwater images due to light absorption and scattering. To address problems, a novel U-Net based Reinforced Swin-Convs Transformer for the Underwater Image Enhancement method (URSCT-UIE) is proposed. Specifically, wit... | ['Ting Luo', 'Mei Yu', 'Gangyi Jiang', 'Haiyong Xu', 'Tingdi Ren'] | 2022-05-01 | null | null | null | null | ['uie'] | ['computer-vision'] | [ 2.00035021e-01 2.01447904e-02 9.95707572e-01 -1.71866491e-01
-3.21845412e-01 6.85473382e-02 9.80660766e-02 -1.63703978e-01
-7.48298764e-01 5.26442826e-01 4.12752062e-01 -1.22489780e-01
-1.91941500e-01 -9.04245436e-01 -8.03230703e-01 -1.03939557e+00
-2.40041941e-01 -8.11890543e-01 3.70810360e-01 -6.44779980... | [10.699969291687012, -3.5168166160583496] |
25538fd9-9206-4d7a-bcd2-9938c3cb5f82 | towards-confidence-guided-shape-completion | 2209.043 | null | https://arxiv.org/abs/2209.04300v1 | https://arxiv.org/pdf/2209.04300v1.pdf | Towards Confidence-guided Shape Completion for Robotic Applications | Many robotic tasks involving some form of 3D visual perception greatly benefit from a complete knowledge of the working environment. However, robots often have to tackle unstructured environments and their onboard visual sensors can only provide incomplete information due to limited workspaces, clutter or object self-o... | ['Lorenzo Natale', 'Michele Colledanchise', 'Fabrizio Bottarel', 'Stefano Berti', 'Andrea Rosasco'] | 2022-09-09 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [ 5.77456094e-02 1.87060237e-01 1.67086825e-01 -1.96939409e-01
-3.57399315e-01 -6.51475549e-01 4.81055886e-01 3.88217837e-01
-4.53562170e-01 5.23094356e-01 -3.26546192e-01 -5.53987138e-02
-3.11373264e-01 -8.52894843e-01 -1.07334507e+00 -3.82407039e-01
6.46456853e-02 1.03678060e+00 4.41094577e-01 -1.70126945... | [5.939987659454346, -0.9042958617210388] |
a93369e8-bb54-49fa-b98f-04a50f735095 | guided-source-separation | 2011.04569 | null | https://arxiv.org/abs/2011.04569v4 | https://arxiv.org/pdf/2011.04569v4.pdf | Informed Source Extraction With Application to Acoustic Echo Reduction | Informed speaker extraction aims to extract a target speech signal from a mixture of sources given prior knowledge about the desired speaker. Recent deep learning-based methods leverage a speaker discriminative model that maps a reference snippet uttered by the target speaker into a single embedding vector that encapsu... | ['Wolfgang Mack', 'Emanuël A. P. Habets', 'Mohamed Elminshawi'] | 2020-11-09 | null | null | null | null | ['acoustic-echo-cancellation', 'speaker-separation', 'acoustic-echo-cancellation'] | ['medical', 'speech', 'speech'] | [ 2.70805717e-01 -3.75468992e-02 1.27022579e-01 -3.04232329e-01
-1.12325013e+00 -4.67721611e-01 6.75393522e-01 -2.33081251e-01
-3.87190655e-02 1.47589222e-01 6.72885776e-01 1.57866612e-01
-2.35857293e-02 -2.64475048e-01 -5.20097077e-01 -1.04649174e+00
-1.56631172e-01 -9.15592983e-02 -1.52551726e-01 -1.26421064... | [14.896720886230469, 5.808861255645752] |
5ac589ba-264e-4d31-b59c-d4bae92ab986 | implicit-and-efficient-point-cloud-completion | 2209.00522 | null | https://arxiv.org/abs/2209.00522v2 | https://arxiv.org/pdf/2209.00522v2.pdf | Implicit and Efficient Point Cloud Completion for 3D Single Object Tracking | The point cloud based 3D single object tracking has drawn increasing attention. Although many breakthroughs have been achieved, we also reveal two severe issues. By extensive analysis, we find the prediction manner of current approaches is non-robust, i.e., exposing a misalignment gap between prediction score and actua... | ['Xiaoping Li', 'Hangcheng Yu', 'En Yu', 'Jinrong Yang', 'Shengkai Wu', 'Liangliang Ren', 'Pan Wang'] | 2022-09-01 | null | null | null | null | ['point-cloud-completion', '3d-single-object-tracking'] | ['computer-vision', 'computer-vision'] | [-1.66990831e-02 -2.91883081e-01 -1.79415837e-01 -2.28303552e-01
-9.41339016e-01 -4.34148341e-01 5.34939528e-01 -1.34865835e-01
6.24999814e-02 2.78842598e-01 4.91484404e-02 -5.21807112e-02
-1.64022803e-01 -5.46607971e-01 -7.96026826e-01 -6.27006233e-01
2.08353326e-01 3.32141906e-01 7.61925101e-01 1.04123592... | [6.620326519012451, -2.3441076278686523] |
043f47bd-4060-4299-98c4-050524dac3fe | robot-basics-representation-rotation-and | 2211.02786 | null | https://arxiv.org/abs/2211.02786v2 | https://arxiv.org/pdf/2211.02786v2.pdf | Robot Basics: Representation, Rotation and Velocity | In this article, we plan to provide an introduction about some basics about robots for readers. Several key topics of classic robotics will be introduced, including robot representation, robot rotational motion, coordinates transformation and velocity transformation. By now, classic rigid-body robot analysis is still t... | ['Jiawei Zhang'] | 2022-11-05 | null | null | null | null | ['motion-planning'] | ['robots'] | [-2.09076762e-01 1.06399707e-01 -7.50862360e-01 -3.33423950e-02
3.38601559e-01 -2.78331429e-01 3.47569436e-01 -2.84852147e-01
-3.63346040e-01 5.36902130e-01 -4.22683179e-01 -2.65297830e-01
-3.32496136e-01 -5.83056450e-01 -5.87684035e-01 -7.31624126e-01
-2.95396209e-01 3.43278259e-01 6.18002191e-02 -1.15032089... | [4.891707420349121, 1.2815784215927124] |
47b5baeb-0933-4afa-837b-5306e40c093b | fakeout-leveraging-out-of-domain-self | 2212.00773 | null | https://arxiv.org/abs/2212.00773v1 | https://arxiv.org/pdf/2212.00773v1.pdf | FakeOut: Leveraging Out-of-domain Self-supervision for Multi-modal Video Deepfake Detection | Video synthesis methods rapidly improved in recent years, allowing easy creation of synthetic humans. This poses a problem, especially in the era of social media, as synthetic videos of speaking humans can be used to spread misinformation in a convincing manner. Thus, there is a pressing need for accurate and robust de... | ['Ohad Fried', 'Gil Knafo'] | 2022-12-01 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 5.60719632e-02 -2.12208077e-01 -2.78299372e-03 -6.10438325e-02
-5.72978199e-01 -8.15088987e-01 7.55094051e-01 -2.47524410e-01
-4.08673346e-01 6.00204587e-01 2.19514728e-01 9.48791802e-02
2.49757513e-01 -6.88995600e-01 -1.06236541e+00 -4.03500348e-01
-1.59617007e-01 1.63580567e-01 3.28125656e-01 -4.36930686... | [12.496408462524414, 1.0912461280822754] |
38c204a7-9f31-47a6-963d-566a4bfb13d5 | deep-attention-diffusion-graph-neural | null | null | https://aclanthology.org/2021.emnlp-main.642 | https://aclanthology.org/2021.emnlp-main.642.pdf | Deep Attention Diffusion Graph Neural Networks for Text Classification | Text classification is a fundamental task with broad applications in natural language processing. Recently, graph neural networks (GNNs) have attracted much attention due to their powerful representation ability. However, most existing methods for text classification based on GNNs consider only one-hop neighborhoods an... | ['Xiaoyue Feng', 'Yanchun Liang', 'Fausto Giunchiglia', 'Renchu Guan', 'Yonghao Liu'] | null | null | null | null | emnlp-2021-11 | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-6.86501190e-02 -9.09171700e-02 -3.26032430e-01 -2.18987644e-01
2.24352449e-01 -7.43973162e-03 7.31026292e-01 7.17198849e-01
-3.30560982e-01 3.55376273e-01 4.07112449e-01 -5.52223742e-01
-3.12421501e-01 -1.11870015e+00 -1.43077597e-01 -6.54854476e-01
-9.70361289e-03 2.14372471e-01 3.13953578e-01 -4.78413969... | [9.920279502868652, 6.720036029815674] |
8f28e349-4d91-4609-b853-593337c11507 | gconet-a-stronger-group-collaborative-co | 2205.15469 | null | https://arxiv.org/abs/2205.15469v4 | https://arxiv.org/pdf/2205.15469v4.pdf | GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector | In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in natural scenes. The proposed GCoNet+ achieves the new state-of-the-art performance for co-salient object detection (CoSOD) through mining cons... | ['Chi-Keung Tang', 'Yu-Wing Tai', 'Luc van Gool', 'Jie Qin', 'Qi Fan', 'Deng-Ping Fan', 'Huazhu Fu', 'Peng Zheng'] | 2022-05-30 | null | null | null | null | ['co-saliency-detection'] | ['computer-vision'] | [ 6.98397961e-03 -1.02563702e-01 -1.40964642e-01 -2.87798584e-01
-8.07900310e-01 1.07365549e-02 4.95972991e-01 2.07110733e-01
-2.57268816e-01 1.60590798e-01 1.31946340e-01 -3.03873862e-03
-2.21207052e-01 -4.09514815e-01 -8.08038712e-01 -7.64424026e-01
1.02223083e-03 1.41673505e-01 6.16965473e-01 -1.61024816... | [9.800750732421875, -0.22158242762088776] |
0794cd0d-883e-43a7-831a-56181eebdede | diversity-measurable-anomaly-detection | 2303.05047 | null | https://arxiv.org/abs/2303.05047v1 | https://arxiv.org/pdf/2303.05047v1.pdf | Diversity-Measurable Anomaly Detection | Reconstruction-based anomaly detection models achieve their purpose by suppressing the generalization ability for anomaly. However, diverse normal patterns are consequently not well reconstructed as well. Although some efforts have been made to alleviate this problem by modeling sample diversity, they suffer from short... | ['Xilin Chen', 'Shiguang Shan', 'Bingpeng Ma', 'Hong Chang', 'Wenrui Liu'] | 2023-03-09 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_Diversity-Measurable_Anomaly_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_Diversity-Measurable_Anomaly_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['anomaly-detection-in-surveillance-videos', 'defect-detection', 'anomaly-detection-in-surveillance-videos', 'one-class-classification'] | ['computer-vision', 'computer-vision', 'methodology', 'miscellaneous'] | [ 3.80647361e-01 -7.16891587e-02 2.09398702e-01 -3.72584850e-01
-3.97063971e-01 -2.55650818e-01 5.10390162e-01 1.32252619e-01
1.95065081e-01 1.87960014e-01 3.57520610e-01 1.11042462e-01
-2.16144204e-01 -9.13652599e-01 -6.36229157e-01 -7.75474608e-01
-7.89887831e-02 -1.49226993e-01 1.93600744e-01 -2.49407962... | [7.606215953826904, 2.09877610206604] |
ddefad95-cf11-4ae3-90ac-ec58b337dff8 | multimodal-side-tuning-for-document | 2301.07502 | null | https://arxiv.org/abs/2301.07502v2 | https://arxiv.org/pdf/2301.07502v2.pdf | Multimodal Side-Tuning for Document Classification | In this paper, we propose to exploit the side-tuning framework for multimodal document classification. Side-tuning is a methodology for network adaptation recently introduced to solve some of the problems related to previous approaches. Thanks to this technique it is actually possible to overcome model rigidity and cat... | ['Maurizio Gabbrielli', 'Giuseppe Lisanti', 'Stefano Pio Zingaro'] | 2023-01-16 | null | null | null | null | ['document-image-classification', 'document-classification'] | ['computer-vision', 'natural-language-processing'] | [ 6.94729090e-02 2.10792184e-01 -5.30750863e-02 -3.33146542e-01
-3.46948415e-01 -5.77200711e-01 8.45449150e-01 1.14571907e-01
-7.73941398e-01 7.80379832e-01 -1.25274286e-01 -2.46130839e-01
-3.01696181e-01 -7.06363142e-01 -6.58735454e-01 -6.36423707e-01
1.32686555e-01 7.90308893e-01 4.24655825e-01 -5.40873766... | [9.907890319824219, 2.4577620029449463] |
a27103b8-1ee8-4519-abfc-6e3e1ee043c9 | learning-based-sound-speed-reconstruction-and | 2306.11034 | null | https://arxiv.org/abs/2306.11034v1 | https://arxiv.org/pdf/2306.11034v1.pdf | Learning-based sound speed reconstruction and aberration correction in linear-array photoacoustic/ultrasound imaging | Photoacoustic (PA) image reconstruction involves acoustic inversion that necessitates the specification of the speed of sound (SoS) within the medium of propagation. Due to the lack of information on the spatial distribution of the SoS within heterogeneous soft tissue, a homogeneous SoS distribution (such as 1540 m/s) ... | ['Wenfeng Xia', 'Tom Vercauteren', 'Mengjie Shi'] | 2023-06-19 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 6.16581798e-01 7.02432543e-02 9.09333229e-01 9.11481753e-02
-9.59186018e-01 -2.20698163e-01 2.53377497e-01 2.13716328e-01
-6.05958879e-01 5.01416624e-01 1.19437486e-01 -2.58509129e-01
-4.07150894e-01 -6.21454298e-01 -5.57454586e-01 -1.28111660e+00
-4.84223515e-01 1.98766589e-01 4.64460641e-01 -2.75624264... | [12.703429222106934, -2.583390712738037] |
cc4d22f4-fe1e-4993-9bc2-d826c9208d51 | prnu-based-source-camera-identification-for-1 | 2201.11737 | null | https://arxiv.org/abs/2201.11737v1 | https://arxiv.org/pdf/2201.11737v1.pdf | PRNU Based Source Camera Identification for Webcam and Smartphone Videos | This communication is about an application of image forensics where we use camera sensor fingerprints to identify source camera (SCI: Source Camera Identification) in webcam/smartphone videos. Sensor or camera fingerprints are based on computing the intrinsic noise that is always present in this kind of sensors due to ... | ['Fernando Isasi-de-Vicente', 'Fernando Martín-Rodríguez'] | 2022-01-27 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 6.37640595e-01 -6.09360516e-01 -1.54222846e-01 2.11500645e-01
-4.20727074e-01 -9.82156396e-01 5.73355496e-01 -1.11672208e-01
-3.46991360e-01 5.10813594e-01 -1.79015234e-01 -2.21002579e-01
-2.00586677e-01 -6.30261123e-01 -8.03061128e-01 -6.01899624e-01
3.40449154e-01 -6.38196841e-02 4.32223558e-01 3.61225367... | [12.383108139038086, 0.9682959318161011] |
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