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36b9bd02-2e62-44f9-a705-c95b07df9b72 | deepsportradar-v1-computer-vision-dataset-for | 2208.08190 | null | https://arxiv.org/abs/2208.08190v1 | https://arxiv.org/pdf/2208.08190v1.pdf | DeepSportradar-v1: Computer Vision Dataset for Sports Understanding with High Quality Annotations | With the recent development of Deep Learning applied to Computer Vision, sport video understanding has gained a lot of attention, providing much richer information for both sport consumers and leagues. This paper introduces DeepSportradar-v1, a suite of computer vision tasks, datasets and benchmarks for automated sport... | ['Davide Zambrano', 'Carlo Del Don', 'Maxime Istasse', 'Vladimir Somers', 'Gabriel Van Zandycke'] | 2022-08-17 | null | null | null | null | ['sports-understanding'] | ['miscellaneous'] | [-1.27490550e-01 -4.37812090e-01 -5.19059956e-01 -2.95797527e-01
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3.62817198e-02 4.91352051e-01 6.92347169e-01 -4.29832727... | [7.804211616516113, 0.16752324998378754] |
bb9987e3-7f5f-471e-b4f2-656dc51f46a8 | discogem-a-crowdsourced-corpus-of-genre-mixed | null | null | https://aclanthology.org/2022.lrec-1.351 | https://aclanthology.org/2022.lrec-1.351.pdf | DiscoGeM: A Crowdsourced Corpus of Genre-Mixed Implicit Discourse Relations | We present DiscoGeM, a crowdsourced corpus of 6,505 implicit discourse relations from three genres: political speech, literature, and encyclopedic texts. Each instance was annotated by 10 crowd workers. Various label aggregation methods were explored to evaluate how to obtain a label that best captures the meaning infe... | ['Vera Demberg', 'Frances Yung', 'Tianai Dong', 'Merel Scholman'] | null | null | null | null | lrec-2022-6 | ['relation-classification'] | ['natural-language-processing'] | [ 8.57257172e-02 7.99396813e-01 -5.22247434e-01 -4.09791559e-01
-8.12298656e-01 -1.00034177e+00 1.12519956e+00 7.71762729e-01
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3.17278266e-01 8.70571077e-01 3.85382473e-01 -3.89609337... | [10.701557159423828, 9.317848205566406] |
e16dbd9c-97ed-425a-b4dc-5e91f8569f57 | one-shot-face-reenactment-using-appearance | 2102.03984 | null | https://arxiv.org/abs/2102.03984v3 | https://arxiv.org/pdf/2102.03984v3.pdf | One-shot Face Reenactment Using Appearance Adaptive Normalization | The paper proposes a novel generative adversarial network for one-shot face reenactment, which can animate a single face image to a different pose-and-expression (provided by a driving image) while keeping its original appearance. The core of our network is a novel mechanism called appearance adaptive normalization, wh... | ['Kun Zhou', 'Mengmeng Wang', 'Yong liu', 'Shanqi Liu', 'Shuang Li', 'Tianjia Shao', 'Yi Yuan', 'Guangming Yao'] | 2021-02-08 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 1.31530449e-01 5.60084164e-01 3.43558975e-02 -5.61160743e-01
-3.04987103e-01 -6.17253423e-01 5.21175444e-01 -1.08574843e+00
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3.28835011e-01 1.64653525e-01 -3.63435537e-01 -5.42871654... | [12.669669151306152, -0.1180625930428505] |
b48f083e-ae0c-4b2d-a83a-73365cd09d37 | context-aware-classification-of-legal | 2304.02787 | null | https://arxiv.org/abs/2304.02787v2 | https://arxiv.org/pdf/2304.02787v2.pdf | Context-Aware Classification of Legal Document Pages | For many business applications that require the processing, indexing, and retrieval of professional documents such as legal briefs (in PDF format etc.), it is often essential to classify the pages of any given document into their corresponding types beforehand. Most existing studies in the field of document image class... | ['Dell Zhang', 'Grace E. Lee', 'Martina Forster', 'Pavlos Fragkogiannis'] | 2023-04-05 | null | null | null | null | ['document-image-classification'] | ['computer-vision'] | [ 7.69668698e-01 -1.62141085e-01 -2.80361980e-01 -2.56785721e-01
-1.23980653e+00 -6.61206722e-01 9.59416687e-01 3.84747595e-01
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2.85230249e-01 2.72565782e-01 5.60906947e-01 1.31780908... | [11.66153621673584, 2.778660297393799] |
109b58d5-50d1-48f0-9f72-52c50b7c1573 | temporal-data-meets-llm-explainable-financial | 2306.11025 | null | https://arxiv.org/abs/2306.11025v1 | https://arxiv.org/pdf/2306.11025v1.pdf | Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting | This paper presents a novel study on harnessing Large Language Models' (LLMs) outstanding knowledge and reasoning abilities for explainable financial time series forecasting. The application of machine learning models to financial time series comes with several challenges, including the difficulty in cross-sequence rea... | ['Yanbin Lu', 'Zongyi Liu', 'Shujing Dong', 'Yuan Ling', 'Zheng Chen', 'Xinli Yu'] | 2023-06-19 | null | null | null | null | ['knowledge-graphs'] | ['knowledge-base'] | [-5.47439933e-01 1.32839605e-01 -1.75235763e-01 -3.59367192e-01
-1.10960543e+00 -8.51828396e-01 7.07812667e-01 -5.42028202e-03
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-9.97528136e-02 -9.36695993e-01 -1.03352356e+00 -1.71937302e-01
-3.09993804e-01 5.65823257e-01 -1.41358063e-01 -3.99287075... | [6.756169319152832, 3.2318689823150635] |
c9558ce4-8600-41da-9526-74c375b897ff | deep-cross-modal-projection-learning-for | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Ying_Zhang_Deep_Cross-Modal_Projection_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Ying_Zhang_Deep_Cross-Modal_Projection_ECCV_2018_paper.pdf | Deep Cross-Modal Projection Learning for Image-Text Matching | The key point of image-text matching is how to accurately measure the similarity between visual and textual inputs. Despite the great progress of associating the deep cross-modal embeddings with the bi-directional ranking loss, developing the strategies for mining useful triplets and selecting appropriate margins remai... | ['Huchuan Lu ', 'Ying Zhang'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['nlp-based-person-retrival'] | ['computer-vision'] | [ 2.91366637e-01 -2.91548908e-01 -3.34885478e-01 -4.99465197e-01
-1.01626682e+00 -3.40413749e-01 8.91980648e-01 8.88771787e-02
-4.88449335e-01 2.32324183e-01 3.57757151e-01 5.65519854e-02
-3.69976342e-01 -3.31466407e-01 -5.05032837e-01 -8.92957091e-01
1.87111899e-01 3.68242353e-01 -1.28971487e-01 4.63463008... | [10.934236526489258, 1.2289223670959473] |
a7cde74e-626a-41db-9ef7-f984933e36e5 | logic-lm-empowering-large-language-models | 2305.12295 | null | https://arxiv.org/abs/2305.12295v1 | https://arxiv.org/pdf/2305.12295v1.pdf | Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning | Large Language Models (LLMs) have shown human-like reasoning abilities but still struggle with complex logical problems. This paper introduces a novel framework, Logic-LM, which integrates LLMs with symbolic reasoning to improve logical problem-solving. Our method first utilizes LLMs to translate a natural language pro... | ['William Yang Wang', 'Xinyi Wang', 'Alon Albalak', 'Liangming Pan'] | 2023-05-20 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 2.38374025e-02 7.76105106e-01 -5.18844903e-01 -3.78467560e-01
-8.91135693e-01 -5.43762088e-01 4.59983349e-01 2.35051677e-01
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-2.14524254e-01 -7.92437911e-01 -9.12361383e-01 2.30638683e-01
2.76089013e-01 5.15652955e-01 3.04314673e-01 -3.49067628... | [9.299925804138184, 7.274547100067139] |
da8a6415-588b-4acd-b786-0251dab30192 | deepvqe-real-time-deep-voice-quality | 2306.03177 | null | https://arxiv.org/abs/2306.03177v1 | https://arxiv.org/pdf/2306.03177v1.pdf | DeepVQE: Real Time Deep Voice Quality Enhancement for Joint Acoustic Echo Cancellation, Noise Suppression and Dereverberation | Acoustic echo cancellation (AEC), noise suppression (NS) and dereverberation (DR) are an integral part of modern full-duplex communication systems. As the demand for teleconferencing systems increases, addressing these tasks is required for an effective and efficient online meeting experience. Most prior research propo... | ['Ross Cutler', 'Jegor Guzvin', 'Tanel Parnamaa', 'Ando Saabas', 'Nicolae-Catalin Ristea', 'Evgenii Indenbom'] | 2023-06-05 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 7.35338032e-02 -2.66789287e-01 6.11221492e-01 -3.19433749e-01
-1.04431164e+00 -4.46985841e-01 3.61625463e-01 -4.61730808e-01
-5.25651395e-01 2.37302855e-01 5.64454019e-01 -5.89407027e-01
-1.95048705e-01 -1.05878353e-01 -4.77825195e-01 -4.24241096e-01
-6.53259158e-02 8.23307931e-02 9.21326950e-02 -7.36971736... | [14.993049621582031, 5.97265625] |
2bf45ced-84c7-4481-a9d4-e0d58f09e413 | enhanced-english-universal-dependencies-an | null | null | https://aclanthology.org/L16-1376 | https://aclanthology.org/L16-1376.pdf | Enhanced English Universal Dependencies: An Improved Representation for Natural Language Understanding Tasks | Many shallow natural language understanding tasks use dependency trees to extract relations between content words. However, strict surface-structure dependency trees tend to follow the linguistic structure of sentences too closely and frequently fail to provide direct relations between content words. To mitigate this p... | ['Sebastian Schuster', 'Christopher D. Manning'] | 2016-05-01 | enhanced-english-universal-dependencies-an-1 | https://aclanthology.org/L16-1376 | https://aclanthology.org/L16-1376.pdf | lrec-2016-5 | ['implicit-relations'] | ['natural-language-processing'] | [-5.35813831e-02 5.32851815e-01 -4.44517523e-01 -9.84864235e-01
-5.48214853e-01 -9.28590953e-01 4.13391113e-01 5.17652631e-01
-2.13072956e-01 8.98736179e-01 7.63401926e-01 -6.73301935e-01
-6.17630258e-02 -9.72385228e-01 -1.40387312e-01 -7.34743327e-02
-1.14752293e-01 5.41177809e-01 4.22770590e-01 -6.43701613... | [10.417211532592773, 9.635456085205078] |
47f72463-ba24-441a-8551-6164b2cf0d18 | ffb-a-fair-fairness-benchmark-for-in | 2306.09468 | null | https://arxiv.org/abs/2306.09468v1 | https://arxiv.org/pdf/2306.09468v1.pdf | FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods | This paper introduces the Fair Fairness Benchmark (\textsf{FFB}), a benchmarking framework for in-processing group fairness methods. Ensuring fairness in machine learning is critical for ethical and legal compliance. However, there exist challenges in comparing and developing of fairness methods due to inconsistencies ... | ['Xia Hu', 'Na Zou', 'Han Zhao', 'Qifan Wang', 'Yu Chen', 'Jianfeng Chi', 'Xiaotian Han'] | 2023-06-15 | null | null | null | null | ['benchmarking', 'benchmarking'] | ['miscellaneous', 'robots'] | [-2.94606388e-01 -1.25488117e-01 -4.05916363e-01 -1.06720757e+00
-7.27265716e-01 -5.19536614e-01 5.91067791e-01 3.49144578e-01
-7.48570979e-01 9.17678773e-01 3.18277180e-01 -6.00439310e-01
-1.03322463e-02 -5.24212122e-01 3.59525569e-02 -2.38440976e-01
1.65312648e-01 2.25078523e-01 -3.38823467e-01 -1.07256599... | [8.923243522644043, 5.29857063293457] |
be3f3b8f-f59d-4798-b8d3-d03da6aef614 | investigation-of-the-challenges-of-underwater | 2306.08738 | null | https://arxiv.org/abs/2306.08738v1 | https://arxiv.org/pdf/2306.08738v1.pdf | Investigation of the Challenges of Underwater-Visual-Monocular-SLAM | In this paper, we present a comprehensive investigation of the challenges of Monocular Visual Simultaneous Localization and Mapping (vSLAM) methods for underwater robots. While significant progress has been made in state estimation methods that utilize visual data in the past decade, most evaluations have been limited ... | ['Kevin Köser', 'Mengkun She', 'David Nakath', 'Michele Grimaldi'] | 2023-06-14 | null | null | null | null | ['simultaneous-localization-and-mapping', 'image-enhancement', 'image-restoration'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.61733717e-01 -3.82406056e-01 7.62862921e-01 -3.24692398e-01
-3.85222495e-01 -7.62726903e-01 4.78608072e-01 8.46814141e-02
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-6.86588734e-02 -4.69268680e-01 -7.38146901e-01 -6.78416371e-01
-4.20201451e-01 2.07750142e-01 3.12852740e-01 -3.67482036... | [7.543638706207275, -1.7968518733978271] |
a915ece3-4c0f-44ad-b403-9523f01043c8 | ad-yolo-you-look-only-once-in-training | 2303.15703 | null | https://arxiv.org/abs/2303.15703v3 | https://arxiv.org/pdf/2303.15703v3.pdf | AD-YOLO: You Look Only Once in Training Multiple Sound Event Localization and Detection | Sound event localization and detection (SELD) combines the identification of sound events with the corresponding directions of arrival (DOA). Recently, event-oriented track output formats have been adopted to solve this problem; however, they still have limited generalization toward real-world problems in an unknown po... | ['Sung Won Han', 'WooSeok Shin', 'Hyun Joon Park', 'Jin Sob Kim'] | 2023-03-28 | null | null | null | null | ['sound-event-detection', 'direction-of-arrival-estimation', 'sound-event-localization-and-detection'] | ['audio', 'audio', 'audio'] | [-3.03746134e-01 -7.12218404e-01 2.17693508e-01 -2.81467527e-01
-1.43628693e+00 -4.49509352e-01 1.91188529e-01 6.52153268e-02
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-3.66251379e-01 4.52117957e-02 8.46962273e-01 8.85566771... | [15.20653247833252, 5.28936243057251] |
55b8d54e-0de1-4b99-ab69-bb734e29a417 | do-prosody-transfer-models-transfer-prosody | 2303.04289 | null | https://arxiv.org/abs/2303.04289v1 | https://arxiv.org/pdf/2303.04289v1.pdf | Do Prosody Transfer Models Transfer Prosody? | Some recent models for Text-to-Speech synthesis aim to transfer the prosody of a reference utterance to the generated target synthetic speech. This is done by using a learned embedding of the reference utterance, which is used to condition speech generation. During training, the reference utterance is identical to the ... | ['Simon King', 'Atli Thor Sigurgeirsson'] | 2023-03-07 | null | null | null | null | ['text-to-speech-synthesis', 'speech-synthesis'] | ['speech', 'speech'] | [ 4.94963318e-01 8.58024776e-01 -2.92600214e-01 -5.04449010e-01
-6.00604951e-01 -5.36210537e-01 7.81856954e-01 -9.67220739e-02
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4.94965196e-01 5.36558926e-01 1.91893920e-01 -6.72239065... | [15.024724960327148, 6.72933292388916] |
c760c46f-9a09-4be6-a617-b507fd4449b9 | anomaly-detection-framework-using-rule | 1410.7709 | null | http://arxiv.org/abs/1410.7709v1 | http://arxiv.org/pdf/1410.7709v1.pdf | Anomaly Detection Framework Using Rule Extraction for Efficient Intrusion Detection | Huge datasets in cyber security, such as network traffic logs, can be
analyzed using machine learning and data mining methods. However, the amount of
collected data is increasing, which makes analysis more difficult. Many machine
learning methods have not been designed for big datasets, and consequently are
slow and di... | ['Antti Juvonen', 'Tuomo Sipola'] | 2014-10-28 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [-2.91923881e-01 -3.64613652e-01 -1.38908669e-01 -2.57351846e-01
3.08925271e-01 -4.90220308e-01 2.71045238e-01 4.53223020e-01
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-2.64049262e-01 6.47242010e-01 5.78779459e-01 -1.38542667... | [5.243856906890869, 7.181462287902832] |
0cb422cf-b784-471b-a34e-c1f0683d14f0 | multimedia-generative-script-learning-for | 2208.12306 | null | https://arxiv.org/abs/2208.12306v3 | https://arxiv.org/pdf/2208.12306v3.pdf | Multimedia Generative Script Learning for Task Planning | Goal-oriented generative script learning aims to generate subsequent steps to reach a particular goal, which is an essential task to assist robots or humans in performing stereotypical activities. An important aspect of this process is the ability to capture historical states visually, which provides detailed informati... | ['Heng Ji', 'Girish Chowdhary', 'Julia Hockenmaier', 'Lifu Huang', 'Hou Pong Chan', 'Manling Li', 'Qingyun Wang'] | 2022-08-25 | null | null | null | null | ['multimodal-generation', 'multimedia-generative-script-learning'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.14210808e-01 -2.13973504e-02 2.03799475e-02 -2.95264393e-01
-1.11070919e+00 -5.59585392e-01 1.24894071e+00 -2.92537481e-01
-4.38567042e-01 8.16077650e-01 4.62439358e-01 8.00923631e-02
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4.62195985e-02 5.83018303e-01 2.34612510e-01 -2.33074516... | [10.792472839355469, 0.6282715201377869] |
b6e1767c-7aab-4fba-97fe-b9de704115cc | interpretable-multimodal-misinformation | 2305.05964 | null | https://arxiv.org/abs/2305.05964v1 | https://arxiv.org/pdf/2305.05964v1.pdf | Interpretable Multimodal Misinformation Detection with Logic Reasoning | Multimodal misinformation on online social platforms is becoming a critical concern due to increasing credibility and easier dissemination brought by multimedia content, compared to traditional text-only information. While existing multimodal detection approaches have achieved high performance, the lack of interpretabi... | ['Haoliang Li', 'Wenya Wang', 'Hui Liu'] | 2023-05-10 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [ 7.72734210e-02 5.34719169e-01 -5.50251901e-01 -4.94077772e-01
-7.50840485e-01 -7.02152789e-01 8.43898773e-01 4.26365465e-01
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-8.06907266e-02 2.45485827e-01 -1.76647771e-02 -3.27469677... | [9.675955772399902, 8.023552894592285] |
c0bbf1f2-4297-48d2-88ac-1db037bab2c1 | relational-self-supervised-learning | 2203.08717 | null | https://arxiv.org/abs/2203.08717v1 | https://arxiv.org/pdf/2203.08717v1.pdf | Relational Self-Supervised Learning | Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations. However, most methods mainly focus on the instance level information (\ie, the different augmented images of the same instance should have the same feature... | ['Chang Xu', 'Xiaogang Wang', 'ChangShui Zhang', 'Chen Qian', 'Fei Wang', 'Shan You', 'Mingkai Zheng'] | 2022-03-16 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 1.08887792e-01 6.95387051e-02 -6.01891160e-01 -5.06695807e-01
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-3.87906581e-01 -5.78135848e-01 -8.74417543e-01 -7.85166740e-01
-6.74247444e-02 3.65833305e-02 1.26222894e-01 -1.09066911... | [9.628482818603516, 2.687084913253784] |
329b4098-72f0-4d8a-95a9-a7c839a02fd7 | guided-speech-enhancement-network | 2303.07486 | null | https://arxiv.org/abs/2303.07486v1 | https://arxiv.org/pdf/2303.07486v1.pdf | Guided Speech Enhancement Network | High quality speech capture has been widely studied for both voice communication and human computer interface reasons. To improve the capture performance, we can often find multi-microphone speech enhancement techniques deployed on various devices. Multi-microphone speech enhancement problem is often decomposed into tw... | ['Matthias Grundmann', 'George Sung', 'Yunpeng Li', 'Chehung Lee', 'Jamie Menjay Lin', 'Hakan Erdogan', 'Shao-Fu Shih', 'Yang Yang'] | 2023-03-13 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 2.79918045e-01 -1.50515556e-01 5.01545429e-01 -7.46907443e-02
-1.06057954e+00 -4.10774767e-01 2.40949959e-01 -2.85300791e-01
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3.62409055e-01 -3.85324746e-01 1.29115298e-01 -1.79218456... | [15.000247955322266, 5.865296363830566] |
b9dce45a-5f65-48ec-9842-3fcc0146da37 | the-emerging-trends-of-multi-label-learning | 2011.11197 | null | https://arxiv.org/abs/2011.11197v3 | https://arxiv.org/pdf/2011.11197v3.pdf | The Emerging Trends of Multi-Label Learning | Exabytes of data are generated daily by humans, leading to the growing need for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classification is an active and rapidly growing research area that deals with classification tasks with an extre... | ['Haobo Wang', 'Ivor W. Tsang', 'Xiaobo Shen', 'Weiwei Liu'] | 2020-11-23 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 1.97485343e-01 -3.30088079e-01 -4.02062178e-01 -7.30975032e-01
-5.81920624e-01 -3.98845792e-01 2.82485336e-01 3.73704016e-01
-3.77020478e-01 4.83533651e-01 -1.41217992e-01 -3.33334580e-02
-1.98788434e-01 -4.78602231e-01 -1.19090326e-01 -7.79315114e-01
2.23933816e-01 5.01163483e-01 -2.79654980e-01 -9.69869271... | [9.614852905273438, 4.281433582305908] |
4ad8e4c1-d8c2-4dba-a888-8f7343995bc8 | uatvr-uncertainty-adaptive-text-video | 2301.06309 | null | https://arxiv.org/abs/2301.06309v1 | https://arxiv.org/pdf/2301.06309v1.pdf | UATVR: Uncertainty-Adaptive Text-Video Retrieval | With the explosive growth of web videos and emerging large-scale vision-language pre-training models, e.g., CLIP, retrieving videos of interest with text instructions has attracted increasing attention. A common practice is to transfer text-video pairs to the same embedding space and craft cross-modal interactions with... | ['Wanli Ouyang', 'Xiangyang Ji', 'Weiping Wang', 'Fu Li', 'Yuxin Song', 'Min Yang', 'Yu Zhou', 'Chang Liu', 'Wenhao Wu', 'Bo Fang'] | 2023-01-16 | null | null | null | null | ['video-retrieval'] | ['computer-vision'] | [-1.39698476e-01 -3.85575324e-01 -4.65895593e-01 -4.40754503e-01
-1.10621870e+00 -5.21149635e-01 7.20144391e-01 1.22422032e-01
-4.77904290e-01 4.63823825e-01 5.18926144e-01 -2.85496525e-02
-3.25520128e-01 -5.32405853e-01 -1.00697219e+00 -4.17422146e-01
9.95378494e-02 6.78758800e-01 2.66499341e-01 -4.36395966... | [10.334847450256348, 0.9442437887191772] |
68984598-3cf5-474f-9b09-3d392674a249 | on-the-equivalence-between-graph-isomorphism | 1905.12560 | null | https://arxiv.org/abs/1905.12560v2 | https://arxiv.org/pdf/1905.12560v2.pdf | On the equivalence between graph isomorphism testing and function approximation with GNNs | Graph Neural Networks (GNNs) have achieved much success on graph-structured data. In light of this, there have been increasing interests in studying their expressive power. One line of work studies the capability of GNNs to approximate permutation-invariant functions on graphs, and another focuses on the their power as... | ['Lei Chen', 'Joan Bruna', 'Zhengdao Chen', 'Soledad Villar'] | 2019-05-29 | on-the-equivalence-between-graph-isomorphism-1 | http://papers.nips.cc/paper/9718-on-the-equivalence-between-graph-isomorphism-testing-and-function-approximation-with-gnns | http://papers.nips.cc/paper/9718-on-the-equivalence-between-graph-isomorphism-testing-and-function-approximation-with-gnns.pdf | neurips-2019-12 | ['graph-regression'] | ['graphs'] | [ 1.43009081e-01 3.00307781e-01 -2.48844609e-01 -1.01206824e-01
1.51050732e-01 -7.12989748e-01 6.39608622e-01 2.39469796e-01
-6.07423671e-02 4.62938070e-01 6.73909113e-02 -5.88431120e-01
-5.48083842e-01 -1.50612533e+00 -7.25568831e-01 -5.11938214e-01
-6.81368709e-01 6.45671785e-01 2.96976417e-01 -5.39422512... | [6.887083530426025, 6.214320182800293] |
a221e393-e88a-47d1-95ef-e9f1d955be45 | the-tiny-time-series-transformer-low-latency | 2303.08951 | null | https://arxiv.org/abs/2303.08951v1 | https://arxiv.org/pdf/2303.08951v1.pdf | The Tiny Time-series Transformer: Low-latency High-throughput Classification of Astronomical Transients using Deep Model Compression | A new golden age in astronomy is upon us, dominated by data. Large astronomical surveys are broadcasting unprecedented rates of information, demanding machine learning as a critical component in modern scientific pipelines to handle the deluge of data. The upcoming Legacy Survey of Space and Time (LSST) of the Vera C. ... | ['Jason D. McEwen', 'Julien Peloton', 'Tarek Allam Jr.'] | 2023-03-15 | null | null | null | null | ['model-compression', 'astronomy'] | ['methodology', 'miscellaneous'] | [-1.32548213e-01 -5.33483803e-01 1.82011917e-01 -3.79748017e-01
-7.65068114e-01 -7.18480825e-01 7.84548044e-01 3.69529098e-01
-6.17294908e-01 4.07139778e-01 -2.95324385e-01 -9.84650135e-01
-6.16095841e-01 -1.03372848e+00 -5.50226450e-01 -6.31867290e-01
-3.58732700e-01 8.52943003e-01 4.70543474e-01 -9.54526290... | [8.099111557006836, 3.155766725540161] |
1ee692d1-fc43-430e-8d0d-547dcc28422a | recent-advances-and-applications-of-machine | 2303.07647 | null | https://arxiv.org/abs/2303.07647v3 | https://arxiv.org/pdf/2303.07647v3.pdf | Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review | For many decades, experimental solid mechanics has played a crucial role in characterizing and understanding the mechanical properties of natural and novel materials. Recent advances in machine learning (ML) provide new opportunities for the field, including experimental design, data analysis, uncertainty quantificatio... | ['Horacio D. Espinosa', 'Enrui Zhang', 'Hanxun Jin'] | 2023-03-14 | null | null | null | null | ['experimental-design'] | ['methodology'] | [ 8.11802596e-02 -3.64254922e-01 -1.79395467e-01 2.63706576e-02
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-5.94292581e-01 -7.54860342e-01 -8.27737212e-01 -9.22021687e-01
-3.67085040e-01 8.46992254e-01 2.73361862e-01 6.18463121... | [6.362446308135986, 3.4275434017181396] |
b23b7d73-a906-4209-80ae-cb25db86bedb | reinforcement-learning-enhanced-shared | 2206.08088 | null | https://arxiv.org/abs/2206.08088v1 | https://arxiv.org/pdf/2206.08088v1.pdf | Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation | Shared-account Cross-domain Sequential Recommendation (SCSR) is an emerging yet challenging task that simultaneously considers the shared-account and cross-domain characteristics in the sequential recommendation. Existing works on SCSR are mainly based on Recurrent Neural Network (RNN) and Graph Neural Network (GNN) bu... | ['Hongzhi Yin', 'Xinhua Wang', 'Tong Chen', 'Jinyu Zhang', 'Lei Guo'] | 2022-06-16 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 2.65895445e-02 -1.82588056e-01 -6.73534751e-01 -1.40158638e-01
-2.77326167e-01 -5.05608380e-01 4.17921364e-01 -1.61853015e-01
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-2.36863464e-01 -7.76857138e-01 -4.57841218e-01 -4.03887749e-01
2.10947052e-01 5.57510197e-01 2.71150380e-01 -4.40784037... | [10.169344902038574, 5.5833845138549805] |
ed508051-b0e7-4b95-8bb6-e63219927b7a | graph-meets-llm-a-novel-approach-to | 2305.14449 | null | https://arxiv.org/abs/2305.14449v3 | https://arxiv.org/pdf/2305.14449v3.pdf | Graph Meets LLM: A Novel Approach to Collaborative Filtering for Robust Conversational Understanding | Conversational AI systems such as Alexa need to understand defective queries to ensure robust conversational understanding and reduce user friction. These defective queries often arise from user ambiguities, mistakes, or errors in automatic speech recognition (ASR) and natural language understanding (NLU). Personalized... | ['Aram Galstyan', 'Yanbin Lu', 'Xiaojiang Huang', 'Xing Fan', 'Eunah Cho', 'Fan Yang', 'Ziyan Jiang', 'Zheng Chen'] | 2023-05-23 | null | null | null | null | ['link-prediction', 'collaborative-filtering', 'automatic-speech-recognition'] | ['graphs', 'miscellaneous', 'speech'] | [ 3.90003920e-01 3.44117135e-01 -8.80050659e-02 -2.98011750e-01
-1.14913678e+00 -7.65097857e-01 6.72177911e-01 2.09912986e-01
-3.19631159e-01 3.97461325e-01 6.87536418e-01 -2.60162324e-01
-3.31951499e-01 -6.02750003e-01 -5.83479643e-01 1.98797807e-01
-4.05333452e-02 7.85549760e-01 2.76273638e-01 -9.47672129... | [12.071883201599121, 7.778079986572266] |
2af2c270-5427-4786-ad78-fe87c9c85ade | learning-graph-structure-for-multi-label | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Tan_Learning_Graph_Structure_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Tan_Learning_Graph_Structure_2015_CVPR_paper.pdf | Learning Graph Structure for Multi-Label Image Classification via Clique Generation | Exploiting label dependency for multi-label image classification can significantly improve classification performance. Probabilistic Graphical Models are one of the primary methods for representing such dependencies. The structure of graphical models, however, is either determined heuristically or learned from very lim... | ['Anton Van Den Hengel', 'Zhen Zhang', 'Fuyuan Hu', 'Chunhua Shen', 'Qinfeng Shi', 'Mingkui Tan', 'Junbin Gao'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['multi-label-image-classification'] | ['computer-vision'] | [ 5.25174141e-01 1.34441108e-01 -7.12293148e-01 -6.54182315e-01
-1.07935524e+00 -6.95171118e-01 3.60114872e-01 4.17412221e-01
-2.77519882e-01 7.69395053e-01 -2.74905622e-01 -3.78126770e-01
-2.20072106e-01 -5.21611273e-01 -6.44593716e-01 -7.17664540e-01
-3.28633860e-02 7.15559185e-01 3.32409918e-01 4.01353538... | [9.18376636505127, 4.191214084625244] |
7fa9a5b4-03fd-4e21-9d96-84153f9fd950 | a-multi-stage-framework-with-context | 1810.07075 | null | http://arxiv.org/abs/1810.07075v1 | http://arxiv.org/pdf/1810.07075v1.pdf | A Multi-stage Framework with Context Information Fusion Structure for Skin Lesion Segmentation | The computer-aided diagnosis (CAD) systems can highly improve the reliability
and efficiency of melanoma recognition. As a crucial step of CAD, skin lesion
segmentation has the unsatisfactory accuracy in existing methods due to large
variability in lesion appearance and artifacts. In this work, we propose a
framework e... | ["Chang'an Zhan", 'Shaofeng Yuan', 'Feng Yang', 'Yujiao Tang'] | 2018-10-16 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 6.49279952e-01 -2.78607011e-01 -1.16615243e-01 -4.35072094e-01
-1.12471688e+00 -2.29959965e-01 2.83259243e-01 2.46503115e-01
-5.59497535e-01 5.28521657e-01 9.03744996e-02 -1.31264150e-01
-3.62514138e-01 -5.06594896e-01 3.24645787e-02 -1.01593757e+00
3.41318876e-01 -7.87568092e-02 3.83356273e-01 -6.56084195... | [15.625676155090332, -2.9020097255706787] |
ba6b6569-31df-4931-9b81-93ae864ec753 | diffroll-diffusion-based-generative-music | 2210.05148 | null | https://arxiv.org/abs/2210.05148v2 | https://arxiv.org/pdf/2210.05148v2.pdf | DiffRoll: Diffusion-based Generative Music Transcription with Unsupervised Pretraining Capability | In this paper we propose a novel generative approach, DiffRoll, to tackle automatic music transcription (AMT). Instead of treating AMT as a discriminative task in which the model is trained to convert spectrograms into piano rolls, we think of it as a conditional generative task where we train our model to generate rea... | ['Yuki Mitsufuji', 'Dorien Herremans', 'Shusuke Takahashi', 'Naoya Takahashi', 'Naoki Murata', 'Toshimitsu Uesaka', 'Ryosuke Sawata', 'Kin Wai Cheuk'] | 2022-10-11 | null | null | null | null | ['music-transcription'] | ['music'] | [ 3.39831501e-01 1.40278712e-01 2.99072564e-01 1.71439722e-01
-1.48728144e+00 -8.60848367e-01 5.63668370e-01 -5.28467655e-01
-2.14064475e-02 7.54156947e-01 3.38576823e-01 -2.91731711e-02
1.43863603e-01 -4.24821883e-01 -8.73349786e-01 -7.44085133e-01
1.06751248e-01 5.33726335e-01 -3.16851914e-01 6.67809248... | [15.709324836730957, 5.703652381896973] |
a99aef55-7692-4692-9db8-eacc11113eeb | improving-automatic-parallel-training-via | 2307.02031 | null | https://arxiv.org/abs/2307.02031v1 | https://arxiv.org/pdf/2307.02031v1.pdf | Improving Automatic Parallel Training via Balanced Memory Workload Optimization | Transformer models have emerged as the leading approach for achieving state-of-the-art performance across various application domains, serving as the foundation for advanced large-scale deep learning (DL) models. However, efficiently training these models across multiple GPUs remains a complex challenge due to the abun... | ['Bin Cui', 'Xiaonan Nie', 'Fangcheng Fu', 'Xupeng Miao', 'Youhe Jiang', 'Yujie Wang'] | 2023-07-05 | null | null | null | null | ['navigate'] | ['reasoning'] | [-1.66305244e-01 -5.51476181e-01 -3.75577301e-01 -3.31485599e-01
-6.64441347e-01 -4.07986850e-01 4.19918716e-01 2.19180644e-01
-4.90894258e-01 3.37714434e-01 -1.09567910e-01 -6.91273570e-01
-3.60589951e-01 -8.56598020e-01 -3.84455740e-01 -5.05478978e-01
5.08884639e-02 7.94370532e-01 3.52995157e-01 -1.84556156... | [8.5361909866333, 3.488621473312378] |
ea4f6e71-74a0-4ea3-8ee0-6e17ee521572 | coap-dos-an-iot-network-intrusion-dataset | 2206.14341 | null | https://arxiv.org/abs/2206.14341v1 | https://arxiv.org/pdf/2206.14341v1.pdf | CoAP-DoS: An IoT Network Intrusion Dataset | The need for secure Internet of Things (IoT) devices is growing as IoT devices are becoming more integrated into vital networks. Many systems rely on these devices to remain available and provide reliable service. Denial of service attacks against IoT devices are a real threat due to the fact these low power devices ar... | ['Shankar Banik', 'Prosenjit Chatterjee', 'Jared Mathews'] | 2022-06-29 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 5.65742329e-02 -3.88255030e-01 -3.71291697e-01 -5.06012142e-01
5.67054115e-02 -7.21097112e-01 5.05973876e-01 2.96073984e-02
-4.31146562e-01 4.37837273e-01 -1.54586434e-01 -7.17834651e-01
-4.60151076e-01 -1.36664796e+00 -1.06730096e-01 -5.22943854e-01
9.64379217e-03 7.34703243e-01 7.29348481e-01 -4.09266979... | [5.1560797691345215, 7.15510368347168] |
c746c7f5-9348-41be-ae96-affd62f22c84 | visual-semantic-parsing-from-images-to | 2210.14862 | null | https://arxiv.org/abs/2210.14862v2 | https://arxiv.org/pdf/2210.14862v2.pdf | Visual Semantic Parsing: From Images to Abstract Meaning Representation | The success of scene graphs for visual scene understanding has brought attention to the benefits of abstracting a visual input (e.g., image) into a structured representation, where entities (people and objects) are nodes connected by edges specifying their relations. Building these representations, however, requires ex... | ['Afsaneh Fazly', 'Vladimir Pavlovic', 'Dhaivat J. Bhatt', 'Kalliopi Basioti', 'Federico Fancellu', 'Zhan Shi', 'Mohamed Ashraf Abdelsalam'] | 2022-10-26 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 6.09071136e-01 4.99753535e-01 -1.31538257e-01 -5.34974098e-01
-2.76615828e-01 -7.66697466e-01 8.65483820e-01 5.55163205e-01
-7.73438737e-02 2.59919494e-01 6.27658963e-01 -5.50494850e-01
1.55330941e-01 -9.42869127e-01 -6.73781514e-01 1.58316065e-02
3.19351137e-01 -5.43836281e-02 2.38967746e-01 -1.62073702... | [10.58191967010498, 1.6230825185775757] |
114277cc-2b5a-47db-aa1a-ccd5228c7a4b | real-time-detection-of-false-readings-in | null | null | https://ieeexplore.ieee.org/document/9765458 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9765458 | Real-Time Detection of False Readings in Smart Grid AMI Using Deep and Ensemble Learning research model code github | In the advanced metering infrastructure, smart meters are deployed at the consumers’ side to
regularly transmit fine-grained electricity consumption readings to the system operator (SO) for billing and
real-time load monitoring and energy management. However, fraudulent consumers may compromise their
meters to launc... | ['AND ABDULLAH M. ABUSORRAH', 'AHMAD H. MILYANI', 'ABDULAH JEZA ALJOHANI', 'JUNAID KHALID', 'MOHAMED M. E. A. MAHMOUD', 'Mohamed I. Ibrahem', 'MOHAMMED J. ABDULAAL'] | 2022-04-29 | null | null | null | ieee-access-2022-4 | ['ensemble-learning', 'ensemble-learning', 'management', 'energy-management'] | ['computer-vision', 'methodology', 'miscellaneous', 'time-series'] | [-1.62727982e-01 -2.01548308e-01 2.41087601e-01 -2.86570847e-01
-6.15547180e-01 -7.17964828e-01 4.55083102e-01 3.14016998e-01
-2.31024712e-01 7.30674505e-01 -2.47016564e-01 -5.60116768e-01
1.77581757e-01 -1.39957952e+00 -2.79576480e-01 -9.51922476e-01
-2.56649703e-01 3.96960288e-01 8.46258253e-02 8.50876719... | [6.027786731719971, 2.595210075378418] |
bf0f0c7a-a79a-4d0d-9c89-5f8358299a6d | unmasking-communication-partners-a-low-cost | 2011.03630 | null | https://arxiv.org/abs/2011.03630v1 | https://arxiv.org/pdf/2011.03630v1.pdf | Unmasking Communication Partners: A Low-Cost AI Solution for Digitally Removing Head-Mounted Displays in VR-Based Telepresence | Face-to-face conversation in Virtual Reality (VR) is a challenge when participants wear head-mounted displays (HMD). A significant portion of a participant's face is hidden and facial expressions are difficult to perceive. Past research has shown that high-fidelity face reconstruction with personal avatars in VR is pos... | ['Christian Geiger', 'Ralf Dörner', 'Alexander Pech', 'Philipp Ladwig'] | 2020-11-06 | null | null | null | null | ['face-reconstruction'] | ['computer-vision'] | [ 6.83610216e-02 5.23293018e-01 6.42683148e-01 -3.85180175e-01
-5.67569971e-01 -4.98678535e-01 2.03349918e-01 -1.19434762e+00
-9.76040140e-02 4.64112014e-01 -2.23865852e-01 -2.91150361e-01
8.30628455e-01 -5.58706343e-01 -8.38721395e-01 -1.09215103e-01
1.90258130e-01 2.48004779e-01 -2.09369659e-02 -5.10411918... | [12.928070068359375, -0.39840927720069885] |
1a408946-7752-4f22-bc19-04b0edb22bb2 | mrclens-an-mrc-dataset-bias-detection-toolkit-1 | 2207.08943 | null | https://arxiv.org/abs/2207.08943v1 | https://arxiv.org/pdf/2207.08943v1.pdf | MRCLens: an MRC Dataset Bias Detection Toolkit | Many recent neural models have shown remarkable empirical results in Machine Reading Comprehension, but evidence suggests sometimes the models take advantage of dataset biases to predict and fail to generalize on out-of-sample data. While many other approaches have been proposed to address this issue from the computati... | ['Eric P. Xing', 'Haohan Wang', 'Yifan Zhong'] | 2022-07-18 | null | null | null | null | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 2.46022463e-01 2.52987504e-01 -2.39765927e-01 -9.77369249e-01
-1.92202330e-01 -5.00620067e-01 5.21742225e-01 3.78876358e-01
-5.94108284e-01 5.35694122e-01 4.49948072e-01 -6.86544240e-01
7.46514723e-02 -6.60895407e-01 -6.73972845e-01 -2.62445450e-01
3.69653791e-01 3.42932850e-01 4.51769494e-02 -1.72104299... | [10.310673713684082, 8.051393508911133] |
5961c0ed-d10b-4c60-93ee-617d2fb3dcf8 | quantized-gan-for-complex-music-generation | 2204.00604 | null | https://arxiv.org/abs/2204.00604v2 | https://arxiv.org/pdf/2204.00604v2.pdf | Quantized GAN for Complex Music Generation from Dance Videos | We present Dance2Music-GAN (D2M-GAN), a novel adversarial multi-modal framework that generates complex musical samples conditioned on dance videos. Our proposed framework takes dance video frames and human body motions as input, and learns to generate music samples that plausibly accompany the corresponding input. Unli... | ['Sergey Tulyakov', 'Yan Yan', 'Menglei Chai', 'Panos Achlioptas', 'Yu Wu', 'Kyle Olszewski', 'Ye Zhu'] | 2022-04-01 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 4.84088510e-01 -9.88402143e-02 2.91811139e-03 1.58364803e-01
-1.04012072e+00 -9.48458970e-01 8.27610731e-01 -6.18429601e-01
6.40370101e-02 7.91768074e-01 2.85765469e-01 2.30689973e-01
-1.75963804e-01 -7.11557865e-01 -8.90964091e-01 -8.82560313e-01
-3.14798616e-02 2.68753290e-01 -2.02700078e-01 -4.52393860... | [15.782713890075684, 5.503054618835449] |
a7c74868-30c4-4492-a757-65835da44c63 | part-level-action-parsing-via-a-pose-guided | 2203.04476 | null | https://arxiv.org/abs/2203.04476v2 | https://arxiv.org/pdf/2203.04476v2.pdf | Part-level Action Parsing via a Pose-guided Coarse-to-Fine Framework | Action recognition from videos, i.e., classifying a video into one of the pre-defined action types, has been a popular topic in the communities of artificial intelligence, multimedia, and signal processing. However, existing methods usually consider an input video as a whole and learn models, e.g., Convolutional Neural... | ['Tao Mei', 'Yongdong Zhang', 'Dong Wu', 'Kun Liu', 'Wu Liu', 'Xinchen Liu', 'Xiaodong Chen'] | 2022-03-09 | null | null | null | null | ['action-parsing'] | ['natural-language-processing'] | [ 4.14712727e-01 -5.64891193e-03 -6.05229259e-01 -3.83714467e-01
-6.15192354e-01 -3.14348489e-01 2.92576730e-01 -2.28949770e-01
-7.71953687e-02 3.02637964e-01 5.07510781e-01 2.27570385e-01
1.64691716e-01 -7.50142872e-01 -8.94772649e-01 -6.54366493e-01
3.20013724e-02 1.84230134e-01 4.63377297e-01 9.33936089... | [8.178271293640137, 0.4891689419746399] |
3cb8ec05-6e44-4e37-9951-aba21e83d192 | very-low-resource-sentence-alignment-luhya-1 | 2211.00046 | null | https://arxiv.org/abs/2211.00046v1 | https://arxiv.org/pdf/2211.00046v1.pdf | Very Low Resource Sentence Alignment: Luhya and Swahili | Language-agnostic sentence embeddings generated by pre-trained models such as LASER and LaBSE are attractive options for mining large datasets to produce parallel corpora for low-resource machine translation. We test LASER and LaBSE in extracting bitext for two related low-resource African languages: Luhya and Swahili.... | ['Bruce A. Bassett', 'Everlyn Asiko Chimoto'] | 2022-10-31 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [-9.29199014e-05 1.46636199e-02 -2.66291678e-01 -5.31758010e-01
-1.48914576e+00 -6.81490421e-01 6.78826213e-01 2.53067285e-01
-8.63434672e-01 9.30686295e-01 6.73764408e-01 -7.79019833e-01
2.28125229e-01 -6.62029684e-01 -6.96766675e-01 -3.28263134e-01
2.41508111e-02 8.34941506e-01 -3.89990479e-01 -4.91785765... | [11.309114456176758, 10.215248107910156] |
cb54b6c8-d634-46ae-9868-06fd8b728814 | stereosnakes-contour-based-consistent-object | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Ju_StereoSnakes_Contour_Based_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Ju_StereoSnakes_Contour_Based_ICCV_2015_paper.pdf | StereoSnakes: Contour Based Consistent Object Extraction For Stereo Images | Consistent object extraction plays an essential role for stereo image editing with the population of stereoscopic 3D media. Most previous methods perform segmentation on entire images for both views using dense stereo correspondence constraints. We find that for such kind of methods the computation is highly redundant ... | ['Gangshan Wu', 'Tongwei Ren', 'Ran Ju'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['stereo-matching'] | ['computer-vision'] | [ 5.18192828e-01 -6.12586141e-02 2.06357643e-01 -3.24526548e-01
-4.87680167e-01 -5.88423848e-01 3.45080137e-01 -2.86757022e-01
-4.91087794e-01 7.22330272e-01 -2.00834945e-01 2.01618508e-03
1.14588730e-01 -7.85661757e-01 -5.80546916e-01 -4.79798228e-01
6.47168100e-01 4.27529544e-01 1.05225027e+00 -1.09752215... | [9.305294036865234, -2.4027369022369385] |
a5c4fb82-333e-4c8a-a001-a9884e38b892 | fine-grained-object-categorization-for | 2210.04613 | null | https://arxiv.org/abs/2210.04613v2 | https://arxiv.org/pdf/2210.04613v2.pdf | Enhancing Fine-Grained 3D Object Recognition using Hybrid Multi-Modal Vision Transformer-CNN Models | Robots operating in human-centered environments, such as retail stores, restaurants, and households, are often required to distinguish between similar objects in different contexts with a high degree of accuracy. However, fine-grained object recognition remains a challenge in robotics due to the high intra-category and... | ['Georgios Tziafas', 'Hamidreza Kasaei', 'Songsong Xiong'] | 2022-10-03 | null | null | null | null | ['3d-object-recognition', 'object-categorization', 'fine-grained-image-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.56563388e-02 -3.38371515e-01 2.34772697e-01 -4.38840121e-01
-4.84903425e-01 -5.72777450e-01 4.29571658e-01 -4.25892125e-04
-3.46311629e-01 4.85170394e-01 -1.21142000e-01 2.08902270e-01
-1.91665322e-01 -7.98534274e-01 -8.92503202e-01 -5.56475699e-01
5.31974547e-02 3.92024368e-01 1.95922032e-01 -2.66422421... | [7.625561237335205, -1.3058170080184937] |
824b8856-784e-41bf-ac63-3984e91f8fd5 | high-throughput-virtual-screening-with-data | 1312.1003 | null | http://arxiv.org/abs/1312.1003v1 | http://arxiv.org/pdf/1312.1003v1.pdf | High Throughput Virtual Screening with Data Level Parallelism in Multi-core Processors | Improving the throughput of molecular docking, a computationally intensive
phase of the virtual screening process, is a highly sought area of research
since it has a significant weight in the drug designing process. With such
improvements, the world might find cures for incurable diseases like HIV
disease and Cancer so... | ['Upul Senanayake', 'Rahal Prabuddha', 'Roshan Ragel'] | 2013-12-04 | null | null | null | null | ['molecular-docking'] | ['medical'] | [-1.67853851e-02 -5.53597033e-01 -1.07113034e-01 -9.60115194e-02
-2.46496603e-01 -5.58418691e-01 2.20367014e-01 4.85257179e-01
-5.84827542e-01 1.05354023e+00 -1.83940366e-01 -8.60936224e-01
1.45704731e-01 -8.87017727e-01 -5.39982378e-01 -7.18328953e-01
2.86034811e-02 6.16571844e-01 2.74985999e-01 -2.80279845... | [4.909379482269287, 5.49327278137207] |
7c2e74ce-5c3b-45dc-be09-c4e4fc67cb59 | robustifying-multi-hop-qa-through-pseudo | 2107.03242 | null | https://arxiv.org/abs/2107.03242v1 | https://arxiv.org/pdf/2107.03242v1.pdf | Robustifying Multi-hop QA through Pseudo-Evidentiality Training | This paper studies the bias problem of multi-hop question answering models, of answering correctly without correct reasoning. One way to robustify these models is by supervising to not only answer right, but also with right reasoning chains. An existing direction is to annotate reasoning chains to train models, requiri... | ['Dohyeon Lee', 'Sang-eun Han', 'Seung-won Hwang', 'Kyungjae Lee'] | 2021-07-07 | null | https://aclanthology.org/2021.acl-long.476 | https://aclanthology.org/2021.acl-long.476.pdf | acl-2021-5 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 3.27533901e-01 1.03777933e+00 -2.48188347e-01 -6.64682209e-01
-1.13270223e+00 -8.21547210e-01 6.89907014e-01 3.80696505e-01
-4.17545408e-01 1.38916588e+00 4.08587426e-01 -6.99977636e-01
-2.63401657e-01 -9.58387792e-01 -1.13357341e+00 -1.04478896e-01
4.58535582e-01 9.29847181e-01 6.39139473e-01 -3.92666996... | [10.819925308227539, 7.871837615966797] |
b22b3031-d287-4ef1-966b-b114d33666c6 | open-access-dataset-for-electromyography | 2201.01051 | null | https://arxiv.org/abs/2201.01051v2 | https://arxiv.org/pdf/2201.01051v2.pdf | Open Access Dataset for Electromyography based Multi-code Biometric Authentication | Recently, surface electromyogram (EMG) has been proposed as a novel biometric trait for addressing some key limitations of current biometrics, such as spoofing and liveness. The EMG signals possess a unique characteristic: they are inherently different for individuals (biometrics), and they can be customized to realize... | ['Ning Jiang', 'Jiayuan He', 'Ashirbad Pradhan'] | 2022-01-04 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 4.12297636e-01 -4.34275657e-01 -4.24068838e-01 -2.08723575e-01
-7.16235042e-01 -5.59086084e-01 2.58482277e-01 -2.57980168e-01
-7.05884099e-01 6.99469030e-01 -6.93214387e-02 9.42822099e-02
-2.93205619e-01 -1.89558402e-01 -1.31916538e-01 -8.78448129e-01
-2.28894934e-01 -1.56609938e-02 -2.32204244e-01 1.76923722... | [13.90792465209961, 1.6584053039550781] |
5b3ab7a7-b5a2-45f9-99f4-b4ce802ca030 | point-pc-point-cloud-completion-guided-by | 2305.17770 | null | https://arxiv.org/abs/2305.17770v1 | https://arxiv.org/pdf/2305.17770v1.pdf | Point-PC: Point Cloud Completion Guided by Prior Knowledge via Causal Inference | Point cloud completion aims to recover raw point clouds captured by scanners from partial observations caused by occlusion and limited view angles. Many approaches utilize a partial-complete paradigm in which missing parts are directly predicted by a global feature learned from partial inputs. This makes it hard to rec... | ['AnAn Liu', 'Nicu Sebe', 'Bruno Lepri', 'Weijie Wang', 'Ruidong Chen', 'Chuanqi Jiao', 'Weizhi Nie'] | 2023-05-28 | null | null | null | null | ['point-cloud-completion', 'causal-inference', 'causal-inference'] | ['computer-vision', 'knowledge-base', 'miscellaneous'] | [ 2.19600108e-02 2.09751531e-01 -1.22949362e-01 -4.78395790e-01
-6.66691363e-01 -4.99891728e-01 6.09084308e-01 -2.40127981e-01
9.75732654e-02 4.12243336e-01 1.30493060e-01 6.62509818e-03
-2.44106844e-01 -1.16045105e+00 -1.31319797e+00 -6.28512442e-01
3.53660107e-01 9.34964478e-01 1.73307344e-01 -1.48400040... | [8.380796432495117, -3.481930732727051] |
e5096a1c-566d-4a43-b22a-62eb0baa578d | dialect-identification-in-nuanced-arabic | 2102.09749 | null | https://arxiv.org/abs/2102.09749v2 | https://arxiv.org/pdf/2102.09749v2.pdf | Dialect Identification in Nuanced Arabic Tweets Using Farasa Segmentation and AraBERT | This paper presents our approach to address the EACL WANLP-2021 Shared Task 1: Nuanced Arabic Dialect Identification (NADI). The task is aimed at developing a system that identifies the geographical location(country/province) from where an Arabic tweet in the form of modern standard Arabic or dialect comes from. We sol... | ['Anshul Wadhawan'] | 2021-02-19 | null | https://aclanthology.org/2021.wanlp-1.35 | https://aclanthology.org/2021.wanlp-1.35.pdf | eacl-wanlp-2021-4 | ['dialect-identification'] | ['natural-language-processing'] | [-1.17072448e-01 -1.25209972e-01 1.14043273e-01 -4.20512557e-01
-1.28524208e+00 -9.67591703e-01 9.65767622e-01 -3.85674601e-03
-5.07855654e-01 1.05409026e+00 1.97282821e-01 -5.26565075e-01
-2.63443410e-01 -4.91725773e-01 -4.03245687e-01 -6.00773990e-01
-3.83691043e-02 1.18668580e+00 1.18345410e-01 -7.83646405... | [10.170493125915527, 10.816917419433594] |
64bf5f58-6c39-40fa-b46b-158eb67ecb66 | improving-self-supervised-single-view-depth | 1908.11112 | null | https://arxiv.org/abs/1908.11112v1 | https://arxiv.org/pdf/1908.11112v1.pdf | Improving Self-Supervised Single View Depth Estimation by Masking Occlusion | Single view depth estimation models can be trained from video footage using a self-supervised end-to-end approach with view synthesis as the supervisory signal. This is achieved with a framework that predicts depth and camera motion, with a loss based on reconstructing a target video frame from temporally adjacent fram... | ['Maarten Schellevis'] | 2019-08-29 | null | null | null | null | ['depth-and-camera-motion'] | ['computer-vision'] | [ 5.61389029e-01 4.86478835e-01 -1.56563535e-01 -4.19939190e-01
-6.21287763e-01 -3.53979617e-01 5.61348498e-01 -1.70996830e-01
-3.78582925e-01 8.31082404e-01 3.30963105e-01 1.38708264e-01
1.50754094e-01 -7.92886019e-01 -1.31675577e+00 -6.99894667e-01
8.58717188e-02 3.63763869e-01 3.75184506e-01 2.09066257... | [8.773252487182617, -2.3782365322113037] |
b0f2fadb-b885-45e2-ab5a-ae863158ecaa | new-pyramidal-hybrid-textural-and-deep | 2203.15090 | null | https://arxiv.org/abs/2203.15090v1 | https://arxiv.org/pdf/2203.15090v1.pdf | New pyramidal hybrid textural and deep features based automatic skin cancer classification model: Ensemble DarkNet and textural feature extractor | Background: Skin cancer is one of the widely seen cancer worldwide and automatic classification of skin cancer can be benefited dermatology clinics for an accurate diagnosis. Hence, a machine learning-based automatic skin cancer detection model must be developed. Material and Method: This research interests to overcome... | ['Sengul Dogan', 'Turker Tuncer', 'Mehmet Baygin'] | 2022-03-28 | null | null | null | null | ['skin-cancer-classification'] | ['medical'] | [ 5.28891563e-01 -3.52102518e-01 -3.12573105e-01 -2.08407491e-02
-6.39923573e-01 -1.36018828e-01 3.67504448e-01 2.93589711e-01
-4.57434148e-01 9.37790096e-01 -2.47712675e-02 5.41536175e-02
-2.42070943e-01 -9.93975520e-01 2.77550310e-01 -1.26109540e+00
-4.80980277e-02 -2.83162355e-01 3.69490981e-01 -8.05342644... | [15.556584358215332, -2.982295513153076] |
266ae77f-c9ae-474a-85c8-7210e5be0a9b | the-defender-s-perspective-on-automatic | 2305.12804 | null | https://arxiv.org/abs/2305.12804v2 | https://arxiv.org/pdf/2305.12804v2.pdf | The defender's perspective on automatic speaker verification: An overview | Automatic speaker verification (ASV) plays a critical role in security-sensitive environments. Regrettably, the reliability of ASV has been undermined by the emergence of spoofing attacks, such as replay and synthetic speech, as well as adversarial attacks and the relatively new partially fake speech. While there are s... | ['Hung-Yi Lee', 'Helen Meng', 'Lingwei Meng', 'Jiawen Kang', 'Haibin Wu'] | 2023-05-22 | null | null | null | null | ['speaker-verification'] | ['speech'] | [ 3.50675613e-01 2.65127450e-01 1.65035814e-01 -4.26218331e-01
-7.17145681e-01 -1.02627397e+00 8.64494741e-01 -2.77735591e-01
-8.93504992e-02 7.81050265e-01 3.25077981e-01 -5.63040972e-01
2.85633683e-01 -2.18384355e-01 -3.72512728e-01 -5.81101179e-01
-1.57349646e-01 1.12802424e-01 1.17056236e-01 -7.71602809... | [14.07193660736084, 5.866696357727051] |
3e868bc8-bdbb-40c1-8fe8-be3d602e91dc | cell-segmentation-by-combining-marker | 2004.01607 | null | https://arxiv.org/abs/2004.01607v1 | https://arxiv.org/pdf/2004.01607v1.pdf | Cell Segmentation by Combining Marker-Controlled Watershed and Deep Learning | We propose a cell segmentation method for analyzing images of densely clustered cells. The method combines the strengths of marker-controlled watershed transformation and a convolutional neural network (CNN). We demonstrate the method universality and high performance on three Cell Tracking Challenge (CTC) datasets of ... | ['Filip Lux', 'Petr Matula'] | 2020-04-03 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 2.97254592e-01 -2.10277334e-01 -3.84290628e-02 -1.91460662e-02
-9.65411246e-01 -7.06966102e-01 7.88365304e-01 5.23008764e-01
-7.20608652e-01 7.84694374e-01 -2.01329544e-01 -1.78113788e-01
3.50356042e-01 -7.37424433e-01 -7.73660481e-01 -1.03823507e+00
-8.43019933e-02 5.72811425e-01 6.16029143e-01 -4.57498692... | [14.560383796691895, -3.186525344848633] |
6d89ae70-a1b6-47e0-b4aa-fe678cc384ca | pf-net-point-fractal-network-for-3d-point | 2003.00410 | null | https://arxiv.org/abs/2003.00410v1 | https://arxiv.org/pdf/2003.00410v1.pdf | PF-Net: Point Fractal Network for 3D Point Cloud Completion | In this paper, we propose a Point Fractal Network (PF-Net), a novel learning-based approach for precise and high-fidelity point cloud completion. Unlike existing point cloud completion networks, which generate the overall shape of the point cloud from the incomplete point cloud and always change existing points and enc... | ['Xinyi Le', 'Jiawen Xu', 'Feng Ni', 'Zitian Huang', 'Yikuan Yu'] | 2020-03-01 | pf-net-point-fractal-network-for-3d-point-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Huang_PF-Net_Point_Fractal_Network_for_3D_Point_Cloud_Completion_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Huang_PF-Net_Point_Fractal_Network_for_3D_Point_Cloud_Completion_CVPR_2020_paper.pdf | cvpr-2020-6 | ['point-cloud-completion'] | ['computer-vision'] | [-9.13225040e-02 2.81757355e-01 3.58237833e-01 3.62956747e-02
-9.52262998e-01 -6.93558753e-01 2.75277436e-01 2.68827416e-02
8.16001594e-02 5.51508546e-01 -3.31782550e-01 3.60225663e-02
-6.07319809e-02 -9.34350967e-01 -1.28355670e+00 -2.37953335e-01
-1.56493187e-01 7.68207192e-01 2.15991110e-01 -3.10612589... | [8.446930885314941, -3.583808660507202] |
6a7b83e1-106f-462f-8e34-d8c0d38b0160 | stop-overkilling-simple-tasks-with-black-box | 2302.02804 | null | https://arxiv.org/abs/2302.02804v2 | https://arxiv.org/pdf/2302.02804v2.pdf | Stop overkilling simple tasks with black-box models and use transparent models instead | In recent years, the employment of deep learning methods has led to several significant breakthroughs in artificial intelligence. Different from traditional machine learning models, deep learning-based approaches are able to extract features autonomously from raw data. This allows for bypassing the feature engineering ... | ['Andrea Albarelli', 'Andrea Gasparetto', 'Alessandro Zangari', 'Matteo Marcuzzo', 'Matteo Rizzo'] | 2023-02-06 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-3.08389038e-01 -6.58272654e-02 3.20677124e-02 -1.98065311e-01
-1.88741818e-01 -4.78468299e-01 9.18471932e-01 5.39149046e-01
-5.59565008e-01 7.05629647e-01 -4.18217719e-01 2.80864984e-02
-3.00611317e-01 -1.19874358e+00 -3.12461972e-01 -6.81623220e-01
-1.19901054e-01 3.69209707e-01 1.78186253e-01 -3.96513134... | [8.504777908325195, 2.715895414352417] |
e6bcd16a-1449-4e0d-a919-64bf58c1891d | self-supervised-learning-of-occlusion-aware | 2108.03893 | null | https://arxiv.org/abs/2108.03893v3 | https://arxiv.org/pdf/2108.03893v3.pdf | Self-supervised Learning of Occlusion Aware Flow Guided 3D Geometry Perception with Adaptive Cross Weighted Loss from Monocular Videos | Self-supervised deep learning-based 3D scene understanding methods can overcome the difficulty of acquiring the densely labeled ground-truth and have made a lot of advances. However, occlusions and moving objects are still some of the major limitations. In this paper, we explore the learnable occlusion aware optical fl... | ['Guizhong Liu', 'Jiaojiao Fang'] | 2021-08-09 | null | null | null | null | ['3d-geometry-perception'] | ['computer-vision'] | [-0.18027233 -0.13399471 -0.14541802 -0.7032686 -0.7337041 -0.45668903
0.29760435 -0.510343 -0.32250574 0.7340088 0.29001442 0.10816291
-0.14109047 -0.5962165 -0.6722056 -0.67765427 0.18418522 0.28343746
0.19556312 0.21972351 0.15531862 0.55796605 -1.5534258 -0.01798168
1.0570085 0.987519 0.... | [8.672439575195312, -2.167828321456909] |
6b62b94d-73cb-4d3b-aa9b-63a750308102 | leveraging-large-language-models-in | 2305.07961 | null | https://arxiv.org/abs/2305.07961v2 | https://arxiv.org/pdf/2305.07961v2.pdf | Leveraging Large Language Models in Conversational Recommender Systems | A Conversational Recommender System (CRS) offers increased transparency and control to users by enabling them to engage with the system through a real-time multi-turn dialogue. Recently, Large Language Models (LLMs) have exhibited an unprecedented ability to converse naturally and incorporate world knowledge and common... | ['Zhenning Tan', 'Manoj Tiwari', 'Zexi Chen', 'Brian Chu', 'Harsh Lara', 'Ajay Patel', 'Gabriel Schubiner', 'Jun Xie', 'Changbo Long', 'Hakim Sidahmed', 'David Allen', 'Sameer Ahuja', 'Luke Friedman'] | 2023-05-13 | null | null | null | null | ['dialogue-management', 'common-sense-reasoning'] | ['natural-language-processing', 'reasoning'] | [-7.58990496e-02 4.66810405e-01 -5.98072931e-02 -5.84929824e-01
-5.09616375e-01 -8.76004815e-01 7.34267175e-01 -2.87948728e-01
-5.27797379e-02 6.89529061e-01 8.99047554e-01 -4.42414701e-01
-7.69321248e-02 -5.27879477e-01 -3.03558469e-01 2.25227252e-01
-2.90341936e-02 6.40948296e-01 -6.64500578e-04 -8.06111753... | [12.524327278137207, 7.700018882751465] |
37ffe439-289c-4579-ae56-16fc6a466484 | superpoint-self-supervised-interest-point | 1712.07629 | null | http://arxiv.org/abs/1712.07629v4 | http://arxiv.org/pdf/1712.07629v4.pdf | SuperPoint: Self-Supervised Interest Point Detection and Description | This paper presents a self-supervised framework for training interest point
detectors and descriptors suitable for a large number of multiple-view geometry
problems in computer vision. As opposed to patch-based neural networks, our
fully-convolutional model operates on full-sized images and jointly computes
pixel-level... | ['Tomasz Malisiewicz', 'Daniel DeTone', 'Andrew Rabinovich'] | 2017-12-20 | null | null | null | null | ['interest-point-detection', 'homography-estimation'] | ['computer-vision', 'computer-vision'] | [-5.36346324e-02 -2.03582063e-01 -8.40472504e-02 -1.10369809e-01
-1.06474054e+00 -6.16829515e-01 8.77483666e-01 -2.28807837e-01
-1.80361018e-01 1.64732739e-01 -3.06855589e-02 3.39757323e-01
1.38533115e-01 -8.39951694e-01 -1.17058027e+00 -2.76097625e-01
-5.03329933e-02 8.19787145e-01 5.07062912e-01 -4.45118308... | [8.061495780944824, -2.1492040157318115] |
b578fdd3-b36f-4a92-8188-31c63209ae6d | learning-attentive-and-hierarchical | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2306_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123600103.pdf | Learning Attentive and Hierarchical Representations for 3D Shape Recognition | This paper proposes a novel method for 3D shape representation learning, namely Hyperbolic Embedded Attentive Representation (HEAR). Different from existing multi-view based methods, HEAR develops a unified framework to address both multi-view redundancy and single-view incompleteness. Specifically, HEAR firstly employ... | ['Fan Zhu', 'Jie Qin', 'Yuming Shen', 'Li Liu', 'Ling Shao', 'Jiaxin Chen'] | null | null | null | null | eccv-2020-8 | ['3d-shape-retrieval', '3d-shape-recognition', '3d-shape-representation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.70430899e-01 -1.41135558e-01 -1.09815642e-01 -4.78437096e-01
-1.09993267e+00 -5.48028290e-01 8.82969141e-01 2.04589237e-02
1.97009325e-01 -4.86504473e-02 4.92962897e-01 1.49230689e-01
-3.57065767e-01 -9.78101969e-01 -4.72458810e-01 -8.66820514e-01
3.18472832e-01 4.26375747e-01 2.64544278e-01 -2.09495679... | [8.14376163482666, -3.8878486156463623] |
3fce106d-8e0d-49ad-8df8-b80631bf2431 | what-s-there-in-the-dark | null | null | https://ieeexplore.ieee.org/document/8803299 | https://ieeexplore.ieee.org/document/8803299 | What's There in the Dark | Scene Parsing is an important cog for modern autonomousdriving systems. Most of the works in semantic segmenta-tion pertains to day-time scenes with favourable weather andillumination conditions. In this paper, we propose a noveldeep architecture, NiSeNet, that performs semantic segmen-tation of nigh... | ['Sukhendu Das', 'Saptakatha Adak', 'Sauradip Nag'] | 2019-09-24 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [-1.45250261e-01 2.27308631e-01 1.79868892e-01 -7.48024464e-01
-6.71724498e-01 -7.04591095e-01 7.47377813e-01 -1.41421080e-01
-8.18372130e-01 9.06841338e-01 -1.11497171e-01 -2.70482093e-01
1.28280640e-01 -1.01161015e+00 -1.03541160e+00 -5.52516699e-01
2.93335199e-01 3.60336185e-01 6.38295233e-01 -6.84424698... | [8.80954360961914, -1.4530682563781738] |
8c513dbc-7310-413d-942c-f757a60ccb9e | multi-level-domain-adaptation-for-lane | 2206.10692 | null | https://arxiv.org/abs/2206.10692v2 | https://arxiv.org/pdf/2206.10692v2.pdf | Multi-level Domain Adaptation for Lane Detection | We focus on bridging domain discrepancy in lane detection among different scenarios to greatly reduce extra annotation and re-training costs for autonomous driving. Critical factors hinder the performance improvement of cross-domain lane detection that conventional methods only focus on pixel-wise loss while ignoring s... | ['Guangliang Cheng', 'Jia Shi', 'Boheng Zhang', 'Chenguang Li'] | 2022-06-21 | null | null | null | null | ['lane-detection'] | ['computer-vision'] | [ 1.46667689e-01 -1.17062978e-01 -3.00905555e-01 -5.38499594e-01
-8.91004562e-01 -5.79988837e-01 4.90907818e-01 2.53263153e-02
-4.66132730e-01 8.47649932e-01 7.74646550e-02 -3.56370509e-01
2.07886621e-01 -6.24092460e-01 -7.67023385e-01 -7.58022547e-01
3.77354831e-01 -1.25621125e-01 9.57969606e-01 -2.62755901... | [8.091313362121582, -1.4787602424621582] |
0b190aa1-05c8-4668-a9ee-527f1c2a44d5 | model-free-robust-average-reward | 2305.10504 | null | https://arxiv.org/abs/2305.10504v1 | https://arxiv.org/pdf/2305.10504v1.pdf | Model-Free Robust Average-Reward Reinforcement Learning | Robust Markov decision processes (MDPs) address the challenge of model uncertainty by optimizing the worst-case performance over an uncertainty set of MDPs. In this paper, we focus on the robust average-reward MDPs under the model-free setting. We first theoretically characterize the structure of solutions to the robus... | ['Shaofeng Zou', 'Ashley Prater-Bennette', 'George Atia', 'Alvaro Velasquez', 'Yue Wang'] | 2023-05-17 | null | null | null | null | ['q-learning'] | ['methodology'] | [-9.46553051e-02 1.47927210e-01 -1.53442860e-01 -2.84148812e-01
-1.56614661e+00 -6.67192578e-01 4.12135839e-01 3.84165466e-01
-5.77938855e-01 1.20682693e+00 -3.60872559e-02 -5.20292819e-01
-8.72720420e-01 -3.80550265e-01 -5.56758761e-01 -8.12185109e-01
-6.48148298e-01 6.53675377e-01 -6.36978671e-02 3.86751026... | [4.395322799682617, 2.65397310256958] |
32b0d651-1509-4851-9414-529b27bdd5ed | projected-canonical-decomposition-for | null | null | https://openreview.net/forum?id=ByeAK1BKPB | https://openreview.net/pdf?id=ByeAK1BKPB | Projected Canonical Decomposition for Knowledge Base Completion | The leading approaches to tensor completion and link prediction are based on the canonical polyadic (CP) decomposition of tensors. While these approaches were originally motivated by low rank approximations, the best performances are usually obtained for ranks as high as permitted by computation constraints. For large ... | ['Nicolas Usunier', 'Joan Bruna', 'Guillaume Obozinski', 'Timothée Lacroix'] | 2019-09-25 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [-3.65908742e-01 -3.07347868e-02 -3.53266358e-01 1.09138653e-01
-5.53443313e-01 -5.92348337e-01 5.15296280e-01 5.06814271e-02
-2.92824268e-01 6.13613129e-01 4.88881320e-01 -4.79756713e-01
-5.03961384e-01 -6.48119211e-01 -7.87375033e-01 -6.54499114e-01
-3.83078575e-01 7.71732926e-01 2.29836404e-01 -4.98954445... | [7.0649824142456055, 4.764883041381836] |
f2000929-13b1-4a65-81d7-2e7c43ecdf68 | ubernet-training-a-universal-convolutional | 1609.02132 | null | http://arxiv.org/abs/1609.02132v1 | http://arxiv.org/pdf/1609.02132v1.pdf | UberNet: Training a `Universal' Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory | In this work we introduce a convolutional neural network (CNN) that jointly
handles low-, mid-, and high-level vision tasks in a unified architecture that
is trained end-to-end. Such a universal network can act like a `swiss knife'
for vision tasks; we call this architecture an UberNet to indicate its
overarching natur... | ['Iasonas Kokkinos'] | 2016-09-07 | null | null | null | null | ['human-part-segmentation'] | ['computer-vision'] | [ 3.24633360e-01 2.31001392e-01 6.37794435e-02 -2.20564395e-01
-6.93476379e-01 -3.79878819e-01 5.87389946e-01 1.59774870e-01
-6.77553535e-01 4.67278183e-01 -3.26896727e-01 -3.66931111e-01
5.49694002e-01 -5.26653707e-01 -1.11082077e+00 -4.64588314e-01
6.81180432e-02 4.65700597e-01 8.75417829e-01 -2.03066573... | [9.385237693786621, 0.12311527878046036] |
e9c32e11-2547-4997-a4fb-dc226e8e5454 | can-chatgpt-enable-its-the-case-of-mixed | 2306.08094 | null | https://arxiv.org/abs/2306.08094v1 | https://arxiv.org/pdf/2306.08094v1.pdf | Can ChatGPT Enable ITS? The Case of Mixed Traffic Control via Reinforcement Learning | The surge in Reinforcement Learning (RL) applications in Intelligent Transportation Systems (ITS) has contributed to its growth as well as highlighted key challenges. However, defining objectives of RL agents in traffic control and management tasks, as well as aligning policies with these goals through an effective for... | ['Weizi Li', 'Bibek Poudel', 'Michael Villarreal'] | 2023-06-13 | null | null | null | null | ['general-knowledge', 'management'] | ['miscellaneous', 'miscellaneous'] | [-2.2058128e-01 8.0360621e-02 -3.5129663e-01 -2.8279167e-01
-8.6241162e-01 -6.5459764e-01 5.9519422e-01 -1.0101039e-01
-6.6825491e-01 1.1944137e+00 3.6956513e-01 -6.6077387e-01
-1.8493491e-01 -6.4309531e-01 -3.8538200e-01 -3.5954425e-01
6.1558355e-02 9.6351367e-01 1.7499581e-01 -6.3815391e-01
2.5595176e-01... | [4.838532447814941, 1.4411981105804443] |
6bc00b3e-d515-40b0-a4a8-b21015dc7e2f | transferable-multi-level-attention-neural | null | null | https://doi.org/10.26434/chemrxiv.12588170.v1 | https://chemrxiv.org/ndownloader/articles/12588170/versions/1/export_pdf | Transferable Multi-level Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multi-task Learning | The development of efficient models for predicting specific properties through machine learning is of great importance for the innovation of chemistry and material science. However, predicting electronic structure properties like frontier molecular orbital HOMO and LUMO energy levels and their HOMO-LUMO gaps from the s... | ['Jing Ma', 'Yanwen Guo', 'Yanyan Jiang', 'Zheng Cheng', 'Qingqing Jia', 'Liqiang Lin'] | 2020-06-30 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [ 3.87224793e-01 -2.11701348e-01 -3.94520551e-01 -2.45322436e-01
-9.12798822e-01 -4.86113369e-01 3.25574219e-01 6.44628406e-01
-2.92920589e-01 1.28471959e+00 7.60771632e-02 -6.56574845e-01
-1.08282290e-01 -7.76971042e-01 -9.50579286e-01 -1.20375395e+00
-4.99166781e-04 3.43162298e-01 -2.77149498e-01 -1.21051811... | [5.097573757171631, 5.805250644683838] |
551bdfe1-500b-4ba7-9539-f68216240279 | modality-alignment-between-deep | null | null | https://aclanthology.org/2022.lrec-1.295 | https://aclanthology.org/2022.lrec-1.295.pdf | Modality Alignment between Deep Representations for Effective Video-and-Language Learning | Video-and-Language learning, such as video question answering or video captioning, is the next challenge in the deep learning society, as it pursues the way how human intelligence perceives everyday life. These tasks require the ability of multi-modal reasoning which is to handle both visual information and text inform... | ['Kyomin Jung', 'Yongil Kim', 'Hyeongu Yun'] | null | null | null | null | lrec-2022-6 | ['video-question-answering'] | ['computer-vision'] | [ 1.22395270e-01 -3.64571631e-01 -1.82276204e-01 -1.82274640e-01
-8.35572600e-01 -5.39543867e-01 9.58103597e-01 -1.01782165e-01
-5.81543982e-01 2.46150002e-01 5.38298368e-01 -2.99483895e-01
1.75763518e-01 -4.67505962e-01 -9.63484347e-01 -5.80598950e-01
4.35140371e-01 3.08161112e-03 9.72206220e-02 -1.71841830... | [10.44334888458252, 1.1424341201782227] |
9a8dd3e5-5359-4769-b922-b42e8da81f50 | piclick-picking-the-desired-mask-in-click | 2304.11609 | null | https://arxiv.org/abs/2304.11609v1 | https://arxiv.org/pdf/2304.11609v1.pdf | PiClick: Picking the desired mask in click-based interactive segmentation | Click-based interactive segmentation enables productive pixel-level annotation and image editing with simple user clicks, whereas target ambiguity remains a problem hindering precise segmentation. That is, in scenes with rich context, one click may refer to multiple potential targets residing in corresponding masks, wh... | ['Efstratios Gavves', 'Guoliang Kang', 'Xu Tang', 'Yao Hu', 'XiaoLong Jiang', 'Jie Liu', 'Haochen Wang', 'Cilin Yan'] | 2023-04-23 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [ 5.68330944e-01 2.97700882e-01 -1.99789181e-01 -3.81689727e-01
-9.34393585e-01 -9.74934995e-01 1.99971229e-01 -1.01820856e-01
-3.45599592e-01 4.85947877e-01 -3.34101282e-02 -2.89697051e-01
2.55573809e-01 -4.78466094e-01 -5.64773679e-01 -2.94121057e-01
3.94554108e-01 2.73341417e-01 8.77757549e-01 1.39510617... | [9.481090545654297, -0.09500307589769363] |
299f5ef4-326d-419e-b50c-6f2ceb16b231 | deeply-self-supervising-edge-to-contour | 1808.00739 | null | https://arxiv.org/abs/1808.00739v5 | https://arxiv.org/pdf/1808.00739v5.pdf | Deeply Self-Supervised Contour Embedded Neural Network Applied to Liver Segmentation | Objective: Herein, a neural network-based liver segmentation algorithm is proposed, and its performance was evaluated using abdominal computed tomography (CT) images. Methods: A fully convolutional network was developed to overcome the volumetric image segmentation problem. To guide a neural network to accurately delin... | ['Yeong-Gil Shin', 'Jeongjin Lee', 'Minkyung Lee', 'Jingyu Lee', 'Minyoung Chung'] | 2018-08-02 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-1.23592228e-01 1.15484171e-01 -1.61723897e-01 -6.81011915e-01
-3.20158452e-01 -2.47412205e-01 2.04287320e-01 2.65824765e-01
-5.97178757e-01 4.70848888e-01 1.16160512e-01 -1.76475629e-01
5.92836738e-02 -6.75944448e-01 -2.49018103e-01 -1.00057590e+00
-5.11893868e-01 4.95703578e-01 1.12889282e-01 1.37357429... | [14.46501636505127, -2.7685413360595703] |
cc3a1b60-524d-4500-b135-d256a9d741c7 | rethinking-zero-shot-action-recognition | null | null | https://link.springer.com/chapter/10.1007/978-3-031-19772-7_7 | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136640102.pdf | Rethinking Zero-shot Action Recognition: Learning from Latent Atomic Actions | To avoid time-consuming annotating and retraining cycle in
applying supervised action recognition models, Zero-Shot Action Recognition (ZSAR) has become a thriving direction. ZSAR requires models to recognize actions that never appear in training set through bridging visual features and semantic representations. Howev... | ['and Alexander G. Hauptmann', 'Wenhe Liu', 'Lijun Yu', 'Yijun Qian'] | 2022-03-28 | null | null | null | eccv-2022-10 | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 4.95860010e-01 1.25873485e-03 -3.81794870e-01 -3.58874798e-01
-5.89493275e-01 -2.80282825e-01 6.32092476e-01 -2.49115393e-01
-5.08715771e-02 5.01090884e-01 4.78340149e-01 3.56602192e-01
-1.87456414e-01 -6.58166468e-01 -7.54158199e-01 -6.45755291e-01
1.13427900e-01 2.28051692e-01 6.82458639e-01 -2.82491475... | [8.439752578735352, 0.803927481174469] |
c8534324-bdd9-4bc6-bb56-7c61d6013a54 | efficient-solution-of-portfolio-optimization | 2306.12639 | null | https://arxiv.org/abs/2306.12639v1 | https://arxiv.org/pdf/2306.12639v1.pdf | Efficient Solution of Portfolio Optimization Problems via Dimension Reduction and Sparsification | The Markowitz mean-variance portfolio optimization model aims to balance expected return and risk when investing. However, there is a significant limitation when solving large portfolio optimization problems efficiently: the large and dense covariance matrix. Since portfolio performance can be potentially improved by c... | ['Hande Y. Benson', 'Cassidy K. Buhler'] | 2023-06-22 | null | null | null | null | ['dimensionality-reduction', 'portfolio-optimization'] | ['methodology', 'time-series'] | [-1.13647938e-01 1.78029984e-01 -2.07771569e-01 -2.44036868e-01
-8.22150171e-01 -8.64688873e-01 2.42997352e-02 -8.47902671e-02
-1.91978097e-01 6.77390099e-01 3.32011342e-01 -9.06718016e-01
-7.24291384e-01 -8.06444943e-01 -5.35772622e-01 -5.06548882e-01
-1.59976661e-01 5.77934384e-01 -3.74062330e-01 9.92082506... | [4.990437030792236, 3.9385507106781006] |
73686482-cf8d-4d79-b27c-28b1c5f0b183 | unsupervised-part-based-disentangling-of | 1903.06946 | null | https://arxiv.org/abs/1903.06946v3 | https://arxiv.org/pdf/1903.06946v3.pdf | Unsupervised Part-Based Disentangling of Object Shape and Appearance | Large intra-class variation is the result of changes in multiple object characteristics. Images, however, only show the superposition of different variable factors such as appearance or shape. Therefore, learning to disentangle and represent these different characteristics poses a great challenge, especially in the uns... | ['Björn Ommer', 'Leonard Bereska', 'Timo Milbich', 'Dominik Lorenz'] | 2019-03-16 | unsupervised-part-based-disentangling-of-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Lorenz_Unsupervised_Part-Based_Disentangling_of_Object_Shape_and_Appearance_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Lorenz_Unsupervised_Part-Based_Disentangling_of_Object_Shape_and_Appearance_CVPR_2019_paper.pdf | cvpr-2019-6 | ['unsupervised-facial-landmark-detection'] | ['computer-vision'] | [ 6.35945201e-01 1.46108091e-01 -2.17710763e-01 -4.36035484e-01
-8.14329743e-01 -9.69198167e-01 9.44572866e-01 -6.00303411e-02
-4.23380621e-02 4.83700246e-01 1.35354385e-01 3.75410229e-01
-1.32881179e-02 -3.21056873e-01 -9.29405034e-01 -8.61056507e-01
3.62499684e-01 9.50971365e-01 1.65951550e-01 -5.22434302... | [8.55988883972168, -2.853766679763794] |
c2a7ddb1-9103-491e-9a24-37410e6e8d76 | l-hydra-multi-head-physics-informed-neural | 2301.02152 | null | https://arxiv.org/abs/2301.02152v1 | https://arxiv.org/pdf/2301.02152v1.pdf | L-HYDRA: Multi-Head Physics-Informed Neural Networks | We introduce multi-head neural networks (MH-NNs) to physics-informed machine learning, which is a type of neural networks (NNs) with all nonlinear hidden layers as the body and multiple linear output layers as multi-head. Hence, we construct multi-head physics-informed neural networks (MH-PINNs) as a potent tool for mu... | ['George Em Karniadakis', 'Zongren Zou'] | 2023-01-05 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-2.10015029e-01 4.70794030e-02 9.55613032e-02 -2.30673775e-01
-1.01876497e+00 -2.57368624e-01 8.98567736e-01 -2.06161693e-01
-2.93300122e-01 9.54291046e-01 -2.45538782e-02 -3.63447964e-01
-4.57685918e-01 -9.60814238e-01 -1.15447581e+00 -1.10266757e+00
4.44169194e-02 9.66121912e-01 1.18805155e-01 3.04543711... | [7.0876078605651855, 3.749976634979248] |
4d6d59e7-d5d2-42b5-b552-a0d1e66dc178 | tree-based-representation-and-generation-of | 2302.07974 | null | https://arxiv.org/abs/2302.07974v1 | https://arxiv.org/pdf/2302.07974v1.pdf | Tree-Based Representation and Generation of Natural and Mathematical Language | Mathematical language in scientific communications and educational scenarios is important yet relatively understudied compared to natural languages. Recent works on mathematical language focus either on representing stand-alone mathematical expressions, especially in their natural tree format, or mathematical reasoning... | ['Andrew Lan', 'Alexander Scarlatos'] | 2023-02-15 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 5.15811503e-01 3.51984471e-01 -3.54818180e-02 -4.76457775e-01
-3.40070754e-01 -9.46268618e-01 8.75302553e-01 4.71607238e-01
-2.06313357e-01 7.25836873e-01 3.86175215e-01 -9.67968464e-01
6.13947213e-02 -1.13396072e+00 -9.72597718e-01 -7.86904022e-02
5.75142168e-02 3.57450306e-01 -4.80917454e-01 -2.64000118... | [9.647650718688965, 7.3288092613220215] |
3fa09bf1-bfb7-4306-8616-341903ae93ea | generating-formulaic-text-by-splicing | 2101.08248 | null | https://arxiv.org/abs/2101.08248v4 | https://arxiv.org/pdf/2101.08248v4.pdf | Data-to-text Generation by Splicing Together Nearest Neighbors | We propose to tackle data-to-text generation tasks by directly splicing together retrieved segments of text from "neighbor" source-target pairs. Unlike recent work that conditions on retrieved neighbors but generates text token-by-token, left-to-right, we learn a policy that directly manipulates segments of neighbor te... | ['Karl Stratos', 'Arturs Backurs', 'Sam Wiseman'] | 2021-01-20 | null | https://aclanthology.org/2021.emnlp-main.352 | https://aclanthology.org/2021.emnlp-main.352.pdf | emnlp-2021-11 | ['conditional-text-generation'] | ['natural-language-processing'] | [ 7.32710779e-01 9.08502460e-01 -3.25592071e-01 -4.92124856e-01
-1.82358849e+00 -1.19314694e+00 8.40561628e-01 1.66813523e-01
-3.57475013e-01 1.21488965e+00 4.89442021e-01 -5.74473977e-01
2.91794151e-01 -1.13145077e+00 -1.19828391e+00 -4.72896248e-01
1.62825048e-01 8.71794522e-01 2.73163915e-02 -2.91369528... | [11.480525970458984, 8.974837303161621] |
ca0655a7-ae99-4c01-a247-f21cdcb7c113 | multiscale-combinatorial-grouping-for-image | 1503.00848 | null | http://arxiv.org/abs/1503.00848v4 | http://arxiv.org/pdf/1503.00848v4.pdf | Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation | We propose a unified approach for bottom-up hierarchical image segmentation
and object proposal generation for recognition, called Multiscale Combinatorial
Grouping (MCG). For this purpose, we first develop a fast normalized cuts
algorithm. We then propose a high-performance hierarchical segmenter that makes
effective ... | ['Jordi Pont-Tuset', 'Jonathan T. Barron', 'Pablo Arbelaez', 'Jitendra Malik', 'Ferran Marques'] | 2015-03-03 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 6.85313568e-02 -4.43128198e-02 -2.54439890e-01 -4.04290468e-01
-1.34035838e+00 -5.31249404e-01 4.12532747e-01 1.01716653e-01
-3.59993488e-01 1.93382874e-01 -1.49297252e-01 -2.81067818e-01
2.46910349e-01 -6.11383855e-01 -4.13270563e-01 -4.66297746e-01
4.64847684e-02 6.81564391e-01 1.09331751e+00 5.49988598... | [9.435242652893066, 0.2911987602710724] |
7c490637-de0e-43ae-911a-aeacc431788d | anomaly-detection-in-surveillance-videos | 2206.01524 | null | https://arxiv.org/abs/2206.01524v2 | https://arxiv.org/pdf/2206.01524v2.pdf | Anomaly detection in surveillance videos using transformer based attention model | Surveillance footage can catch a wide range of realistic anomalies. This research suggests using a weakly supervised strategy to avoid annotating anomalous segments in training videos, which is time consuming. In this approach only video level labels are used to obtain frame level anomaly scores. Weakly supervised vide... | ['Sonali Agarwal', 'Sanjay Kumar Sonbhadra', 'Narinder Singh Punn', 'Kapil Deshpande'] | 2022-06-03 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 6.75986186e-02 -3.14397842e-01 2.77651791e-02 -4.64706272e-01
-3.09620678e-01 -4.01381791e-01 4.43233341e-01 1.26133695e-01
-3.16115111e-01 4.23627734e-01 5.82917258e-02 -1.44977078e-01
1.68138579e-01 -4.81063485e-01 -7.92665601e-01 -7.81034350e-01
-3.25413138e-01 -1.81157008e-01 4.94511306e-01 -1.41510993... | [7.859673976898193, 1.534198522567749] |
fc5f4f39-e0e2-4620-89c1-0add7963a59b | can-we-evaluate-domain-adaptation-models | 2305.18712 | null | https://arxiv.org/abs/2305.18712v1 | https://arxiv.org/pdf/2305.18712v1.pdf | Can We Evaluate Domain Adaptation Models Without Target-Domain Labels? A Metric for Unsupervised Evaluation of Domain Adaptation | Unsupervised domain adaptation (UDA) involves adapting a model trained on a label-rich source domain to an unlabeled target domain. However, in real-world scenarios, the absence of target-domain labels makes it challenging to evaluate the performance of deep models after UDA. Additionally, prevailing UDA methods typica... | ['Lihua Xie', 'Yuecong Xu', 'Hanjie Qian', 'Jianfei Yang'] | 2023-05-30 | null | null | null | null | ['unsupervised-domain-adaptation'] | ['methodology'] | [ 2.41861045e-01 -1.86986610e-01 -2.08543897e-01 -2.89807409e-01
-8.94612491e-01 -1.02752531e+00 7.48769581e-01 1.67739801e-02
-3.52048934e-01 6.26400054e-01 6.05428480e-02 -2.61629313e-01
-5.70512190e-02 -7.14001298e-01 -6.90709651e-01 -7.65376806e-01
2.78529495e-01 3.36383790e-01 7.69759249e-03 1.32106528... | [10.213519096374512, 3.0953054428100586] |
d9ecd320-035d-435c-b8ff-059ba61cef2b | generalized-face-anti-spoofing-via-multi-task | 2211.15955 | null | https://arxiv.org/abs/2211.15955v1 | https://arxiv.org/pdf/2211.15955v1.pdf | Generalized Face Anti-Spoofing via Multi-Task Learning and One-Side Meta Triplet Loss | With the increasing variations of face presentation attacks, model generalization becomes an essential challenge for a practical face anti-spoofing system. This paper presents a generalized face anti-spoofing framework that consists of three tasks: depth estimation, face parsing, and live/spoof classification. With the... | ['Shang-Hong Lai', 'Chien-Yi Wang', 'Chu-Chun Chuang'] | 2022-11-29 | null | null | null | null | ['face-parsing', 'face-anti-spoofing'] | ['computer-vision', 'computer-vision'] | [ 4.67583716e-01 -1.24827541e-01 -5.34739316e-01 -2.77509809e-01
-4.14793104e-01 -3.78772378e-01 5.46956897e-01 -5.83924830e-01
4.62795943e-02 4.97341603e-01 -2.29509428e-01 -4.00391161e-01
2.04341486e-01 -7.29686439e-01 -5.01564622e-01 -9.48852956e-01
-2.08414420e-01 1.56142071e-01 2.32547998e-01 -1.43380269... | [13.003393173217773, 1.237642765045166] |
00504c38-7c04-4e43-9282-60868b8d6ef5 | parametric-implicit-face-representation-for-1 | 2306.07579 | null | https://arxiv.org/abs/2306.07579v1 | https://arxiv.org/pdf/2306.07579v1.pdf | Parametric Implicit Face Representation for Audio-Driven Facial Reenactment | Audio-driven facial reenactment is a crucial technique that has a range of applications in film-making, virtual avatars and video conferences. Existing works either employ explicit intermediate face representations (e.g., 2D facial landmarks or 3D face models) or implicit ones (e.g., Neural Radiance Fields), thus suffe... | ['Guanbin Li', 'Yipeng Qin', 'Peiwen Lai', 'Ricong Huang'] | 2023-06-13 | parametric-implicit-face-representation-for | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Parametric_Implicit_Face_Representation_for_Audio-Driven_Facial_Reenactment_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Parametric_Implicit_Face_Representation_for_Audio-Driven_Facial_Reenactment_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-inpainting'] | ['computer-vision'] | [ 3.71551871e-01 3.98323536e-01 -1.06929906e-01 -4.96736318e-01
-5.16707659e-01 -3.93217444e-01 5.64643145e-01 -7.07668066e-01
1.23424686e-01 6.02997541e-01 4.10897911e-01 1.75406694e-01
-1.81216896e-01 -6.54224813e-01 -8.01084995e-01 -7.83194780e-01
1.26262024e-01 7.70464838e-02 -2.11753815e-01 -4.52463627... | [12.944238662719727, -0.35720232129096985] |
ec548eae-519f-4387-a795-1a57efbfd141 | an-efficient-distributed-learning-algorithm | 1310.8418 | null | http://arxiv.org/abs/1310.8418v4 | http://arxiv.org/pdf/1310.8418v4.pdf | An efficient distributed learning algorithm based on effective local functional approximations | Scalable machine learning over big data is an important problem that is
receiving a lot of attention in recent years. On popular distributed
environments such as Hadoop running on a cluster of commodity machines,
communication costs are substantial and algorithms need to be designed suitably
considering those costs. In... | ['Leon Bottou', 'Dhruv Mahajan', 'S. Sundararajan', 'S. Sathiya Keerthi', 'Nikunj Agrawal'] | 2013-10-31 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-6.93200648e-01 -2.46406436e-01 5.13404906e-02 -6.50729954e-01
-1.17845333e+00 -3.05947304e-01 1.88855946e-01 2.87431806e-01
-7.79263198e-01 8.37348104e-01 -3.23149562e-01 -1.56064451e-01
-3.05296898e-01 -1.02131641e+00 -9.07969296e-01 -1.05713725e+00
-9.32356045e-02 9.25867021e-01 1.15174957e-01 -5.99090047... | [6.293936252593994, 4.988681316375732] |
8eb3103b-4579-4821-83a5-62107c64cf4d | skeletal-video-anomaly-detection-using-deep | 2301.00114 | null | https://arxiv.org/abs/2301.00114v2 | https://arxiv.org/pdf/2301.00114v2.pdf | Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions | The existing methods for video anomaly detection mostly utilize videos containing identifiable facial and appearance-based features. The use of videos with identifiable faces raises privacy concerns, especially when used in a hospital or community-based setting. Appearance-based features can also be sensitive to pixel-... | ['Shehroz S. Khan', 'Alex Mihailidis', 'Pratik K. Mishra'] | 2022-12-31 | null | null | null | null | ['video-anomaly-detection'] | ['computer-vision'] | [ 3.25016469e-01 -7.94360321e-03 -1.08933970e-01 -4.50175583e-01
-1.70674354e-01 -4.23645735e-01 2.46293366e-01 3.67185891e-01
-5.23390532e-01 4.01484400e-01 3.44151072e-02 -9.21713039e-02
1.61837161e-01 -5.11091888e-01 -7.00682342e-01 -9.13805842e-01
-4.69802767e-01 -3.28481823e-01 1.16035223e-01 2.28551731... | [7.854331016540527, 1.3986703157424927] |
c776f3f0-e701-4c6b-aed1-1c05011dee6d | extracting-clinician-s-goals-by-what-if | 2110.15165 | null | https://arxiv.org/abs/2110.15165v3 | https://arxiv.org/pdf/2110.15165v3.pdf | Extracting Expert's Goals by What-if Interpretable Modeling | Although reinforcement learning (RL) has tremendous success in many fields, applying RL to real-world settings such as healthcare is challenging when the reward is hard to specify and no exploration is allowed. In this work, we focus on recovering clinicians' rewards in treating patients. We incorporate the what-if rea... | ['Anna Goldenberg', 'Rich Caruana', 'George Alexandru Adam', 'Chun-Hao Chang'] | 2021-10-28 | null | null | null | null | ['additive-models'] | ['methodology'] | [-2.29421128e-02 8.01974595e-01 -8.23320687e-01 -3.65839750e-01
-7.93883502e-01 -2.15192258e-01 5.42523712e-02 3.95104229e-01
-5.58748782e-01 1.25428140e+00 5.69350362e-01 -8.23320985e-01
-4.21788484e-01 -3.62573087e-01 -6.28247917e-01 -4.08581227e-01
-2.28575245e-01 7.80302107e-01 -5.69916964e-01 2.49056257... | [4.038642883300781, 2.6845877170562744] |
76466212-75cd-442d-a840-22689aea7a14 | a-two-stage-data-association-approach-for-3d | 2101.08684 | null | https://arxiv.org/abs/2101.08684v1 | https://arxiv.org/pdf/2101.08684v1.pdf | A two-stage data association approach for 3D Multi-object Tracking | Multi-object tracking (MOT) is an integral part of any autonomous driving pipelines because itproduces trajectories which has been taken by other moving objects in the scene and helps predicttheir future motion. Thanks to the recent advances in 3D object detection enabled by deep learning,track-by-detection has become ... | ['Vincent Frémont', 'Minh-Quan Dao'] | 2021-01-21 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [-3.19922954e-01 -3.42829138e-01 -3.26029330e-01 2.13094261e-02
-6.68533564e-01 -6.50178492e-01 1.06128383e+00 2.00605497e-01
-7.93426216e-01 4.83247280e-01 -3.89147311e-01 -2.92315066e-01
-2.05284759e-01 -5.87154627e-01 -9.56326127e-01 -9.17697787e-01
4.39667627e-02 1.10958755e+00 1.06716466e+00 -2.05771267... | [6.606133937835693, -2.2527785301208496] |
6399c356-3d55-4da2-a1e3-877627045829 | fdvts-s-solution-for-2nd-cov19d-competition | 2207.01758 | null | https://arxiv.org/abs/2207.01758v1 | https://arxiv.org/pdf/2207.01758v1.pdf | FDVTS's Solution for 2nd COV19D Competition on COVID-19 Detection and Severity Analysis | This paper presents our solution for the 2nd COVID-19 Competition, occurring in the framework of the AIMIA Workshop in the European Conference on Computer Vision (ECCV 2022). In our approach, we employ an effective 3D Contrastive Mixup Classification network for COVID-19 diagnosis on chest CT images, which is composed ... | ['Yuejie Zhang', 'Rui Feng', 'Jilan Xu', 'Junlin Hou'] | 2022-07-05 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 3.24591100e-01 1.18910149e-02 -1.68820411e-01 1.83212459e-02
-1.36594343e+00 -3.70311737e-01 5.93759775e-01 3.73611093e-01
-6.38448536e-01 4.75043952e-01 1.11559853e-01 -2.23326281e-01
8.86700824e-02 -3.49594265e-01 -3.03245008e-01 -5.06377399e-01
6.76617324e-02 6.67171419e-01 4.14465278e-01 1.50683805... | [15.358924865722656, -1.9139043092727661] |
e2f498c7-420e-4cc1-b686-6627a7429770 | causal-modeling-of-soil-processes-for | 2211.05675 | null | https://arxiv.org/abs/2211.05675v1 | https://arxiv.org/pdf/2211.05675v1.pdf | Causal Modeling of Soil Processes for Improved Generalization | Measuring and monitoring soil organic carbon is critical for agricultural productivity and for addressing critical environmental problems. Soil organic carbon not only enriches nutrition in soil, but also has a gamut of co-benefits such as improving water storage and limiting physical erosion. Despite a litany of work ... | ['Ranveer Chandra', 'Emre Kiciman', 'John Crawford', 'Eduardo Rodrigues', 'Sara Malvar', 'Andy Neal', 'Swati Sharma', 'Somya Sharma'] | 2022-11-10 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 5.41942596e-01 6.95533976e-02 -9.24180388e-01 4.72773537e-02
-1.79550216e-01 -6.20900393e-01 4.33076769e-01 6.68009937e-01
-8.08997750e-02 1.30940735e+00 2.85599649e-01 -1.02905357e+00
-5.41835308e-01 -1.34333217e+00 -9.91638601e-01 -7.01716781e-01
-3.04369539e-01 9.56892781e-03 2.93714106e-01 1.22091189... | [9.316872596740723, -1.3645097017288208] |
9280fd5b-191e-46bc-bf1c-1ea728466e29 | masking-by-moving-learning-distraction-free | 1909.03752 | null | https://arxiv.org/abs/1909.03752v4 | https://arxiv.org/pdf/1909.03752v4.pdf | Masking by Moving: Learning Distraction-Free Radar Odometry from Pose Information | This paper presents an end-to-end radar odometry system which delivers robust, real-time pose estimates based on a learned embedding space free of sensing artefacts and distractor objects. The system deploys a fully differentiable, correlation-based radar matching approach. This provides the same level of interpretabil... | ['Ingmar Posner', 'Dan Barnes', 'Rob Weston'] | 2019-09-09 | null | null | null | null | ['radar-odometry'] | ['robots'] | [ 1.82014808e-01 3.71318400e-01 1.80682689e-01 -8.39578211e-01
-1.18284059e+00 -5.18352509e-01 8.93670201e-01 -1.00995727e-01
-6.09649241e-01 8.70537758e-01 1.06037296e-02 -2.62870669e-01
-5.75279832e-01 -8.31032515e-01 -6.09824061e-01 -4.94419426e-01
-5.78303337e-01 9.73224401e-01 3.26046973e-01 -4.73558962... | [7.3736653327941895, -2.116434335708618] |
7f05e6ab-cd0c-42e0-979c-cfb8e6370894 | 3d-u-net-learning-dense-volumetric | 1606.06650 | null | http://arxiv.org/abs/1606.06650v1 | http://arxiv.org/pdf/1606.06650v1.pdf | 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation | This paper introduces a network for volumetric segmentation that learns from
sparsely annotated volumetric images. We outline two attractive use cases of
this method: (1) In a semi-automated setup, the user annotates some slices in
the volume to be segmented. The network learns from these sparse annotations
and provide... | ['Soeren S. Lienkamp', 'Özgün Çiçek', 'Thomas Brox', 'Olaf Ronneberger', 'Ahmed Abdulkadir'] | 2016-06-21 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 2.40831092e-01 7.73052335e-01 -2.44437128e-01 -6.73583984e-01
-5.72987795e-01 -4.80476648e-01 2.52767682e-01 3.13026637e-01
-7.63972044e-01 6.99856043e-01 -2.88046539e-01 -5.51010035e-02
4.63512301e-01 -7.43928134e-01 -1.05241179e+00 -4.27052855e-01
-3.07239175e-01 1.05311596e+00 3.23946536e-01 1.79548293... | [14.627257347106934, -2.2278640270233154] |
8462d3ac-5880-4e77-9282-0e55d6b0df6d | a-biologically-inspired-evaluation-of | 2208.09658 | null | https://arxiv.org/abs/2208.09658v2 | https://arxiv.org/pdf/2208.09658v2.pdf | A biologically-inspired multi-modal evaluation of molecular generative machine learning | While generative models have recently become ubiquitous in many scientific areas, less attention has been paid to their evaluation. For molecular generative models, the state-of-the-art examines their output in isolation or in relation to its input. However, their biological and functional properties, such as ligand-ta... | ['Siamac Fazli', 'Vsevolod Peshkov', 'Ferdinand Molnár', 'Rustam Zhumagambetov', 'Karina Pats', 'Abylay Salimzhanov', 'Anuar Suleimenov', 'Albina Li', 'Maxim Mametkulov', 'Mukhamejan Karatayev', 'Alisher Amanatay', 'Abay Artykbayev', 'Elizaveta Vinogradova'] | 2022-08-20 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 2.63992310e-01 -3.39692123e-02 -1.92500725e-01 -4.80816737e-02
-7.43978679e-01 -7.64047921e-01 8.82953703e-01 5.07464111e-01
-3.27227116e-01 1.31698334e+00 -4.32881750e-02 -3.28789741e-01
-3.96562606e-01 -8.60849738e-01 -6.96271241e-01 -1.16897774e+00
7.02598989e-02 7.64804900e-01 9.92644802e-02 -2.30324239... | [5.02547550201416, 5.579461574554443] |
fd2257f8-d51a-4c23-8174-1a5db63d30d8 | attentive-pooling-for-group-activity | 2208.14847 | null | https://arxiv.org/abs/2208.14847v1 | https://arxiv.org/pdf/2208.14847v1.pdf | Attentive pooling for Group Activity Recognition | In group activity recognition, hierarchical framework is widely adopted to represent the relationships between individuals and their corresponding group, and has achieved promising performance. However, the existing methods simply employed max/average pooling in this framework, which ignored the distinct contributions ... | ['Zhizhong Zhang', 'Yongqiang Tang', 'Wensheng Zhang', 'Yuan Xie', 'Ding Li'] | 2022-08-31 | null | null | null | null | ['group-activity-recognition'] | ['computer-vision'] | [ 3.03326815e-01 1.12944625e-01 -3.21731627e-01 -2.90326089e-01
-2.02442229e-01 1.71315849e-01 8.04484367e-01 -1.74366646e-02
-4.31499749e-01 7.13734329e-01 9.42777693e-01 6.26543701e-01
-2.90470839e-01 -8.54194164e-01 -2.47370407e-01 -1.02049780e+00
-2.17912927e-01 -1.11021481e-01 4.93318230e-01 1.43879026... | [8.172174453735352, 0.6370940208435059] |
247a6fca-a0fe-407a-ac09-1c1fd3d3f5bd | facial-emotion-recognition-state-of-the-art | 2105.03588 | null | https://arxiv.org/abs/2105.03588v1 | https://arxiv.org/pdf/2105.03588v1.pdf | Facial Emotion Recognition: State of the Art Performance on FER2013 | Facial emotion recognition (FER) is significant for human-computer interaction such as clinical practice and behavioral description. Accurate and robust FER by computer models remains challenging due to the heterogeneity of human faces and variations in images such as different facial pose and lighting. Among all techn... | ['Zhuofa Chen', 'Yousif Khaireddin'] | 2021-05-08 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [-7.34533593e-02 -1.51514009e-01 -1.36302695e-01 -6.54584587e-01
-5.59363604e-01 -1.25217913e-02 3.71744074e-02 -3.15062582e-01
-4.70953763e-01 5.95690966e-01 -2.36000776e-01 1.09728865e-01
9.96524245e-02 -4.04081702e-01 -3.29512686e-01 -4.71981674e-01
-2.11982012e-01 2.12142020e-01 -3.07379991e-01 -2.30495259... | [13.528135299682617, 1.6870688199996948] |
f86f380d-66d6-41ae-b6d5-da26b6dcb387 | scoot-a-perceptual-metric-for-facial-sketches | 1908.08433 | null | https://arxiv.org/abs/1908.08433v2 | https://arxiv.org/pdf/1908.08433v2.pdf | Scoot: A Perceptual Metric for Facial Sketches | Human visual system has the strong ability to quick assess the perceptual similarity between two facial sketches. However, existing two widely-used facial sketch metrics, e.g., FSIM and SSIM fail to address this perceptual similarity in this field. Recent study in facial modeling area has verified that the inclusion of... | ['Ming-Ming Cheng', 'Yu-Huan Wu', 'Shengchuan Zhang', 'Deng-Ping Fan', 'Paul L. Rosin', 'Yun Liu', 'Bo Ren', 'Rongrong Ji'] | 2019-08-21 | scoot-a-perceptual-metric-for-facial-sketches-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Fan_Scoot_A_Perceptual_Metric_for_Facial_Sketches_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Fan_Scoot_A_Perceptual_Metric_for_Facial_Sketches_ICCV_2019_paper.pdf | iccv-2019-10 | ['face-sketch-synthesis'] | ['computer-vision'] | [-1.08909167e-01 -5.46819925e-01 -2.16291279e-01 -5.49093008e-01
-2.69555539e-01 -3.08641136e-01 9.16836500e-01 -1.35723159e-01
7.21810609e-02 3.13674927e-01 2.03863055e-01 7.10978592e-03
-2.33881325e-01 -6.35608077e-01 -1.84582815e-01 -4.27739620e-01
1.42790675e-01 -2.28573412e-01 4.07450348e-01 -3.03371549... | [12.783318519592285, 0.2269609570503235] |
914a8051-2205-4a2c-ac41-aaf32ea14b88 | sibert-enhanced-chinese-pre-trained-language | null | null | https://aclanthology.org/2020.lrec-1.293 | https://aclanthology.org/2020.lrec-1.293.pdf | SiBert: Enhanced Chinese Pre-trained Language Model with Sentence Insertion | Pre-trained models have achieved great success in learning unsupervised language representations by self-supervised tasks on large-scale corpora. Recent studies mainly focus on how to fine-tune different downstream tasks from a general pre-trained model. However, some studies show that customized self-supervised tasks ... | ['Chenjie Cao', 'Xiuyan Jiang', 'Jiahao Chen'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['cloze-test'] | ['natural-language-processing'] | [ 6.67233467e-02 -2.59346604e-01 -2.17048451e-01 -6.19112194e-01
-1.03456187e+00 -4.26958442e-01 1.83901265e-01 3.37521434e-01
-6.42451763e-01 4.88561004e-01 5.24159312e-01 -4.57083762e-01
5.01822308e-02 -7.18440175e-01 -2.99688876e-01 -3.04124087e-01
3.38115185e-01 3.11605394e-01 2.70431548e-01 -4.94020879... | [10.824393272399902, 9.068069458007812] |
cb37fc90-b08c-4972-9ffc-4d781bc7cbd9 | boosted-prompt-ensembles-for-large-language | 2304.05970 | null | https://arxiv.org/abs/2304.05970v1 | https://arxiv.org/pdf/2304.05970v1.pdf | Boosted Prompt Ensembles for Large Language Models | Methods such as chain-of-thought prompting and self-consistency have pushed the frontier of language model reasoning performance with no additional training. To further improve performance, we propose a prompt ensembling method for large language models, which uses a small dataset to construct a set of few shot prompts... | ['Jimmy Ba', 'Andrew Wang', 'Michael R. Zhang', 'Silviu Pitis'] | 2023-04-12 | null | null | null | null | ['gsm8k'] | ['natural-language-processing'] | [ 1.13930799e-01 2.60506600e-01 6.09264597e-02 -7.38274872e-01
-1.28315699e+00 -7.02064812e-01 9.39201951e-01 4.64517474e-01
-6.47733569e-01 7.62063265e-01 4.01674271e-01 -6.01181924e-01
-1.14039367e-03 -5.25723696e-01 -4.81944829e-01 -5.50068438e-01
1.27865925e-01 7.55841970e-01 3.35010052e-01 -3.56988013... | [10.860560417175293, 8.227086067199707] |
81cab239-befe-4d2f-88fc-e78eb0ba2d53 | efficient-meshy-neural-fields-for-animatable | 2303.12965 | null | https://arxiv.org/abs/2303.12965v1 | https://arxiv.org/pdf/2303.12965v1.pdf | Efficient Meshy Neural Fields for Animatable Human Avatars | Efficiently digitizing high-fidelity animatable human avatars from videos is a challenging and active research topic. Recent volume rendering-based neural representations open a new way for human digitization with their friendly usability and photo-realistic reconstruction quality. However, they are inefficient for lon... | ['Jiwen Lu', 'Jie zhou', 'Xiu Li', 'Yansong Tang', 'Yiji Cheng', 'Xiaoke Huang'] | 2023-03-23 | null | null | null | null | ['inverse-rendering'] | ['computer-vision'] | [ 5.96995763e-02 1.66516885e-01 2.23971233e-01 -6.66788965e-02
-6.06758118e-01 -5.17493367e-01 6.31969392e-01 -4.56246942e-01
-1.34964362e-01 8.37200463e-01 -1.33899599e-01 -2.46538818e-01
1.57937720e-01 -1.13919568e+00 -1.13147175e+00 -5.68592787e-01
2.09839076e-01 7.26141632e-01 -1.80855347e-03 -5.03180981... | [9.148468017578125, -3.15118145942688] |
1963c01a-4db0-46d7-aa3f-aa895e2188df | skin-cancer-classification-using-inception | 2111.02402 | null | https://arxiv.org/abs/2111.02402v1 | https://arxiv.org/pdf/2111.02402v1.pdf | Skin Cancer Classification using Inception Network and Transfer Learning | Medical data classification is typically a challenging task due to imbalance between classes. In this paper, we propose an approach to classify dermatoscopic images from HAM10000 (Human Against Machine with 10000 training images) dataset, consisting of seven imbalanced types of skin lesions, with good precision and low... | ['Mauro Femminella', 'Gianluca Reali', 'Osvaldo Gervasi', 'Marco Simonetti', 'Damiano Perri', 'Priscilla Benedetti'] | 2021-11-03 | null | null | null | null | ['skin-cancer-classification'] | ['medical'] | [ 4.87475038e-01 -2.59965565e-03 -4.87003148e-01 -4.93955791e-01
-4.63736534e-01 -5.16726732e-01 2.39474282e-01 3.15631092e-01
-7.20391512e-01 9.76888299e-01 -4.82748806e-01 -3.92179847e-01
-2.89090365e-01 -7.40853131e-01 -3.76883954e-01 -5.51079392e-01
-1.66235670e-01 6.60447717e-01 8.42315406e-02 -6.53853863... | [15.658957481384277, -2.9720678329467773] |
18bbb742-fe8c-41af-bda3-3157bd2cfcf8 | domain-adversarial-graph-convolutional | 2204.05184 | null | https://arxiv.org/abs/2204.05184v3 | https://arxiv.org/pdf/2204.05184v3.pdf | Domain Adversarial Graph Convolutional Network Based on RSSI and Crowdsensing for Indoor Localization | In recent years, the use of WiFi fingerprints for indoor positioning has grown in popularity, largely due to the widespread availability of WiFi and the proliferation of mobile communication devices. However, many existing methods for constructing fingerprint datasets rely on labor-intensive and time-consuming processe... | ['Xuan Song', 'Ryosuke Shibasaki', 'Zipei Fan', 'Mingxin Zhang'] | 2022-04-06 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 2.27305129e-01 -1.17563337e-01 -1.43108457e-01 -4.81482536e-01
-7.54357696e-01 -8.32639933e-01 3.70336652e-01 -6.93696514e-02
-1.18862599e-01 8.51685584e-01 2.11317003e-01 -4.19494331e-01
-2.30820596e-01 -1.27749801e+00 -9.57011461e-01 -4.40064520e-01
-1.82859704e-01 2.73974240e-01 1.53527278e-02 -4.15832177... | [6.421818256378174, 0.8708997964859009] |
725abf83-ae7f-4371-a209-dba3db186584 | a-simple-information-based-approach-to-1 | 2201.12549 | null | https://arxiv.org/abs/2201.12549v1 | https://arxiv.org/pdf/2201.12549v1.pdf | A Simple Information-Based Approach to Unsupervised Domain-Adaptive Aspect-Based Sentiment Analysis | Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task which aims to extract the aspects from sentences and identify their corresponding sentiments. Aspect term extraction (ATE) is the crucial step for ABSA. Due to the expensive annotation for aspect terms, we often lack labeled target domain ... | ['Xiaojun Wan', 'Xiang Chen'] | 2022-01-29 | null | null | null | null | ['term-extraction', 'aspect-based-sentiment-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.46713027e-01 -1.74121365e-01 -5.16765751e-02 -6.62971377e-01
-1.05396080e+00 -8.56529474e-01 7.04301596e-01 2.28398383e-01
-4.66065705e-01 6.43465996e-01 1.70948971e-02 -2.58512765e-01
8.83149430e-02 -8.55846941e-01 -4.28549439e-01 -6.58991814e-01
4.98929203e-01 5.26278019e-01 4.16823208e-01 -6.08593166... | [11.372610092163086, 6.726204872131348] |
9f476898-b1c7-48f0-82c0-74c2e89efe1d | on-exploring-node-feature-and-graph-structure-1 | 2306.12726 | null | https://arxiv.org/abs/2306.12726v1 | https://arxiv.org/pdf/2306.12726v1.pdf | On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling | A pooling operation is essential for effective graph-level representation learning, where the node drop pooling has become one mainstream graph pooling technology. However, current node drop pooling methods usually keep the top-k nodes according to their significance scores, which ignore the graph diversity in terms of... | ['Tongliang Liu', 'Wenbin Hu', 'Bo Du', 'Liu Liu', 'Baosheng Yu', 'Yibing Zhan', 'Chuang Liu'] | 2023-06-22 | on-exploring-node-feature-and-graph-structure | https://openreview.net/forum?id=zc0YnpS90ug | https://openreview.net/pdf?id=zc0YnpS90ug | null | ['graph-classification'] | ['graphs'] | [ 1.31447330e-01 1.51189277e-02 -3.82953823e-01 2.79133506e-02
-5.10248125e-01 -5.39234698e-01 2.92887390e-01 7.12944031e-01
-2.30107665e-01 9.48974133e-01 6.93878159e-02 -2.32051432e-01
-2.98075616e-01 -9.21644628e-01 -6.57784939e-01 -8.84530544e-01
-2.97401369e-01 1.19122993e-02 5.40283382e-01 -1.46859840... | [7.080223560333252, 6.2690558433532715] |
1c37ae94-6250-49a4-81ba-939b5b6d1948 | improving-state-of-the-art-in-one-class-1 | 2203.07206 | null | https://arxiv.org/abs/2203.07206v1 | https://arxiv.org/pdf/2203.07206v1.pdf | Improving State-of-the-Art in One-Class Classification by Leveraging Unlabeled Data | When dealing with binary classification of data with only one labeled class data scientists employ two main approaches, namely One-Class (OC) classification and Positive Unlabeled (PU) learning. The former only learns from labeled positive data, whereas the latter also utilizes unlabeled data to improve the overall per... | ['Aleksei Shpilman', 'Dmitry Ivanov', 'Farid Bagirov'] | 2022-03-14 | improving-state-of-the-art-in-one-class | https://openreview.net/forum?id=4KOJ5XJ_z5W | https://openreview.net/pdf?id=4KOJ5XJ_z5W | null | ['one-class-classification'] | ['miscellaneous'] | [ 1.22576416e-01 2.88622588e-01 -6.84754252e-01 -4.47131306e-01
-1.10320508e+00 -4.93980318e-01 3.13162506e-01 1.15862399e-01
-2.98192561e-01 1.27092969e+00 -4.78409290e-01 -4.36117709e-01
6.14180490e-02 -6.02524042e-01 -5.36573887e-01 -1.13187611e+00
1.30477384e-01 8.12078893e-01 3.35298687e-01 2.14695483... | [9.30871868133545, 3.9140524864196777] |
85182508-1598-4f6d-9648-ab4632fd6349 | detector-free-structure-from-motion | 2306.15669 | null | https://arxiv.org/abs/2306.15669v1 | https://arxiv.org/pdf/2306.15669v1.pdf | Detector-Free Structure from Motion | We propose a new structure-from-motion framework to recover accurate camera poses and point clouds from unordered images. Traditional SfM systems typically rely on the successful detection of repeatable keypoints across multiple views as the first step, which is difficult for texture-poor scenes, and poor keypoint dete... | ['Xiaowei Zhou', 'Hujun Bao', 'QiXing Huang', 'Sida Peng', 'Yifan Wang', 'Jiaming Sun', 'Xingyi He'] | 2023-06-27 | null | null | null | null | ['keypoint-detection'] | ['computer-vision'] | [ 8.63265023e-02 -5.09926796e-01 8.62946361e-02 -2.86282897e-01
-9.59852159e-01 -5.36122978e-01 4.90893543e-01 -1.13181427e-01
-1.46171033e-01 6.08740896e-02 2.66123731e-02 -5.32847978e-02
-1.50946258e-02 -7.55795121e-01 -8.45444262e-01 -3.91393512e-01
5.26884139e-01 7.06644118e-01 8.32080960e-01 -2.35331357... | [7.789266109466553, -2.453009605407715] |
e2269792-45f7-4ea6-a5b6-72ef64b89860 | learning-to-generate-move-by-move-commentary | null | null | https://aclanthology.org/P18-1154 | https://aclanthology.org/P18-1154.pdf | Learning to Generate Move-by-Move Commentary for Chess Games from Large-Scale Social Forum Data | This paper examines the problem of generating natural language descriptions of chess games. We introduce a new large-scale chess commentary dataset and propose methods to generate commentary for individual moves in a chess game. The introduced dataset consists of more than 298K chess move-commentary pairs across 11K ch... | ['Taylor Berg-Kirkpatrick', 'Graham Neubig', 'Harsh Jhamtani', 'Varun Gangal', 'Eduard Hovy'] | 2018-07-01 | null | null | null | acl-2018-7 | ['game-of-chess'] | ['playing-games'] | [ 1.12148620e-01 6.22596025e-01 7.12396875e-02 -4.21308458e-01
-1.20894063e+00 -9.39896643e-01 1.02780771e+00 -6.26217900e-03
-2.48637334e-01 1.10639906e+00 1.10600901e+00 -3.09227854e-01
2.85477757e-01 -6.85005963e-01 -6.50233865e-01 -1.15847655e-01
5.04617095e-01 9.70290124e-01 2.17629850e-01 -1.24815738... | [11.791887283325195, 8.917034149169922] |
9cb2adbf-fe8d-4b63-9060-f838060bba12 | an-image-analogies-approach-for-multi-scale | 2007.11047 | null | https://arxiv.org/abs/2007.11047v1 | https://arxiv.org/pdf/2007.11047v1.pdf | An Image Analogies Approach for Multi-Scale Contour Detection | In this paper we deal with contour detection based on the recent image analogy principle which has been successfully used for super-resolution, texture and curves synthesis and interactive editing. Hand-drawn outlines are initially as benchmarks. Given such a reference image, we present a new method based on this exper... | ['Slimane Larabi', 'Neil M. Robertson'] | 2020-07-21 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 5.44300020e-01 4.33667116e-02 3.11436206e-01 -7.45991915e-02
-4.61529404e-01 -6.41267896e-01 5.42646587e-01 2.82624692e-01
-6.46290302e-01 7.65796840e-01 -4.14384484e-01 -2.34835204e-02
-9.54392254e-02 -1.08048856e+00 -4.43576217e-01 -6.26868665e-01
6.24389648e-02 5.27626574e-01 9.01567698e-01 -4.04025674... | [10.479490280151367, -1.8881138563156128] |
e89bf493-c18f-4506-8584-9baf90d90d88 | when-hyperspectral-image-classification-meets | 2306.08964 | null | https://arxiv.org/abs/2306.08964v1 | https://arxiv.org/pdf/2306.08964v1.pdf | When Hyperspectral Image Classification Meets Diffusion Models: An Unsupervised Feature Learning Framework | Learning effective spectral-spatial features is important for the hyperspectral image (HSI) classification task, but the majority of existing HSI classification methods still suffer from modeling complex spectral-spatial relations and characterizing low-level details and high-level semantics comprehensively. As a new c... | ['Tao Chen', 'Bin Wang', 'Tong He', 'Peng Ye', 'Jiayuan Fan', 'Jiamu Sheng', 'Jingyi Zhou'] | 2023-06-15 | null | null | null | null | ['classification-1'] | ['methodology'] | [ 4.54426825e-01 -6.73892498e-01 -3.89972597e-01 -4.91320968e-01
-6.30642235e-01 -4.59277332e-01 6.39051616e-01 1.75179049e-01
3.06250174e-02 5.37887216e-01 2.46309519e-01 1.58816323e-01
-6.98390245e-01 -1.28448558e+00 -2.91999340e-01 -1.26079178e+00
-3.38566840e-01 4.91382778e-02 3.31300765e-01 4.03246842... | [9.934417724609375, -1.5473088026046753] |
bdd3feb7-65f6-4df1-a662-59b174e1696a | reward-uncertainty-for-exploration-in-1 | 2205.12401 | null | https://arxiv.org/abs/2205.12401v1 | https://arxiv.org/pdf/2205.12401v1.pdf | Reward Uncertainty for Exploration in Preference-based Reinforcement Learning | Conveying complex objectives to reinforcement learning (RL) agents often requires meticulous reward engineering. Preference-based RL methods are able to learn a more flexible reward model based on human preferences by actively incorporating human feedback, i.e. teacher's preferences between two clips of behaviors. Howe... | ['Pieter Abbeel', 'Kimin Lee', 'Katherine Shu', 'Xinran Liang'] | 2022-05-24 | reward-uncertainty-for-exploration-in | https://openreview.net/forum?id=OWZVD-l-ZrC | https://openreview.net/pdf?id=OWZVD-l-ZrC | iclr-2022-4 | ['robot-manipulation'] | ['robots'] | [-1.56796664e-01 3.30511123e-01 -6.94704056e-01 -2.80616671e-01
-9.81970727e-01 -5.49839973e-01 4.58314478e-01 7.70728812e-02
-7.38549829e-01 1.19004595e+00 3.73933077e-01 -9.58415866e-02
-3.87687027e-01 -6.89683795e-01 -6.36697769e-01 -7.70551383e-01
-3.41652423e-01 6.50532246e-01 1.12095512e-01 -6.27110362... | [3.9662930965423584, 1.766076683998108] |
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