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Titlebook: Computer Vision – ECCV 2020; 16th European Confer Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm Conference proceedings 2020 Springer Natur

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书目名称Computer Vision – ECCV 2020
副标题16th European Confer
编辑Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Computer Vision – ECCV 2020; 16th European Confer Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm Conference proceedings 2020 Springer Natur
描述The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic..The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.. .. .
出版日期Conference proceedings 2020
关键词Computer Science; Informatics; Conference Proceedings; Research; Applications
版次1
doihttps://doi.org/10.1007/978-3-030-58604-1
isbn_softcover978-3-030-58603-4
isbn_ebook978-3-030-58604-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Computer Vision – ECCV 2020978-3-030-58604-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234219.jpg
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Cost Model for Pregel on GraphXown great process in unsupervised person re-identification (re-id). However, they have an intrinsic problem of modeling the in-camera variability of images successfully, that is, pedestrian features extracted from the same camera tend to be clustered into the same class. This often results in a non-
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Piotr Krzyżagórski,Tadeusz Morzyument images, only limited attempts exist on table structure recognition. Most existing literature on structure recognition depends on extraction of meta-features from the . document or on the optical character recognition (.) models to extract low-level layout features from the image. However, thes
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