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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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發(fā)表于 2025-3-21 16:20:33 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Computer Vision – ECCV 2020
副標(biāo)題16th European Confer
編輯Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm
視頻videohttp://file.papertrans.cn/235/234225/234225.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面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
關(guān)鍵詞computer networks; computer vision; education; face recognition; Human-Computer Interaction (HCI); image
版次1
doihttps://doi.org/10.1007/978-3-030-58452-8
isbn_softcover978-3-030-58451-1
isbn_ebook978-3-030-58452-8Series 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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https://doi.org/10.1007/978-1-349-00731-8comprises of two surrogates, one at the architecture level to improve sample efficiency and one at the weights level, through a supernet, to improve gradient descent training efficiency. On standard benchmark datasets (C10, C100, ImageNet), the resulting models, dubbed NSGANetV2, either match or out
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Studies in Economic and Social HistoryF), amenable to learning inter-dependency of correlated observations, with the newly devised temporal and spatial self-attention to learn the temporal evolution and spatial relational contexts of every actor in videos. Such a combination utilizes the global receptive fields of self-attention to cons
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Studies in Economic and Social Historyexamined how attention progresses to accomplish a task and whether it is reasonable. In this work, we propose an Attention with Reasoning capability (AiR) framework that uses attention to understand and improve the process leading to task outcomes. We first define an evaluation metric based on a seq
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IPO Capital Raising in the Global Economy,plenoptic function for a particular scene. In this paper, we present a new approach to novel view synthesis under time-varying illumination from such data. Our approach builds on the recent . (MPI) format for representing local light fields under fixed viewing conditions. We introduce a new . repres
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