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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 networks; computer systems; computer vision; education; face recognition; image analysis; image r
版次1
doihttps://doi.org/10.1007/978-3-030-58542-6
isbn_softcover978-3-030-58541-9
isbn_ebook978-3-030-58542-6Series 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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978-3-030-58541-9Springer Nature Switzerland AG 2020
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234213.jpg
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Leonard Sandin,Piet F. M. Verdonschott the perturbed image is incorrectly predicted by one deep neural network (DNN) model. The sparse adversarial attack involves two challenges, ., where to perturb, and how to determine the perturbation magnitude. Many existing works determined the perturbed positions manually or heuristically, and th
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https://doi.org/10.1007/978-1-4020-5493-8o overcome the problem of reconstructing regions in 3D that are occluded in the 2D image, we propose to learn this information from synthetically generated high-resolution data. To do this, we introduce a deep network architecture that is specifically designed for volumetric TSDF data by featuring a
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Amateur Theatre, Place and Place-Making,clouds in real time. Previous studies have proposed localization methods to estimate a camera pose using a line-cloud map for a single image or a reconstructed point cloud. These methods offer a scene privacy protection against the inversion attacks by converting a point cloud to a line cloud, which
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