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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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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/235/234220/234220.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 security; computer vision; education; face recognition; Human-Computer Interaction (HCI); image
版次1
doihttps://doi.org/10.1007/978-3-030-58580-8
isbn_softcover978-3-030-58579-2
isbn_ebook978-3-030-58580-8Series 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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Unsupervised Sketch to Photo Synthesis,s and visual details. We study unsupervised sketch to photo synthesis for the first time, learning from . sketch and photo data where the target photo for a sketch is unknown during training. Existing works only deal with either style difference or spatial deformation alone, synthesizing photos from
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SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification,eir unorganized nature – points are stored in an unordered way – makes them less suited to be processed by deep learning pipelines. In this paper, we propose a method for 3D object completion and classification based on point clouds. We introduce a new way of organizing the extracted features based
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Hierarchical Face Aging Through Disentangled Latent Characteristics, it, we design a novel facial age prior to guide the aging mechanism modeling. To explore the age effects on facial images, we propose a Disentangled Adversarial Autoencoder (DAAE) to disentangle the facial images into three independent factors: age, identity and extraneous information. To avoid the
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