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Titlebook: Computer Vision – ACCV 2018; 14th Asian Conferenc C. V. Jawahar,Hongdong Li,Konrad Schindler Conference proceedings 2019 Springer Nature Sw

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书目名称Computer Vision – ACCV 2018
副标题14th Asian Conferenc
编辑C. V. Jawahar,Hongdong Li,Konrad Schindler
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Computer Vision – ACCV 2018; 14th Asian Conferenc C. V. Jawahar,Hongdong Li,Konrad Schindler Conference proceedings 2019 Springer Nature Sw
描述The six volume set LNCS 11361-11366 constitutes the proceedings of the 14.th. Asian Conference on Computer Vision, ACCV 2018, held in Perth, Australia, in December 2018. The total of 274 contributions was carefully reviewed and selected from 979 submissions during two rounds of reviewing and improvement. The papers focus on motion and tracking, segmentation and grouping, image-based modeling, dep learning, object recognition object recognition, object detection and categorization, vision and language, video analysis and event recognition, face and gesture analysis, statistical methods and learning, performance evaluation, medical image analysis, document analysis, optimization methods, RGBD and depth camera processing, robotic vision, applications of computer vision.
出版日期Conference proceedings 2019
关键词artificial intelligence; classification; computer networks; computer vision; estimation; face recognition
版次1
doihttps://doi.org/10.1007/978-3-030-20887-5
isbn_softcover978-3-030-20886-8
isbn_ebook978-3-030-20887-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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Hidden Aztecs and Absent Spaniards (CrossNet). We have creatively introduced the . structure, where the feature map of depthwise convolutions can be reused within the same . by cross connection. Such a design can make the CrossNet have high performance while less computing resource, especially suitable for mobile devices with very l
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Higher Education Research in Europetructured data like 3D point clouds containing irregular neighborhoods constantly breaks the grid-based data assumption. Therefore applying best-practices and design choices from 2D-image learning methods towards processing point clouds are not readily possible. In this work, we introduce a natural
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