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

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發(fā)表于 2025-3-21 17:31:34 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Computer Vision – ACCV 2018
副標題14th Asian Conferenc
編輯C.V. Jawahar,Hongdong Li,Konrad Schindler
視頻videohttp://file.papertrans.cn/235/234121/234121.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computer Vision – ACCV 2018; 14th Asian Conferenc C.V. Jawahar,Hongdong Li,Konrad Schindler Conference proceedings 2019 Springer Nature Swi
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
關鍵詞artificial intelligence; computer vision; estimation; face recognition; Human-Computer Interaction (HCI)
版次1
doihttps://doi.org/10.1007/978-3-030-20876-9
isbn_softcover978-3-030-20875-2
isbn_ebook978-3-030-20876-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

書目名稱Computer Vision – ACCV 2018影響因子(影響力)




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FSNet: An Identity-Aware Generative Model for Image-Based Face Swappingmorphable models (3DMMs), and facial textures are replaced between the estimated three-dimensional (3D) geometries in two images of different individuals. However, the estimation of 3D geometries along with different lighting conditions using 3DMMs is still a difficult task. We herein represent the
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ScoringNet: Learning Key Fragment for Action Quality Assessment with Ranking Loss in Skilled Sportsting effective features and predicting reasonable scores for a long skilled sport video still beset researchers. In this paper, we introduce the ScoringNet, a novel network consisting of key fragment segmentation (KFS) and score prediction (SP), to address these two problems. To get the effective fe
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Style Transfer with Adversarial Learning for Cross-Dataset Person Re-identificationingle dataset but fail to generalize well on another datasets. The emerging problem mainly comes from style difference between two datasets. To address this problem, we propose a novel style transfer framework based on Generative Adversarial Networks (GAN) to generate target-style images. Specifical
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