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Titlebook: Computer Vision – ACCV 2020; 15th Asian Conferenc Hiroshi Ishikawa,Cheng-Lin Liu,Jianbo Shi Conference proceedings 2021 Springer Nature Swi

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書目名稱Computer Vision – ACCV 2020
副標題15th Asian Conferenc
編輯Hiroshi Ishikawa,Cheng-Lin Liu,Jianbo Shi
視頻videohttp://file.papertrans.cn/235/234128/234128.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computer Vision – ACCV 2020; 15th Asian Conferenc Hiroshi Ishikawa,Cheng-Lin Liu,Jianbo Shi Conference proceedings 2021 Springer Nature Swi
描述The six volume set of LNCS 12622-12627 constitutes the proceedings of the 15th Asian Conference on Computer Vision, ACCV 2020, held in Kyoto, Japan, in November/ December 2020.*.The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics:..Part I: 3D computer vision; segmentation and grouping..Part II: low-level vision, image processing; motion and tracking..Part III: recognition and detection; optimization, statistical methods, and learning; robot vision.Part IV: deep learning for computer vision, generative models for computer vision..Part V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysis..Part VI: applications of computer vision; vision for X; datasets and performance analysis..*The conference was held virtually..
出版日期Conference proceedings 2021
關(guān)鍵詞artificial intelligence; biomedical image analysis; computer networks; computer vision; databases; image
版次1
doihttps://doi.org/10.1007/978-3-030-69535-4
isbn_softcover978-3-030-69534-7
isbn_ebook978-3-030-69535-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

書目名稱Computer Vision – ACCV 2020影響因子(影響力)




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書目名稱Computer Vision – ACCV 2020網(wǎng)絡(luò)公開度




書目名稱Computer Vision – ACCV 2020網(wǎng)絡(luò)公開度學科排名




書目名稱Computer Vision – ACCV 2020被引頻次




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https://doi.org/10.1007/978-3-319-48323-8rimental results with the OU-MVLP and CASIA-B datasets demonstrate the state-of-the-art performance of the proposed method for both gait identification and verification scenarios, a direct consequence of the explicitly disentangled pose and shape features produced by the proposed end-to-end model-ba
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