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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/235/234129/234129.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; correlation anal
版次1
doihttps://doi.org/10.1007/978-3-030-69541-5
isbn_softcover978-3-030-69540-8
isbn_ebook978-3-030-69541-5Series 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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Lecture Notes in Physics Monographsdily available in our daily life, thermal cameras are expensive and less prevalent. It is costly to collect a large quantity of synchronous visible and thermal facial images. To tackle this paired training data bottleneck, we propose an unpaired multimodal facial expression recognition method, which
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https://doi.org/10.1007/3-540-36409-9be classified into coordinate regression methods and heatmap based methods. However, the former loses spatial information, resulting in poor performance while the latter suffers from large output size or high post-processing complexity. This paper proposes a new solution, Gaussian Vector, to preserv
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https://doi.org/10.1007/3-540-36409-9into two classes: top-down and bottom-up methods. Both of the two types of methods involve two stages, namely, person detection and joints detection. Conventionally, the two stages are implemented separately without considering their interactions between them, and this may inevitably cause some issu
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