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Titlebook: Computer Vision – ACCV 2020 Workshops; 15th Asian Conferenc Imari Sato,Bohyung Han Conference proceedings 2021 Springer Nature Switzerland

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發(fā)表于 2025-3-21 16:21:26 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Computer Vision – ACCV 2020 Workshops
副標(biāo)題15th Asian Conferenc
編輯Imari Sato,Bohyung Han
視頻videohttp://file.papertrans.cn/235/234133/234133.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computer Vision – ACCV 2020 Workshops; 15th Asian Conferenc Imari Sato,Bohyung Han Conference proceedings 2021 Springer Nature Switzerland
描述This book constitutes the refereed post-conference proceedings of four workshops held at the 15th Asian Conference on Computer Vision, ACCV 2020, which was held in Kyoto, Japan, in November/ December 2020.*.The 13 papers were carefully reviewed and selected from the following two workshops: Machine Learning and Computing for Visual Semantic Analysis (MLCSA) and Multi-Visual-Modality Human Activity Understanding (MMHAU)..*The conference and workshops were held virtually..
出版日期Conference proceedings 2021
關(guān)鍵詞artificial intelligence; computer vision; deep learning; image analysis; image processing; image quality;
版次1
doihttps://doi.org/10.1007/978-3-030-69756-3
isbn_softcover978-3-030-69755-6
isbn_ebook978-3-030-69756-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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Unsupervised Multispectral and Hyperspectral Image Fusion with Deep Spatial and Spectral Priorsailed spectral distribution helping for numerous applications. However, existing HS imaging sensor can only obtain images with low spatial resolution. Thus fusing a low resolution hyperspectral (LR-HS) image with a high resolution (HR) RGB (or multispectral) image into a HR-HS image has received muc
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Cell Detection and Segmentation in Microscopy Images with Improved Mask R-CNNclinical practice, and automation of this task to develop computer aided system based on image processing and machine learning technique has been rapidly evolved for providing quantitative evaluation and mitigating burden and time of the biological experts. Automated cell/nuclei detection and segmen
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Multiview Similarity Learning for Robust Visual Clusteringesents encouraging performance on lots of applications. Nevertheless, the recent existing multiview similarity learning methods have two main drawbacks. On one hand, the comprehensive consensus similarity is learned based on previous fixed graphs learned from all views separately, which ignores the
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Real-Time Spatio-Temporal Action Localization via Learning Motion Representationination of optical flow and RGB significantly improves the performance, optical flow estimation brings a large amount of computational cost and the whole network is not end-to-end trainable. These shortcomings hinder the interactive fusion between motion information and RGB information, and greatly
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