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Titlebook: Computer Vision – ECCV 2020; 16th European Confer Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm Conference proceedings 2020 Springer Natur

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發(fā)表于 2025-3-21 19:03:44 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Computer Vision – ECCV 2020
副標(biāo)題16th European Confer
編輯Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm
視頻videohttp://file.papertrans.cn/235/234230/234230.mp4
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Computer Vision – ECCV 2020; 16th European Confer Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm Conference proceedings 2020 Springer Natur
描述The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic..The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..?..?.
出版日期Conference proceedings 2020
關(guān)鍵詞computer networks; computer vision; data security; education; face recognition; image analysis; image proc
版次1
doihttps://doi.org/10.1007/978-3-030-58592-1
isbn_softcover978-3-030-58591-4
isbn_ebook978-3-030-58592-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

書(shū)目名稱(chēng)Computer Vision – ECCV 2020影響因子(影響力)




書(shū)目名稱(chēng)Computer Vision – ECCV 2020影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Computer Vision – ECCV 2020網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Computer Vision – ECCV 2020網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Computer Vision – ECCV 2020被引頻次




書(shū)目名稱(chēng)Computer Vision – ECCV 2020被引頻次學(xué)科排名




書(shū)目名稱(chēng)Computer Vision – ECCV 2020年度引用




書(shū)目名稱(chēng)Computer Vision – ECCV 2020年度引用學(xué)科排名




書(shū)目名稱(chēng)Computer Vision – ECCV 2020讀者反饋




書(shū)目名稱(chēng)Computer Vision – ECCV 2020讀者反饋學(xué)科排名




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,Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and?Objects for 3D Hand Pose Egned (a) to evaluate the influence of both depth and color modalities on 3D hand pose estimation, under the presence or absence of objects; (b) to assess the generalisation abilities .. ?four main axes: shapes, articulations, viewpoints, and objects; (c) to explore the use of a synthetic hand models
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Disentangling Multiple Features in Video Sequences Using Gaussian Processes in Variational Autoencothe curvature of the data manifold to improve learning. Our experiments show that the combination of the improved representations with the novel loss function enable MGP-VAE to outperform the baselines in video prediction.
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SACA Net: Cybersickness Assessment of Individual Viewers for VR Content via Graph-Based Symptom Relmulus-response symptom features to effectively predict cybersickness by embedding relational characteristics with graph formulation. For validation, we utilize two public 360-degree video datasets that contain cybersickness scores and physiological signals. Experimental results show that the propose
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Know Your Surroundings: Exploiting Scene Information for Object Tracking,re propagated through the sequence and combined with the appearance model output to localize the target. Our network is learned to effectively utilize the scene information by directly maximizing tracking performance on video segments. The proposed approach sets a new state-of-the-art on 3 tracking
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