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Titlebook: Computer Vision – ECCV 2024; 18th European Confer Ale? Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

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發(fā)表于 2025-3-21 16:19:21 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Computer Vision – ECCV 2024
副標(biāo)題18th European Confer
編輯Ale? Leonardis,Elisa Ricci,Gül Varol
視頻videohttp://file.papertrans.cn/243/242339/242339.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computer Vision – ECCV 2024; 18th European Confer Ale? Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic
描述.The multi-volume set of LNCS books with volume numbers 15059 up to 15147 constitutes the refereed proceedings of the 18th European Conference on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024...The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 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; motion estimation...?.
出版日期Conference proceedings 2025
關(guān)鍵詞artificial intelligence; computer networks; computer systems; computer vision; education; Human-Computer
版次1
doihttps://doi.org/10.1007/978-3-031-72933-1
isbn_softcover978-3-031-72932-4
isbn_ebook978-3-031-72933-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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,DGInStyle: Domain-Generalizable Semantic Segmentation with?Image Diffusion Models and?Stylized Semang DGInStyle, we generate a diverse dataset of street scenes, train a domain-agnostic semantic segmentation model on it, and evaluate the model on multiple popular autonomous driving datasets. Our approach consistently increases the performance of several domain generalization methods compared to th
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,COSMU: Complete 3D Human Shape from?Monocular Unconstrained Images,e camera, we propose a novel framework to generate complete 3D human shapes. We introduce a novel module to generate 2D multi-view normal maps of the person registered with the target input image. The module consists of body part-based reference selection and body part-based registration. The genera
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