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

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发表于 2025-3-21 17:07:35 | 显示全部楼层 |阅读模式
书目名称Computer Vision – ECCV 2024
副标题18th European Confer
编辑Aleš Leonardis,Elisa Ricci,Gül Varol
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Computer Vision – ECCV 2024; 18th European Confer Aleš 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. They 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
关键词artificial intelligence; computer networks; computer systems; computer vision; education; Human-Computer
版次1
doihttps://doi.org/10.1007/978-3-031-73347-5
isbn_softcover978-3-031-73346-8
isbn_ebook978-3-031-73347-5Series 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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,Exploring Guided Sampling of Conditional GANs,e FID score to 4.37. It is noteworthy that our sampling strategy sufficiently closes the gap between GANs and one-step diffusion models (.., with FID 4.02) under comparable model size. Code is available at ..
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,MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion, a diffusion decoder conditioned on the representations extracted by a semantic encoder. Random masking is applied to encoder inputs to introduce a information bottleneck and remove redundancy of skeletons. Furthermore, we theoretically demonstrate that our generative objective involves the contrast
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,TCC-Det: Temporarily Consistent Cues for Weakly-Supervised 3D Detection,and allows training using orders of magnitude more data than traditional fully-supervised methods..The method is evaluated on KITTI and Waymo Open datasets, where it outperforms all previous weakly-supervised methods and where it narrows the gap when compared to methods using human 3D labels..The so
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,FoundPose: Unseen Object Pose Estimation with Foundation Features,. Such descriptors carry stronger positional information than descriptors from the last layer, and we show their importance when semantic information is ambiguous due to object symmetries or a lack of texture. To avoid establishing correspondences against all object templates, we develop an efficien
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