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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 18:12:57 | 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. 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 2025
关键词artificial intelligence; computer networks; computer systems; computer vision; education; Human-Computer
版次1
doihttps://doi.org/10.1007/978-3-031-72664-4
isbn_softcover978-3-031-72663-7
isbn_ebook978-3-031-72664-4Series 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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https://doi.org/10.1007/978-3-031-72664-4artificial intelligence; computer networks; computer systems; computer vision; education; Human-Computer
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Ambulanzmanual Pädiatrie von A-Zhe same feature to achieve recognition invariance. However, visual recognition involves not only identifying . an object is but also understanding . it is presented. For example, seeing a car from the side versus head-on is crucial for deciding whether to stay put or jump out of the way. While unsup
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https://doi.org/10.1007/978-3-642-24683-8s to the success of CLIP and its variants. Several works have also used CLIP features for dense prediction tasks and have shown the emergence of open-set abilities. However, the contrastive objective used by these models only focuses on image-text alignment and does not incentivise image feature lea
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Ambulanzmanual Pädiatrie von A-Zwever, most previous approaches have struggled to accurately predict motions that are both realistic and coherent with past motion due to a lack of guidance on the latent distribution. In this paper, we introduce Semantic Latent Directions (SLD) as a solution to this challenge, aiming to constrain t
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Ambulanzmanual Pädiatrie von A-Zr the generation process through user queries) and . (.. visually grounding their predictions), which we deem essential for future adoption in clinical practice. While there have been efforts to tackle these issues, they are either limited in their interactivity by not supporting textual queries or
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