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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 16:13:13 | 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/10.1007/978-3-031-73116-7
isbn_softcover978-3-031-73115-0
isbn_ebook978-3-031-73116-7Series 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
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242337.jpg
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978-3-031-73115-0The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Alternate Fractionation in Radiotherapytional datasets focus on driver or vehicle behavior, often neglecting pedestrian perspectives. To fill this gap, we introduce the WTS dataset, highlighting detailed behaviors of both vehicles and pedestrians across over 1.2. video events in over hundreds traffic scenarios. WTS integrates diverse per
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An Unlikely Couple: Fintry and Oldenburgthe brain. Incorporating Transformers with SNNs has shown promise for accuracy. However, they struggle to learn high-frequency patterns, such as moving edges and pixel-level brightness changes, because they rely on the global self-attention mechanism. Learning these high-frequency representations is
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Discussion: Who gets what, when, and how?lized datasets impedes the advancement of model development in this domain. To address this, we have developed the Pests and Diseases Tree dataset (PDT dataset). PDT dataset represents the first high-precision UAV-based dataset for targeted detection of tree pests and diseases, which is collected in
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https://doi.org/10.1007/978-3-658-32307-3t are generalizable to new materials. This opens the door to BRDF reconstructions from a variety of data sources. The success of our approach relies on the ability of hypernetworks to generate a robust representation of BRDFs and a set encoder that allows us to feed inputs of different sizes to the
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https://doi.org/10.1007/978-3-658-32307-3ton avalanche diode (SPAD) detects individual photons using an avalanche process; the raw photon data can then be processed to extract scene information under extremely low light, high dynamic range, and rapid motion. Yet, single-photon sensitivity in SPADs comes at a cost—each photon detection cons
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