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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 18:22:53 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)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/243/242303/242303.mp4
叢書(shū)名稱(chēng)Lecture Notes in Computer Science
圖書(shū)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 upto 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 and 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/10.1007/978-3-031-72907-2
isbn_softcover978-3-031-72906-5
isbn_ebook978-3-031-72907-2Series 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

書(shū)目名稱(chēng)Computer Vision – ECCV 2024影響因子(影響力)




書(shū)目名稱(chēng)Computer Vision – ECCV 2024影響因子(影響力)學(xué)科排名




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書(shū)目名稱(chēng)Computer Vision – ECCV 2024被引頻次




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書(shū)目名稱(chēng)Computer Vision – ECCV 2024年度引用




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書(shū)目名稱(chēng)Computer Vision – ECCV 2024讀者反饋




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https://doi.org/10.1007/978-3-642-59535-6 tokens and style mappers to learn and transform this editing direction to 3D latent space. To train LAE with multiple attributes, we use directional contrastive loss and style token loss. Furthermore, to ensure view consistency and identity preservation across different poses and attributes, we emp
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https://doi.org/10.1007/978-3-642-59535-6ted extensive experiments on two benchmarks: the low-resolution PKU-DDD17-Car dataset and the high-resolution DSEC dataset. Experimental results show that our method surpasses the state-of-the-art by an impressive margin of . on the DSEC dataset. Besides, our method exhibits significantly better rob
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