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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書目名稱Computer Vision – ECCV 2024影響因子(影響力)




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




書目名稱Computer Vision – ECCV 2024網(wǎng)絡(luò)公開度




書目名稱Computer Vision – ECCV 2024網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computer Vision – ECCV 2024被引頻次




書目名稱Computer Vision – ECCV 2024被引頻次學(xué)科排名




書目名稱Computer Vision – ECCV 2024年度引用




書目名稱Computer Vision – ECCV 2024年度引用學(xué)科排名




書目名稱Computer Vision – ECCV 2024讀者反饋




書目名稱Computer Vision – ECCV 2024讀者反饋學(xué)科排名

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,Enhanced Sparsification via?Stimulative Training,press the importance of dropped weights, which is regarded as the suppressed sparsification paradigm. However, this paradigm inactivates the dropped parts of networks causing capacity damage before pruning, thereby leading to performance degradation. To alleviate this issue, we first study and revea

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PapMOT: Exploring Adversarial Patch Attack Against Multiple Object Tracking,heir respective identities across successive frames. Despite significant progress made in multiple object tracking (MOT), recent studies have revealed the vulnerability of existing MOT methods to adversarial attacks. Nevertheless, all of these attacks belong to digital attacks that inject pixel-leve

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,HiDiffusion: Unlocking Higher-Resolution Creativity and?Efficiency in?Pretrained Diffusion Models,n models will encounter unreasonable object duplication and exponentially increase the generation time. In this paper, we discover that object duplication arises from feature duplication in the deep blocks of the U-Net. Concurrently, We pinpoint the extended generation times to self-attention redund

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