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Titlebook: Computer Vision – ECCV 2020; 16th European Confer Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm Conference proceedings 2020 Springer Natur

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樓主: 富裕
21#
發(fā)表于 2025-3-25 04:49:35 | 只看該作者
Adversarial Robustness on In- and Out-Distribution Improves Explainability,
22#
發(fā)表于 2025-3-25 10:07:02 | 只看該作者
Visual Matched Filtering in Vertebratesn doing so, GroSS is able to train an entire grouped convolution architecture search-space concurrently. We demonstrate this through architecture searches with performance objectives on multiple datasets and networks. GroSS enables more effective and efficient search for grouped convolutional architectures.
23#
發(fā)表于 2025-3-25 14:06:34 | 只看該作者
24#
發(fā)表于 2025-3-25 16:47:45 | 只看該作者
25#
發(fā)表于 2025-3-25 22:31:40 | 只看該作者
26#
發(fā)表于 2025-3-26 02:50:48 | 只看該作者
27#
發(fā)表于 2025-3-26 04:57:32 | 只看該作者
Efficient Adversarial Attacks for Visual Object Tracking,ork based trackers. Under a single GPU, FAN is efficient in the training speed and has a strong attack performance. The FAN can generate an adversarial example at 10ms, achieve effective targeted attack (at least 40% drop rate on OTB) and untargeted attack (at least 70% drop rate on OTB).
28#
發(fā)表于 2025-3-26 10:09:54 | 只看該作者
What Makes Fake Images Detectable? Understanding Properties that Generalize,technique to exaggerate these detectable properties and demonstrate that, even when the image generator is adversarially finetuned against a fake image classifier, it is still imperfect and leaves detectable artifacts in certain image patches. Code is available at ..
29#
發(fā)表于 2025-3-26 14:47:06 | 只看該作者
30#
發(fā)表于 2025-3-26 19:26:15 | 只看該作者
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