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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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樓主: Coolidge
31#
發(fā)表于 2025-3-26 22:04:59 | 只看該作者
,Which Model Generated This Image? A Model-Agnostic Approach for?Origin Attribution,iments corresponding to various generative models verify the effectiveness of our OCC-CLIP framework. Furthermore, an experiment based on?the recently released DALL.E-3 API verifies the real-world applicability of our solution. Our source code is available?at ..
32#
發(fā)表于 2025-3-27 01:52:43 | 只看該作者
33#
發(fā)表于 2025-3-27 05:22:58 | 只看該作者
0302-9743 ce 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; r
34#
發(fā)表于 2025-3-27 12:35:42 | 只看該作者
35#
發(fā)表于 2025-3-27 16:50:43 | 只看該作者
36#
發(fā)表于 2025-3-27 21:33:58 | 只看該作者
37#
發(fā)表于 2025-3-28 00:51:07 | 只看該作者
Christine Altenbuchner,Manuela Larcher. Nvidia’s PYOCO, and . vs. Meta’s Make-A-Video. Our model outperforms commercial solutions such as RunwayML’s Gen2 and Pika Labs. Finally, our factorizing approach naturally lends itself to animating images based on a user’s text prompt, where our generations are preferred . over prior work.
38#
發(fā)表于 2025-3-28 03:42:05 | 只看該作者
39#
發(fā)表于 2025-3-28 09:55:20 | 只看該作者
,Improving Adversarial Transferability via?Model Alignment,metric analysis of the resulting changes in the loss landscape. Extensive experiments on the ImageNet dataset, using a variety of model architectures, demonstrate that perturbations generated from aligned source models exhibit significantly higher transferability than those from the original source model. Our source code is available at ..
40#
發(fā)表于 2025-3-28 11:14:12 | 只看該作者
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