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Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

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樓主: Autopsy
51#
發(fā)表于 2025-3-30 08:40:16 | 只看該作者
52#
發(fā)表于 2025-3-30 13:35:30 | 只看該作者
,Totems: Physical Objects for?Verifying Visual Integrity,h taken of that scene. Totems bend and redirect light rays, thus providing multiple, albeit distorted, views of the scene within a single image. A defender can use these distorted totem pixels to detect if an image has been manipulated. Our approach unscrambles the light rays passing through the tot
53#
發(fā)表于 2025-3-30 19:04:03 | 只看該作者
,Dual-Stream Knowledge-Preserving Hashing for?Unsupervised Video Retrieval,n frame-level temporal context changes without focusing on video-level global semantics that are more useful for retrieval. Hence, we address this problem by decomposing video information into reconstruction-dependent and semantic-dependent information, which disentangles the semantic extraction fro
54#
發(fā)表于 2025-3-30 22:58:05 | 只看該作者
55#
發(fā)表于 2025-3-31 03:01:38 | 只看該作者
,Adaptive Cross-domain Learning for?Generalizable Person Re-identification,nseen target domains. Most existing methods are challenged for dealing with the shared and specific characteristics among different domains, which is called the domain conflict problem. To address this problem, we present an Adaptive Cross-domain Learning (ACL) framework equipped with a CrOss-Domain
56#
發(fā)表于 2025-3-31 06:53:16 | 只看該作者
Multi-query Video Retrieval,Despite recent progress, imperfect annotations in existing video retrieval datasets have posed significant challenges on model evaluation and development. In this paper, we tackle this issue by focusing on the less-studied setting of multi-query video retrieval, where multiple descriptions are provi
57#
發(fā)表于 2025-3-31 10:22:55 | 只看該作者
58#
發(fā)表于 2025-3-31 13:30:37 | 只看該作者
59#
發(fā)表于 2025-3-31 20:52:50 | 只看該作者
60#
發(fā)表于 2025-3-31 22:39:33 | 只看該作者
Domain Adaptive Person Search,nificant advances under fully and weakly supervised settings. However, existing methods ignore the generalization ability of the person search models. In this paper, we take a further step and present Domain Adaptive Person Search (DAPS), which aims to generalize the model from a labeled source doma
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