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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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51#
發(fā)表于 2025-3-30 08:21:03 | 只看該作者
52#
發(fā)表于 2025-3-30 14:14:06 | 只看該作者
,Free Lunch for?Gait Recognition: A Novel Relation Descriptor,on the training set’s identity count. To address this, we propose Farthest gait-Anchor Selection to identify the most discriminative gait anchors and an Orthogonal Regularization Loss to increase diversity within gait anchors. Compared to individual-specific features extracted from the backbone, our
53#
發(fā)表于 2025-3-30 20:00:39 | 只看該作者
54#
發(fā)表于 2025-3-30 21:56:40 | 只看該作者
,Adaptive Correspondence Scoring for?Unsupervised Medical Image Registration,ustrate the versatility and effectiveness of our method, we tested our framework on three representative registration architectures across three medical image datasets along with other baselines. Our adaptive framework consistently outperforms other methods both quantitatively and qualitatively. Pai
55#
發(fā)表于 2025-3-31 02:32:22 | 只看該作者
,Watch Your Steps: Local Image and?Scene Editing by?Text Instructions,elevance map conveys the importance of changing each pixel to achieve an edit, and guides downstream modifications, ensuring that pixels irrelevant to the edit remain unchanged. With the relevance maps of multiview posed images, we can define the ., defining the 3D region within which modifications
56#
發(fā)表于 2025-3-31 06:53:01 | 只看該作者
,Forget More to?Learn More: Domain-Specific Feature Unlearning for?Semi-supervised and?Unsupervised aiming to learn domain-specific features. This involves minimizing classification loss for in-domain images and maximizing uncertainty loss for out-of-domain images. Subsequently, we transform the images into a new space, strategically unlearning (forgetting) the domain-specific representations whi
57#
發(fā)表于 2025-3-31 10:27:36 | 只看該作者
58#
發(fā)表于 2025-3-31 16:36:27 | 只看該作者
Human-in-the-Loop Visual Re-ID for Population Size Estimation,0% using CV alone to less than 20% by vetting a fraction (often less than 0.002%) of the total pairs. The cost of vetting reduces with the increase in accuracy and provides a practical approach for population size estimation within a desired tolerance when deploying Re-ID systems. (Code available at
59#
發(fā)表于 2025-3-31 18:59:52 | 只看該作者
60#
發(fā)表于 2025-4-1 01:12:10 | 只看該作者
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