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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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21#
發(fā)表于 2025-3-25 05:38:41 | 只看該作者
22#
發(fā)表于 2025-3-25 07:44:14 | 只看該作者
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發(fā)表于 2025-3-25 15:36:31 | 只看該作者
W. Steggemann,C. Krabbe-Steggemanntest dataset with a limited annotation budget. Previous approaches relied on deep ensemble models to identify highly informative instances for labeling, but fell short in dense recognition tasks like segmentation and object detection due to their high computational costs. In this work, we present Me
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發(fā)表于 2025-3-25 18:14:21 | 只看該作者
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發(fā)表于 2025-3-25 22:03:43 | 只看該作者
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發(fā)表于 2025-3-26 02:35:42 | 只看該作者
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發(fā)表于 2025-3-26 06:41:53 | 只看該作者
https://doi.org/10.1007/978-3-662-47312-2age without relying on reference captions, bridging the gap between human judgment and machine-generated image captions. Experiments spanning several datasets demonstrate that our proposal achieves state-of-the-art results compared to existing reference-free evaluation scores. Our source code and trained models are publicly available at: ..
28#
發(fā)表于 2025-3-26 09:51:00 | 只看該作者
29#
發(fā)表于 2025-3-26 15:39:26 | 只看該作者
30#
發(fā)表于 2025-3-26 19:16:00 | 只看該作者
ABDM und Schwangerschaftshypertonieout manually tuning the perturbation parameters; and a novel application of Gumbel-sigmoid reparameterization for efficiently learning Bernoulli-distributed binary masks under continuous optimization. Our experiments on problematic region detection and faithfulness tests demonstrate our method’s superiority over state-of-the-art UA methods.
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