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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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31#
發(fā)表于 2025-3-26 22:55:59 | 只看該作者
32#
發(fā)表于 2025-3-27 04:07:52 | 只看該作者
Manish Asthana,Kapil Dev Gupta,Arvind Kumarssing potential spurious correlations in datasets, annotating concepts for images, and refining the annotations for improved robustness. We evaluate the proposed method on multiple datasets, and the results demonstrate its effectiveness in reducing model reliance on spurious correlations while preserving its interpretability.
33#
發(fā)表于 2025-3-27 07:06:08 | 只看該作者
,: Long-Form Video Understanding with?Large Language Model as?Agent,es used on average. These results demonstrate superior effectiveness and efficiency of our method over the current state-of-the-art methods, highlighting the potential of agent-based approaches in advancing long-form video understanding.
34#
發(fā)表于 2025-3-27 10:08:55 | 只看該作者
35#
發(fā)表于 2025-3-27 17:12:59 | 只看該作者
Sunil B. Bhoi,Jayesh M. Dhodiyaion learning of the natural world—and introduce Nature Multi-View (NMV), a dataset of natural world imagery including >3 million ground-level and aerial image pairs for over 6,000 plant taxa across the ecologically diverse state of California. The NMV dataset and accompanying material are available at ..
36#
發(fā)表于 2025-3-27 18:30:15 | 只看該作者
Conference proceedings 2025uter 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; reinforceme
37#
發(fā)表于 2025-3-28 00:33:36 | 只看該作者
,Ex2Eg-MAE: A Framework for?Adaptation of?Exocentric Video Masked Autoencoders for?Egocentric Socialntly excels across diverse social role understanding tasks. It achieves state-of-the-art results in Ego4D’s . challenge (+0.7% mAP, +3.2% Accuracy). For the . challenge, it achieves competitive performance with the state-of-the-art (–0.7% mAP, +1.5% Accuracy) without supervised training on external
38#
發(fā)表于 2025-3-28 02:45:18 | 只看該作者
,SAVE: Protagonist Diversification with?,tructure ,gnostic ,ideo ,diting,xtual embedding to properly represent the motion in a source video. We also regulate the motion word to attend to proper motion-related areas by introducing a novel pseudo optical flow, efficiently computed from the pre-calculated attention maps. Finally, we decouple the motion from the appearance o
39#
發(fā)表于 2025-3-28 09:39:47 | 只看該作者
,Meta-optimized Angular Margin Contrastive Framework for?Video-Language Representation Learning, training guided by a small amount of unbiased meta-data and augmented by video-text data generated by large vision-language model, we improve video-language representations and achieve superior performances on commonly used video question answering and text-video retrieval datasets.
40#
發(fā)表于 2025-3-28 13:57:13 | 只看該作者
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