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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: HAVEN
21#
發(fā)表于 2025-3-25 04:50:52 | 只看該作者
0302-9743 reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation..978-3-031-73003-0978-3-031-73004-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
22#
發(fā)表于 2025-3-25 11:22:35 | 只看該作者
https://doi.org/10.1007/978-3-642-34946-1 in the mask without losing minor ones. Our approach, validated through extensive experimentation, significantly improves WSS across five benchmarks (VOC: 79.8%, COCO: 53.9%, Context: 49.0%, ADE: 32.9%, Stuff: 37.4%), reducing the gap with fully supervised methods by over 84% on the VOC validation set. Code is available at ..
23#
發(fā)表于 2025-3-25 12:09:25 | 只看該作者
ADR Tools in Spanish Administrative Lawl dynamics prior knowledge in the videos. This abstract prior knowledge can be readily adapted to downstream tasks and aligned with executable actions through online adaptation. We conduct experiments on a series of robotics visual control tasks and verify that PVDR is an effective form for pre-training with videos to promote policy learning.
24#
發(fā)表于 2025-3-25 18:13:02 | 只看該作者
25#
發(fā)表于 2025-3-25 23:20:43 | 只看該作者
,Pre-trained Visual Dynamics Representations for?Efficient Policy Learning,l dynamics prior knowledge in the videos. This abstract prior knowledge can be readily adapted to downstream tasks and aligned with executable actions through online adaptation. We conduct experiments on a series of robotics visual control tasks and verify that PVDR is an effective form for pre-training with videos to promote policy learning.
26#
發(fā)表于 2025-3-26 02:18:42 | 只看該作者
27#
發(fā)表于 2025-3-26 05:21:52 | 只看該作者
28#
發(fā)表于 2025-3-26 11:03:36 | 只看該作者
29#
發(fā)表于 2025-3-26 13:18:37 | 只看該作者
30#
發(fā)表于 2025-3-26 19:50:30 | 只看該作者
,Reinforcement Learning via?Auxiliary Task Distillation,ment task from the environment reward without demonstrations, a learning curriculum, or pre-trained skills. AuxDistill achieves . higher success than the previous state-of-the-art baseline in the Habitat Object Rearrangement benchmark and outperforms methods that use pre-trained skills and expert demonstrations.
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