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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: ominous
31#
發(fā)表于 2025-3-26 21:10:29 | 只看該作者
0302-9743 puter Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022..?The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforce
32#
發(fā)表于 2025-3-27 05:06:14 | 只看該作者
,Category-Level 6D Object Pose and?Size Estimation Using Self-supervised Deep Prior Deformation Networe specifically, we apply two rigid transformations to each object observation in parallel, and feed them into DPDN respectively to yield dual sets of predictions; on top of the parallel learning, an inter-consistency term is employed to keep cross consistency between dual predictions for improving
33#
發(fā)表于 2025-3-27 07:49:34 | 只看該作者
34#
發(fā)表于 2025-3-27 12:17:23 | 只看該作者
,Domain Adaptive Hand Keypoint and?Pixel Localization in?the?Wild,sy predictions during self-training. In this paper, we propose to utilize the divergence of two predictions to estimate the confidence of the target image for both tasks. These predictions are given from two separate networks, and their divergence helps identify the noisy predictions. To integrate o
35#
發(fā)表于 2025-3-27 15:32:49 | 只看該作者
36#
發(fā)表于 2025-3-27 18:44:02 | 只看該作者
37#
發(fā)表于 2025-3-28 01:40:56 | 只看該作者
,Multimodal Object Detection via?Probabilistic Ensembling, hold, e.g., fusing outputs from other fusion methods (both off-the-shelf and trained in-house). We validate ProbEn on two benchmarks containing both aligned (KAIST) and unaligned (FLIR) multimodal images, showing that ProbEn outperforms prior work by more than . in relative performance!
38#
發(fā)表于 2025-3-28 05:17:31 | 只看該作者
39#
發(fā)表于 2025-3-28 09:37:35 | 只看該作者
,CPO: Change Robust Panorama to?Point Cloud Localization,r gradient-based optimization. CPO is lightweight and achieves effective localization in all tested scenarios, showing stable performance despite scene changes, repetitive structures, or featureless regions, which are typical challenges for visual localization with perspective cameras.
40#
發(fā)表于 2025-3-28 12:05:10 | 只看該作者
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