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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: exterminate
41#
發(fā)表于 2025-3-28 15:13:37 | 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; reinforc
42#
發(fā)表于 2025-3-28 20:22:18 | 只看該作者
43#
發(fā)表于 2025-3-29 01:00:43 | 只看該作者
44#
發(fā)表于 2025-3-29 05:49:47 | 只看該作者
,An Efficient Person Clustering Algorithm for?Open Checkout-free Groceries,ial since it faces challenges of recognizing the dynamic and massive flow of people. In particular, a clustering method that can efficiently assign each snapshot to the corresponding customer is essential for the system. In order to address the unique challenges in the open checkout-free grocery, we
45#
發(fā)表于 2025-3-29 08:11:57 | 只看該作者
46#
發(fā)表于 2025-3-29 11:46:45 | 只看該作者
47#
發(fā)表于 2025-3-29 19:34:44 | 只看該作者
,Actor-Centered Representations for?Action Localization in?Streaming Videos, tackle the problem of learning . representations through the notion of . to . actions in streaming videos . the need for training labels and outlines for the objects in the video. We propose a framework driven by the notion of hierarchical predictive learning to construct . features by attention-ba
48#
發(fā)表于 2025-3-29 20:02:16 | 只看該作者
49#
發(fā)表于 2025-3-30 00:42:25 | 只看該作者
,Domain Knowledge-Informed Self-supervised Representations for?Workout Form Assessment,ally requires estimating human’s body pose. However, off-the-shelf pose estimators struggle to perform well on the videos recorded in gym scenarios due to factors such as camera angles, occlusion from gym equipment, illumination, and clothing. To aggravate the problem, the errors to be detected in t
50#
發(fā)表于 2025-3-30 04:24:00 | 只看該作者
,Responsive Listening Head Generation: A Benchmark Dataset and?Baseline,rsation. As the indispensable complement to talking heads generation, listening head generation has seldomly been studied in literature. Automatically synthesizing listening behavior that actively responds to a talking head, is critical to applications such as digital human, virtual agents and socia
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