嘴唇可修剪 发表于 2025-3-28 15:13:37
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橡子 发表于 2025-3-28 20:22:18
http://reply.papertrans.cn/24/2343/234248/234248_42.pngRedundant 发表于 2025-3-29 01:00:43
http://reply.papertrans.cn/24/2343/234248/234248_43.png相互影响 发表于 2025-3-29 05:49:47
,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厚脸皮 发表于 2025-3-29 08:11:57
http://reply.papertrans.cn/24/2343/234248/234248_45.pngDuodenitis 发表于 2025-3-29 11:46:45
http://reply.papertrans.cn/24/2343/234248/234248_46.pngAlbumin 发表于 2025-3-29 19:34:44
,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-bavascular 发表于 2025-3-29 20:02:16
http://reply.papertrans.cn/24/2343/234248/234248_48.pngCHAFE 发表于 2025-3-30 00:42:25
,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 tmonochromatic 发表于 2025-3-30 04:24:00
,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