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Titlebook: Computer Vision – ACCV 2022; 16th Asian Conferenc Lei Wang,Juergen Gall,Rama Chellappa Conference proceedings 2023 The Editor(s) (if applic

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31#
發(fā)表于 2025-3-27 00:41:52 | 只看該作者
Conference proceedings 2023ing, and shape representation; datasets and performance analysis;.Part VI: biomedical image analysis; deep learning for computer vision; ..Part VII: generative models for computer vision; segmentation and grouping; motion and tracking; document image analysis; big data, large scale methods. .
32#
發(fā)表于 2025-3-27 04:39:40 | 只看該作者
Conference proceedings 2023cember 2022...The total of 277 contributions included in the proceedings set was carefully reviewed and selected from 836 submissions during two rounds of reviewing and improvement. The papers focus on the following topics:..Part I: 3D computer vision; optimization methods;.Part II: applications of
33#
發(fā)表于 2025-3-27 07:25:08 | 只看該作者
A Rational Protocol Treatment of 51% Attacksg of a 2D to 3D human pose lifter neural network. Our results show that we can achieve 3D pose estimation performance comparable to methods using real data from specialized datasets but in a zero-shot setup, showing the generalization potential of our framework.
34#
發(fā)表于 2025-3-27 11:12:34 | 只看該作者
35#
發(fā)表于 2025-3-27 17:00:29 | 只看該作者
Decanus to?Legatus: Synthetic Training for?2D-3D Human Pose Liftingg of a 2D to 3D human pose lifter neural network. Our results show that we can achieve 3D pose estimation performance comparable to methods using real data from specialized datasets but in a zero-shot setup, showing the generalization potential of our framework.
36#
發(fā)表于 2025-3-27 20:41:05 | 只看該作者
37#
發(fā)表于 2025-3-27 23:28:55 | 只看該作者
38#
發(fā)表于 2025-3-28 02:21:11 | 只看該作者
39#
發(fā)表于 2025-3-28 07:47:25 | 只看該作者
40#
發(fā)表于 2025-3-28 12:02:43 | 只看該作者
Learning Video-Independent Eye Contact Segmentation from?In-the-Wild Videosact targets vary across different videos, learning a generic video-independent eye contact detector is still a challenging task. In this work, we address the task of one-way eye contact detection for videos in the wild. Our goal is to build a unified model that can identify when a person is looking
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