Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Gan, Weihao"'
Autor:
Pu, Yifan, Wang, Yiru, Xia, Zhuofan, Han, Yizeng, Wang, Yulin, Gan, Weihao, Wang, Zidong, Song, Shiji, Huang, Gao
Rotated object detection aims to identify and locate objects in images with arbitrary orientation. In this scenario, the oriented directions of objects vary considerably across different images, while multiple orientations of objects exist within an
Externí odkaz:
http://arxiv.org/abs/2303.07820
Autor:
He, Mengzhe, Wang, Yali, Wu, Jiaxi, Wang, Yiru, Li, Hanqing, Li, Bo, Gan, Weihao, Wu, Wei, Qiao, Yu
Cross domain object detection is a realistic and challenging task in the wild. It suffers from performance degradation due to large shift of data distributions and lack of instance-level annotations in the target domain. Existing approaches mainly fo
Externí odkaz:
http://arxiv.org/abs/2205.01291
Autor:
Wu, Jiaxi, Chen, Jiaxin, He, Mengzhe, Wang, Yiru, Li, Bo, Ma, Bingqi, Gan, Weihao, Wu, Wei, Wang, Yali, Huang, Di
Domain adaptive object detection (DAOD) is a promising way to alleviate performance drop of detectors in new scenes. Albeit great effort made in single source domain adaptation, a more generalized task with multiple source domains remains not being w
Externí odkaz:
http://arxiv.org/abs/2204.07964
Autor:
Shen, Qiuhong, Qiao, Lei, Guo, Jinyang, Li, Peixia, Li, Xin, Li, Bo, Feng, Weitao, Gan, Weihao, Wu, Wei, Ouyang, Wanli
Unsupervised learning has been popular in various computer vision tasks, including visual object tracking. However, prior unsupervised tracking approaches rely heavily on spatial supervision from template-search pairs and are still unable to track ob
Externí odkaz:
http://arxiv.org/abs/2204.01475
Autor:
Xu, Liang, Song, Ziyang, Wang, Dongliang, Su, Jing, Fang, Zhicheng, Ding, Chenjing, Gan, Weihao, Yan, Yichao, Jin, Xin, Yang, Xiaokang, Zeng, Wenjun, Wu, Wei
We present a GAN-based Transformer for general action-conditioned 3D human motion generation, including not only single-person actions but also multi-person interactive actions. Our approach consists of a powerful Action-conditioned motion TransForme
Externí odkaz:
http://arxiv.org/abs/2203.07706
Autor:
Chen, Boyu, Li, Peixia, Bai, Lei, Qiao, Lei, Shen, Qiuhong, Li, Bo, Gan, Weihao, Wu, Wei, Ouyang, Wanli
Exploiting a general-purpose neural architecture to replace hand-wired designs or inductive biases has recently drawn extensive interest. However, existing tracking approaches rely on customized sub-modules and need prior knowledge for architecture s
Externí odkaz:
http://arxiv.org/abs/2203.05328
Autor:
Yu, Shoubin, Zhao, Zhongyin, Fang, Haoshu, Deng, Andong, Su, Haisheng, Wang, Dongliang, Gan, Weihao, Lu, Cewu, Wu, Wei
Anomaly detection in surveillance videos is challenging and important for ensuring public security. Different from pixel-based anomaly detection methods, pose-based methods utilize highly-structured skeleton data, which decreases the computational bu
Externí odkaz:
http://arxiv.org/abs/2112.03649
This technical report presents an overview of our solution used in the submission to 2021 HACS Temporal Action Localization Challenge on both Supervised Learning Track and Weakly-Supervised Learning Track. Temporal Action Localization (TAL) requires
Externí odkaz:
http://arxiv.org/abs/2107.12618
Efficient spatiotemporal modeling is an important yet challenging problem for video action recognition. Existing state-of-the-art methods exploit neighboring feature differences to obtain motion clues for short-term temporal modeling with a simple co
Externí odkaz:
http://arxiv.org/abs/2106.01088
Autor:
Qing, Zhiwu, Su, Haisheng, Gan, Weihao, Wang, Dongliang, Wu, Wei, Wang, Xiang, Qiao, Yu, Yan, Junjie, Gao, Changxin, Sang, Nong
Temporal action proposal generation aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet important task in the video understanding field. The proposals generated by current methods still suffer from inaccurat
Externí odkaz:
http://arxiv.org/abs/2103.13141