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	WHEN SKELETON MEETS APPEARANCE: ADAPTIVE APPEARANCE INFORMATION ENHANCEMENT FOR SKELETON BASED ACTION RECOGNITION
	Wang Suqin1,4; Zhou Lu1,4; Chen Yingying1,3,4; Jiangtao Huo2; Wang Jinqiao1,4
	2022
会议日期	July 18-22, 2022
会议地点	中国台北
英文摘要	Skeleton-based action recognition methods which utilize graph convolution networks (GCNs) have achieved remark able success in recent years. However, action recognizer can be easily confused by the ambiguity caused by different ac tions with similar skeleton sequences when only skeleton data is trained. Introducing appearance information can effectively eliminate the ambiguity. Based on this, we introduce a two stream network for action recognition. One trained on RGB images extracts appearance information. The other trained on skeleton data models motion information and adaptively captures appearance information of action areas at action related intervals via a specially tailored attention mechanism. Our architecture is trained and evaluated on two large-scale datasets: NTU RGB+D and NTU RGB+D 120, and a small scale human-object interaction dataset Northwestern-UCLA. Experiment results verify the effectiveness of our method and the performance of our method exceeds the state-of-the-art with a significant margin.
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/57151]
专题	紫东太初大模型研究中心
作者单位	1.School of Artificial Intelligence, University of Chinese Academy of Sciences 2.Army Medical University, NCO School of PLA 3.Development Research Institute of Guangzhou Smart City 4.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Wang Suqin,Zhou Lu,Chen Yingying,et al. WHEN SKELETON MEETS APPEARANCE: ADAPTIVE APPEARANCE INFORMATION ENHANCEMENT FOR SKELETON BASED ACTION RECOGNITION[C]. 见:. 中国台北. July 18-22, 2022.

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