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Graph convolutional network with structure pooling and joint-wise channel attention for action recognition

文献类型：期刊论文

作者Chen, Yuxin3; Ma, Gaoqun2; Yuan, Chunfeng3; Li, Bing3; Zhang, Hui4; Wang, Fangshi2; Hu, Weiming1,3,5
刊名PATTERN RECOGNITION
出版日期2020-07-01
卷号103页码:12
关键词Graph convolutional network Structure graph pooling Joint-wise channel attention
ISSN号0031-3203
DOI10.1016/j.patcog.2020.107321
通讯作者Yuan, Chunfeng(cfyuan@nlpr.ia.ac.cn) ; Li, Bing(bli@nlpr.ia.ac.cn)
英文摘要Recently, graph convolutional networks (GCNs) have achieved state-of-the-art results for skeleton based action recognition by expanding convolutional neural networks (CNNs) to graphs. However, due to the lack of effective feature aggregation method, e.g. max pooling in CNN, existing GCN-based methods only learn local information among adjacent joints and are hard to obtain high-level interaction features, such as interactions between five parts of human body. Moreover, subtle differences of confusing actions often hide in specific channels of key joints' features, this kind of discriminative information is rarely exploited in previous methods. In this paper, we propose a novel graph convolutional network with structure based graph pooling (SGP) scheme and joint-wise channel attention UCA) modules. The SGP scheme pools the human skeleton graph according to the prior knowledge of human body's typology. This pooling scheme not only leads to more global representations but also reduces the amount of parameters and computation cost. The JCA module learns to selectively focus on discriminative joints of skeleton and pays different levels of attention to different channels. This novel attention mechanism enhance the model's ability to classify confusing actions. We evaluate our SGP scheme and JCA module on three most challenging skeleton based action recognition datasets: NTU-RGB+D, Kinetics-M, and SYSU-3D. Our method outperforms the state-of-art methods on three benchmarks. (C) 2020 Elsevier Ltd. All rights reserved.
资助项目National Key RD Plan[2017YFB1-002801] ; National Key RD Plan[2016QY01W0106] ; Natural Science Foundation of China[U1803119] ; Natural Science Foundation of China[U1736106] ; Natural Science Foundation of China[61751212] ; Natural Science Foundation of China[61721004] ; Natural Science Foundation of China[61972397] ; Natural Science Foundation of China[61772225] ; NSFC-General Technology Collaborative Fund for Basic Research[U1636218] ; Key Research Program of Frontier Sciences, CAS[YZDJSSW-JSC040] ; Beijing Natural Science Foundation[JQ18018] ; Beijing Natural Science Foundation[L172051] ; Beijing Natural Science Foundation[L182058] ; CAS External Cooperation Key Project ; Youth Innovation Promotion Association, CAS
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000530845000048
出版者ELSEVIER SCI LTD
资助机构National Key RD Plan ; Natural Science Foundation of China ; NSFC-General Technology Collaborative Fund for Basic Research ; Key Research Program of Frontier Sciences, CAS ; Beijing Natural Science Foundation ; CAS External Cooperation Key Project ; Youth Innovation Promotion Association, CAS
源URL[http://ir.ia.ac.cn/handle/173211/39472]  
专题自动化研究所_模式识别国家重点实验室_视频内容安全团队
通讯作者Yuan, Chunfeng; Li, Bing
作者单位1.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
2.Beijing Jiaotong Univ, Sch Software Engn, Beijing 100044, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Inst Informat Engn, Beijing 100093, Peoples R China
5.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Yuxin,Ma, Gaoqun,Yuan, Chunfeng,et al. Graph convolutional network with structure pooling and joint-wise channel attention for action recognition[J]. PATTERN RECOGNITION,2020,103:12.
APA Chen, Yuxin.,Ma, Gaoqun.,Yuan, Chunfeng.,Li, Bing.,Zhang, Hui.,...&Hu, Weiming.(2020).Graph convolutional network with structure pooling and joint-wise channel attention for action recognition.PATTERN RECOGNITION,103,12.
MLA Chen, Yuxin,et al."Graph convolutional network with structure pooling and joint-wise channel attention for action recognition".PATTERN RECOGNITION 103(2020):12.

入库方式： OAI收割

来源：自动化研究所

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