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Graph convolutional network with structure pooling and joint-wise channel attention for action recognition | |
Chen, Yuxin1![]() ![]() ![]() ![]() | |
发表期刊 | PATTERN RECOGNITION
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ISSN | 0031-3203 |
2020-07-01 | |
卷号 | 103页码:12 |
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. |
关键词 | Graph convolutional network Structure graph pooling Joint-wise channel attention |
DOI | 10.1016/j.patcog.2020.107321 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | the National Key R&D Plan (Nos. 2017YFB1-002801 and 2016QY01W0106), the Natural Science Foundation of China (Nos. U1803119, U1736106, 61751212, 61721004, 61972397, and 61772225 ), the NSFC-General Technology Collaborative Fund for Basic Research (Grant No. U1636218), the Key Research Program of Frontier Sciences, CAS (Grant No. YZDJ- SSW-JSC040), Beijing Natural Science Foundation (Nos. JQ18018 , L172051 andL182058 ) and the CAS External Cooperation Key Project. |
项目资助者 | 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 |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000530845000048 |
出版者 | ELSEVIER SCI LTD |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39473 |
专题 | 多模态人工智能系统全国重点实验室_视频内容安全 |
通讯作者 | Yuan, Chunfeng; Li, Bing |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, PR China 2.School of Software Engineering, Beijing Jiaotong University, Beijing 10 0 044, PR China 3.CAS Center for Excellence in Brain Science and Intelligence Technology Academy of Sciences, Beijing 100190, PR China 4.University of Chinese Academy of Sciences, Beijing 100190, PR China 5.Institute of Information Engineering, Chinese Academy of Sciences, Beijing 10 0 093, PR China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
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. |
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