Knowledge Commons of Institute of Automation,CAS
Extremely Lightweight Skeleton-Based Action Recognition With ShiftGCN plus | |
Cheng, Ke1,2,3; Zhang, Yifan1,2,3; He, Xiangyu1,2,3; Cheng, Jian1,2,3; Lu, Hanqing1,2,3 | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
ISSN | 1057-7149 |
2021 | |
卷号 | 30页码:7333-7348 |
摘要 | In skeleton-based action recognition, graph convolutional networks (GCNs) have achieved remarkable success. However, there are two shortcomings of current GCN-based methods. Firstly, the computation cost is pretty heavy, typically over 15 GFLOPs for one action sample. Some recent works even reach similar to 100 GFLOPs. Secondly, the receptive fields of both spatial graph and temporal graph are inflexible. Although recent works introduce incremental adaptive modules to enhance the expressiveness of spatial graph, their efficiency is still limited by regular GCN structures. In this paper, we propose a shift graph convolutional network (ShiftGCN) to overcome both short-comings. ShiftGCN is composed of novel shift graph operations and lightweight point-wise convolutions, where the shift graph operations provide flexible receptive fields for both spatial graph and temporal graph. To further boost the efficiency, we introduce four techniques and build a more lightweight skeleton-based action recognition model named ShiftGCN++. ShiftGCN-H- is an extremely computation-efficient model, which is designed for low-power and low-cost devices with very limited computing power. On three datasets for skeleton-based action recognition, ShiftGCN notably exceeds the state-of-the-art methods with over 10x less FLOPs and 4x practical speedup. ShiftGCN-H- further boosts the efficiency of ShiftGCN, which achieves comparable performance with 6x less FLOPs and 2x practical speedup. |
关键词 | Skeleton-based action recognition graph convolutional network lightweight network shift network |
DOI | 10.1109/TIP.2021.3104182 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences[XDA27040300] ; NSFC[61876182] ; NSFC[61906195] ; Jiangsu Frontier Technology Basic Research Project[BK20192004] ; Key Project of Chinese Academy of Sciences[ZDRW-XH-2021-3] |
项目资助者 | Strategic Priority Research Program of the Chinese Academy of Sciences ; NSFC ; Jiangsu Frontier Technology Basic Research Project ; Key Project of Chinese Academy of Sciences |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000686764400009 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 图像视频处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/45867 |
专题 | 紫东太初大模型研究中心_图像与视频分析 复杂系统认知与决策实验室_高效智能计算与学习 |
通讯作者 | Zhang, Yifan |
作者单位 | 1.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, AIRIA, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
第一作者单位 | 模式识别国家重点实验室; 中国科学院自动化研究所 |
通讯作者单位 | 模式识别国家重点实验室; 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Cheng, Ke,Zhang, Yifan,He, Xiangyu,et al. Extremely Lightweight Skeleton-Based Action Recognition With ShiftGCN plus[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2021,30:7333-7348. |
APA | Cheng, Ke,Zhang, Yifan,He, Xiangyu,Cheng, Jian,&Lu, Hanqing.(2021).Extremely Lightweight Skeleton-Based Action Recognition With ShiftGCN plus.IEEE TRANSACTIONS ON IMAGE PROCESSING,30,7333-7348. |
MLA | Cheng, Ke,et al."Extremely Lightweight Skeleton-Based Action Recognition With ShiftGCN plus".IEEE TRANSACTIONS ON IMAGE PROCESSING 30(2021):7333-7348. |
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