Motif-GCNs With Local and Non-Local Temporal Blocks for Skeleton-Based Action Recognition
文献类型：期刊论文
| 作者 | Wen, Yu-Hui5; Gao, Lin3,4; Fu, Hongbo2; Zhang, Fang-Lue1; Xia, Shihong3,4; Liu, Yong-Jin5 |
| 刊名 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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| 出版日期 | 2023-02-01 |
| 卷号 | 45期号:2页码:2009-2023 |
| ISSN号 | 0162-8828 |
| 关键词 | Skeleton Feature extraction Joints Convolutional codes Topology Training Sparse matrices Action recognition graph convolutional neural networks spatio-temporal attention non-local block skeleton sequence |
| DOI | 10.1109/TPAMI.2022.3170511 |
| 英文摘要 | Recent works have achieved remarkable performance for action recognition with human skeletal data by utilizing graph convolutional models. Existing models mainly focus on developing graph convolutional operations to encode structural properties of a skeletal graph, whose topology is manually predefined and fixed over all action samples. Some recent works further take sample-dependent relationships among joints into consideration. However, the complex relationships between arbitrary pairwise joints are difficult to learn and the temporal features between frames are not fully exploited by simply using traditional convolutions with small local kernels. In this paper, we propose a motif-based graph convolution method, which makes use of sample-dependent latent relations among non-physically connected joints to impose a high-order locality and assigns different semantic roles to physical neighbors of a joint to encode hierarchical structures. Furthermore, we propose a sparsity-promoting loss function to learn a sparse motif adjacency matrix for latent dependencies in non-physical connections. For extracting effective temporal information, we propose an efficient local temporal block. It adopts partial dense connections to reuse temporal features in local time windows, and enrich a variety of information flow by gradient combination. In addition, we introduce a non-local temporal block to capture global dependencies among frames. Our model can capture local and non-local relationships both spatially and temporally, by integrating the local and non-local temporal blocks into the sparse motif-based graph convolutional networks (SMotif-GCNs). Comprehensive experiments on four large-scale datasets show that our model outperforms the state-of-the-art methods. Our code is publicly available at https://github.com/wenyh1616/SAMotif-GCN. |
| 资助项目 | National Key Research and Development Plan[2021YFF0307702] ; China Postdoctoral Sci-ence Foundation[2021M701891] ; Tsinghua University Initiative Scientific Research Program ; National Natural Science Foundation of China[61725204] ; National Natural Science Foundation of China[61872440] ; Beijing Munic-ipal Natural Science Foundation for Distinguished Young Scholars[JQ21013] ; Youth Innovation Promotion Association CAS, Marsden Fund Council[MFP-20-VUW-180] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| 出版者 | IEEE COMPUTER SOC |
| WOS记录号 | WOS:000912386000044 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/20041]  |
| 专题 | 中国科学院计算技术研究所期刊论文 |
| 通讯作者 | Gao, Lin; Liu, Yong-Jin |
| 作者单位 | 1.Victoria Univ Wellington, Sch Engn & Comp Sci, Wellington 6012, New Zealand 2.City Univ Hong Kong, Sch Creat Media, Hong Kong, Peoples R China 3.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China 5.Tsinghua Univ, Dept Comp Sci & Technol, BNRist, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wen, Yu-Hui,Gao, Lin,Fu, Hongbo,et al. Motif-GCNs With Local and Non-Local Temporal Blocks for Skeleton-Based Action Recognition[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2023,45(2):2009-2023. |
| APA | Wen, Yu-Hui,Gao, Lin,Fu, Hongbo,Zhang, Fang-Lue,Xia, Shihong,&Liu, Yong-Jin.(2023).Motif-GCNs With Local and Non-Local Temporal Blocks for Skeleton-Based Action Recognition.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,45(2),2009-2023. |
| MLA | Wen, Yu-Hui,et al."Motif-GCNs With Local and Non-Local Temporal Blocks for Skeleton-Based Action Recognition".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 45.2(2023):2009-2023. |
入库方式： OAI收割
来源：计算技术研究所
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