会议论文

英文

2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)

2161-4393

2022

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

IEEE International Joint Conference on Neural Networks (IJCNN)

JUL 18-23, 2022

Padua, ITALY

[Wang, Wei;Xie, Wei;Li, Wanxin;Jin, Lianghao] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Peoples R China.^[Wang, Wei;Xie, Wei;Li, Wanxin;Jin, Lianghao] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.^[Tu, Zhigang] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Peoples R China.

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

978-1-7281-8671-9

10.1109/IJCNN55064.2022.9892727

Collaborative Innovation Center for Informatization and Balanced Development of K-12 Education by MOE [xtzd2021-004]; Hubei Province [xtzd2021-004]; Fundamental Research Funds for the Central Universities [CCNU20TS028]; Teaching research project of CCNU [202013]

本校为第一机构

In skeleton-based action recognition task, graph convolutional network has attracted widespread attention and achieved remarkable results. However, most of the current methods are performing graph convolution on the entire skeleton graph, ignoring the fact that people are composed of different body parts. In addition, previous work ignores the temporal and spatial independence and relevance of different parts. Thus, to solve these issues, we optimize the representation of the skeleton graph, graph convolution and temporal convolution respectively. In this work, we propose multi-part adaptive g...