Title | ||
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Structure-Aware Multi-scale Hierarchical Graph Convolutional Network for Skeleton Action Recognition |
Abstract | ||
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In recent years, graph convolutional neural network (GCNN) has achieved the most advanced results in skeleton action recognition tasks. However, existing models mainly focus on extracting local information from joint-level and part-level, but ignore the global information of frame-level and the relevance between multiple levels, which lead to the loss of hierarchical information. Moreover, these models consider the non-physical connection relationship between nodes but neglect the dependence between body parts. The lose of topology information directly results in poor model performance. In this paper, we propose a structure-aware multi-scale hierarchical graph convolutional network (SAMS-HGCN) model, which includes two modules: a structure-aware hierarchical graph pooling block (SA-HGP Block) and a multi-scale fusion module (MSF module). Specifically, SA-HGP Block establishes a hierarchical network to capture the topological information of multiple levels by using the hierarchical graph pooling (HGP) operation and model the dependence among parts via the structure-aware learning (SA Learning) operation. MSF module fuses information of different scales in each level to obtain multi-scale global structural information. Experiments show that our method achieves comparable performances to state-of-the-art methods on NTU-RGB+D and Kinetics-Skeleton datasets. |
Year | DOI | Venue |
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2021 | 10.1007/978-3-030-86365-4_24 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2021, PT III |
Keywords | DocType | Volume |
Skeleton action recognition, Hierarchical graph convolution network, Multi-scale fusion | Conference | 12893 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Changxiang He | 1 | 0 | 0.68 |
Shuting Liu | 2 | 0 | 0.34 |
Y. Zhao | 3 | 10 | 5.43 |
Xiaofei Qin | 4 | 0 | 1.69 |
Jiayuan Zeng | 5 | 0 | 0.34 |
Xuedian Zhang | 6 | 0 | 0.68 |