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Multi-Stage Attention-Enhanced Sparse Graph Convolutional Network for Skeleton-Based Action Recognition
| Content Provider | MDPI |
|---|---|
| Author | Li, Chaoyue Zou, Lian Fan, Cien Jiang, Hao Liu, Yifeng |
| Copyright Year | 2021 |
| Description | Graph convolutional networks (GCNs), which model human actions as a series of spatial-temporal graphs, have recently achieved superior performance in skeleton-based action recognition. However, the existing methods mostly use the physical connections of joints to construct a spatial graph, resulting in limited topological information of the human skeleton. In addition, the action features in the time domain have not been fully explored. To better extract spatial-temporal features, we propose a multi-stage attention-enhanced sparse graph convolutional network (MS-ASGCN) for skeleton-based action recognition. To capture more abundant joint dependencies, we propose a new strategy for constructing skeleton graphs. This simulates bidirectional information flows between neighboring joints and pays greater attention to the information transmission between sparse joints. In addition, a part attention mechanism is proposed to learn the weight of each part and enhance the part-level feature learning. We introduce multiple streams of different stages and merge them in specific layers of the network to further improve the performance of the model. Our model is finally verified on two large-scale datasets, namely NTU-RGB+D and Skeleton-Kinetics. Experiments demonstrate that the proposed MS-ASGCN outperformed the previous state-of-the-art methods on both datasets. |
| Starting Page | 2198 |
| e-ISSN | 20799292 |
| DOI | 10.3390/electronics10182198 |
| Journal | Electronics |
| Issue Number | 18 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-09-08 |
| Access Restriction | Open |
| Subject Keyword | Electronics Artificial Intelligence Graph Convolutional Networks Skeleton-based Action Recognition Spatial-temporal Graphs Multi-stage Streams |
| Content Type | Text |
| Resource Type | Article |
