The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition
Open accessAbstract
Action recognition with skeleton data has recently attracted much attention in computer vision. Previous studies are mostly based on fixed skeleton graphs, only capturing local physical dependencies among joints, which may miss implicit joint correlations. To capture richer dependencies, we introduce an encoder-decoder structure, called A-link inference module, to capture action-specific latent dependencies, i.e. actional links, directly from actions. We also extend the existing skeleton graphs to represent higher-order dependencies, i.e. structural links. Combing the two types of links into a generalized skeleton graph, We further propose the actional-structural graph convolution network (AS-GCN), which stacks actional-structural graph convolution and temporal convolution as a basic building block, to learn both spatial and temporal features for action recognition. A future pose prediction head is added in parallel to the recognition head to help capture more detailed action patterns through self-supervision. We validate AS-GCN in action recognition using two skeleton data sets, NTU-RGB+D and Kinetics. The proposed AS-GCN achieves consistently large improvement compared to the state-of-the-art methods. As a side product, AS-GCN also shows promising results for future pose prediction.
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Cites
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Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Yan, S., Xiong, Y., & Lin, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12328 · Cited by 5,410
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The Kinetics Human Action Video Dataset
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Cited by
Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition
Chen, Y., Zhang, Z., Yuan, C., Li, B., Deng, Y., & Hu, W. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01311 · Cited by 1,039
Skeleton-Based Action Recognition With Shift Graph Convolutional Network
Cheng, K., Zhang, Y., He, X., Chen, W., Cheng, J., & Lu, H. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00026 · Cited by 981
Self-citationRevisiting Skeleton-based Action Recognition
Duan, H., Zhao, Y., Chen, K., Lin, D., & Dai, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00298 · Cited by 834
Human Action Recognition From Various Data Modalities: A Review
Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., & Liu, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183112 · Cited by 601
Skeleton-Based Action Recognition With Multi-Stream Adaptive Graph Convolutional Networks
Shi, L., Zhang, Y., Cheng, J., & Lu, H. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3028207 · Cited by 553
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Metadata
- Authors
- Li, M., Chen, S., Chen, X., Zhang, Y., Wang, Y., & Tian, Q.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 3590-3598
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2940457086
- Cited by
- 1,230
- References
- 33
- FWCI
- 48.83
- Type
- conference paper
- Language
- en
- Open access
- green
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