The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
Open accessAbstract
In skeleton-based action recognition, graph convolutional networks (GCNs), which model the human body skeletons as spatiotemporal graphs, have achieved remarkable performance. However, in existing GCN-based methods, the topology of the graph is set manually, and it is fixed over all layers and input samples. This may not be optimal for the hierarchical GCN and diverse samples in action recognition tasks. In addition, the second-order information (the lengths and directions of bones) of the skeleton data, which is naturally more informative and discriminative for action recognition, is rarely investigated in existing methods. In this work, we propose a novel two-stream adaptive graph convolutional network (2s-AGCN) for skeleton-based action recognition. The topology of the graph in our model can be either uniformly or individually learned by the BP algorithm in an end-to-end manner. This data-driven method increases the flexibility of the model for graph construction and brings more generality to adapt to various data samples. Moreover, a two-stream framework is proposed to model both the first-order and the second-order information simultaneously, which shows notable improvement for the recognition accuracy. Extensive experiments on the two large-scale datasets, NTU-RGBD and Kinetics-Skeleton, demonstrate that the performance of our model exceeds the state-of-the-art with a significant margin.
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Cites
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Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
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Cited by
Revisiting 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
Constructing Stronger and Faster Baselines for Skeleton-Based Action Recognition
Song, Y.-F., Zhang, Z., Shan, C., & Wang, L. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3157033 · Cited by 528
Not only Look, But Also Listen: Learning Multimodal Violence Detection Under Weak Supervision
Wu, P., Liu, J., Shi, Y., Sun, Y., Shao, F., Wu, Z., & Yang, Z. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58577-8_20 · Cited by 482
Pareek, P., & Thakkar, A. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-020-09904-8 · Cited by 455
Metadata
- Authors
- Shi, L., Zhang, Y., Cheng, J., & Lu, H.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 12018-12027
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2948058585
- Cited by
- 2,071
- References
- 38
- FWCI
- 64.94
- Type
- conference paper
- Language
- en
- Open access
- green
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