The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition
Open accessAbstract
raph Convolutional Networks (GCNs) have attracted increasing interests for the task of skeleton-based action recognition. The key lies in the design of the graph structure, which encodes skeleton topology information. In this paper, we propose Dynamic GCN, in which a novel convolutional neural network named Context-encoding Network (CeN) is introduced to learn skeleton topology automatically. In particular, when learning the dependency between two joints, contextual features from the rest joints are incorporated in a global manner. CeN is extremely lightweight yet effective, and can be embedded into a graph convolutional layer. By stacking multiple CeN-enabled graph convolutional layers, we build Dynamic GCN. Notably, as a merit of CeN, dynamic graph topologies are constructed for different input samples as well as graph convolutional layers of various depths. Besides, three alternative context modeling architectures are well explored, which may serve as a guideline for future research on graph topology learning. CeN brings only ~7% extra FLOPs for the baseline model, and Dynamic GCN achieves better performance with 2x ~4x fewer FLOPs than existing methods. By further combining static physical body connections and motion modalities, we achieve state-of-the-art performance on three large-scale benchmarks, namely NTU-RGB+D, NTU-RGB+D 120 and Skeleton-Kinetics.
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Cites
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“We follow the same train-validation split as [33].”
BackgroundMethodInfluentialNTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
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Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
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NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
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Cited by
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
Spatio-temporal segments attention for skeleton-based action recognition
Qiu, H., Hou, B., Ren, B., & Zhang, X. (2023) · Neurocomputing · doi:10.1016/j.neucom.2022.10.084 · Cited by 86
Miao, S., Chen, L., Hu, R., & Luo, Y. (2022) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3550331 · Cited by 36
Metadata
- Authors
- Ye, F., Pu, S., Zhong, Q., Li, C., Xie, D., & Tang, H.
- Venue
- Proceedings of the 28th ACM International Conference on Multimedia
- Publisher
- ACM
- Published
- 2020-10-12
- Pages
- 55-63
- OpenAlex
- W3093411241
- Cited by
- 341
- References
- 34
- FWCI
- 11.68
- Type
- conference paper
- Language
- en
- Open access
- green
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- Ye, F., Pu, S., Zhong, Q., Li, C., Xie, D., & Tang, H. (2020). Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition. Proceedings of the 28th ACM International Conference on Multimedia. https://doi.org/10.1145/3394171.3413941
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