Papers › Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition
Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition
Fanfan Ye, ShiLiang Pu, Qiaoyong Zhong, Chao Li, Di Xie, Huiming Tang
Graph 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 Contextencoding 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 2×~4× 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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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Skeleton Based Action Recognition | Kinetics-Skeleton dataset | Dynamic GCN | Accuracy | 37.9 | #11 of 42 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | Dynamic GCN | Accuracy (CS) | 91.5 | #41 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | Dynamic GCN | Accuracy (CV) | 96.0 | #41 of 135 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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