Research map / Graph learning / Knowledge and temporal graphs
Graph CNNs with Motif and Variable Temporal Block for Skeleton-Based Action Recognition
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| motif-based graph convolution instead of uni-labeling GCN (ablation) | skeleton GCN · Kinetics-M | top-1 accuracy: 76.4% -> 82.5% | Helped |
| add variable temporal dense block (VTDB) (ablation) | motif-GCNs · Kinetics-M | top-1 accuracy: 82.5% -> 83.3% | Helped |
| add non-local block (ablation) | motif-GCNs · Kinetics-M | top-1 accuracy: 82.5% -> 83.2% | Helped |
| add non-local block (ablation) | motif-GCNs + VTDB · Kinetics-M | top-1 accuracy: 83.3% -> 84.2% | Helped |
| increase VTDB input channels and growth rate (ablation) | full model · Kinetics-M | top-1 accuracy: 82.9% -> 83.5% | Helped |
| full model vs STGCN | skeleton action recognition · Kinetics-M | top-1 accuracy: 79.7% -> 84.2% | Helped |
Closest papers by meaning
- Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition (AAAI 2018 · similarity 0.94)
- Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition (CVPR 2019 · similarity 0.93)
- Dynamic Semantic-Based Spatial Graph Convolution Network for Skeleton-Based Human Action Recognition (AAAI 2024 · similarity 0.92)
- Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action Recognition (CVPR 2025 · similarity 0.92)
- BlockGCN: Redefine Topology Awareness for Skeleton-Based Action Recognition (CVPR 2024 · similarity 0.91)
- Heterogeneous Skeleton-Based Action Representation Learning (CVPR 2025 · similarity 0.90)
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