Research map / Graph learning / Graph self-supervised learning
Graph Contrastive Learning for Skeleton-based Action Recognition
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add SkeletonGCL graph contrast | CTR-GCN · NTU60 X-Sub J | accuracy: 89.8% to 90.8% | Helped |
| Add SkeletonGCL graph contrast | CTR-GCN · NTU120 X-Sub J | top-1 accuracy: 84.9 -> 85.6 | Helped |
| Add SkeletonGCL graph contrast | CTR-GCN · NW-UCLA J | top-1 accuracy: 94.6 -> 95.1 | Helped |
| Use inter-batch memory contrast instead of intra-batch contrast (ablation) | CTR-GCN on NTU60 X-Sub J · NTU60 X-Sub | accuracy: 90.2 -> 90.8 | Helped |
| Contrast learned graphs instead of features (ablation) | CTR-GCN on NTU60 X-Sub J · NTU60 X-Sub | accuracy: 90.2 -> 90.8 | Helped |
| Combine instance-level and semantic-level memory banks rather than using either alone (ablation) | CTR-GCN on NTU60 X-Sub J · NTU60 X-Sub | accuracy: 90.3 -> 90.8; 90.2 -> 90.8 | Helped |
| Use hard positive and negative sampling, with random negatives (ablation) | CTR-GCN on NTU60 X-Sub J · NTU60 X-Sub | accuracy: 90.1 -> 90.8 | Helped |
| Replace InfoNCE with triplet loss (ablation) | CTR-GCN on NTU60 X-Sub J · NTU60 X-Sub | accuracy: 90.8 vs. 90.7 | No clear effect |
| Supervise graph learning directly with cross-entropy (ablation) | CTR-GCN on NTU60 X-Sub J · NTU60 X-Sub | accuracy: 89.8% to 89.7% | Hurt |
| Add a vertex-aware FC layer to graph projection (ablation) | CTR-GCN on NTU60 X-Sub J · NTU60 X-Sub | accuracy: 90.1 -> 90.8 | Helped |
| Apply graph contrast at deeper rather than shallower layers (ablation) | CTR-GCN on NTU60 X-Sub J · NTU60 X-Sub | accuracy: 89.8 -> 90.8 | Helped |
| Increase instance memory size from P=128 to P=684 (ablation) | CTR-GCN on NTU60 X-Sub J · NTU60 X-Sub | accuracy: 90.3 -> 90.8 | Helped |
Closest papers by meaning
- Context Aware Graph Convolution for Skeleton-Based Action Recognition (CVPR 2020 · similarity 0.92)
- Learning Graph Convolutional Network for Skeleton-Based Human Action Recognition by Neural Searching (AAAI 2020 · similarity 0.92)
- Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition (CVPR 2019 · similarity 0.92)
- Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action Recognition (CVPR 2025 · similarity 0.90)
- Dynamic Semantic-Based Spatial Graph Convolution Network for Skeleton-Based Human Action Recognition (AAAI 2024 · similarity 0.90)
- BlockGCN: Redefine Topology Awareness for Skeleton-Based Action Recognition (CVPR 2024 · similarity 0.89)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.