De-biasing Skeleton-based Action Recognition with Convex Hull Adaptive Shift

arXiv:2609.32454 · cs.CV, cs.AI, cs.LG · Submitted 2026-09-26 · Read on arXiv

cs.CV, cs.AI, cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/Necolizer/CHASE

Project page: https://assembly-101.github.io

Terminology

• Benchmark — 21× in this paper · explained in 2 episodes
• Adaptive Shift — 19× in this paper
• Transformer — 19× in this paper · explained in 2 episodes
• Convex Hull Adaptive Shift — 14× in this paper
• Accuracy — 12× in this paper · explained in 1 episode
• Computer Vision and Pattern Recognition — 11× in this paper
• CVF International Conference on Computer Vision — 6× in this paper
• European Conference on Computer Vision — 6× in this paper
• Late Fusion — 6× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Alignment — 5× in this paper · explained in 1 episode
• Adaptability — 4× in this paper · explained in 1 episode
• Adaptation — 4× in this paper · explained in 1 episode
• Hypergraph — 4× in this paper · explained in 2 episodes
• Actor — 3× in this paper · explained in 3 episodes
• Core — 3× in this paper · explained in 2 episodes
• Covariate Shift — 3× in this paper · explained in 2 episodes
• Human-Robot Interactions — 3× in this paper
• Representation Learning — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Adversarial Training — 2× in this paper · explained in 1 episode
• Attention Mechanism — 2× in this paper · explained in 3 episodes
• Coefficient Learning Block — 2× in this paper
• Collective Activity Dataset — 2× in this paper
• Distribution Matching — 2× in this paper · explained in 2 episodes
• Domain Adaptation — 2× in this paper · explained in 5 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Volleyball Dataset — 2× in this paper
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adaptive Graph — 1× in this paper · explained in 1 episode
• Adaptivity — 1× in this paper · explained in 1 episode
• Bhattacharyya Distance — 1× in this paper
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Entity Rearrangement — 1× in this paper
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Hellinger Distance — 1× in this paper
• Hilbert Space — 1× in this paper · explained in 2 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• International Conference on Learning Representations — 1× in this paper
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• Layer Normalization
• Scaling Up Dynamic Human-Scene Interaction Modeling
• TSGCNeXt: Dynamic-Static Multi-Graph Convolution for Efficient Skeleton-Based Action Recognition with Long-term Learning Potential
• STEP CATFormer: Spatial-Temporal Effective Body-Part Cross Attention Transformer for Skeleton-based Action Recognition
• In My Perspective, In My Hands: Accurate Egocentric 2D Hand Pose and Action Recognition
• Instance Normalization: The Missing Ingredient for Fast Stylization
• Language Knowledge-Assisted Representation Learning for Skeleton-Based Action Recognition
• Hypergraph Transformer for Skeleton-based Action Recognition

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