Zihao Bi

Riemannian Graph Convolutional Network for Skeleton-Based Two-Person Interaction Recognition

In the field of skeleton-based human action recognition, Graph Convolutional Networks (GCNs) have become a dominant framework. However, existing GCN-based approaches often treat the sequences of two-person interaction as separate entities, ignoring the inherent semantic dependencies and spatial corr