Feedback Graph Convolutional Network for Skeleton-based Action Recognition

Understand

Skeleton-based action recognition has attracted considerable attention in computer vision since skeleton data is more robust to the dynamic circumstance and complicated background than other modalities.

• Recently, many researchers have used the Graph Convolutional Network (GCN) to model spatial-temporal features of skeleton sequences by an end-to-end optimization.
• However, conventional GCNs are feedforward networks which are impossible for low-level layers to access semantic information in the high-level layers.
• In this paper, we propose a novel network, named Feedback Graph Convolutional Network (FGCN).