Decoupled Knowledge Embedded Graph Convolutional Network for Skeleton-Based Human Action Recognition

Skeleton-based action recognition has broad prospects owing to the fact that skeleton data is more robust to scene noise and camera view changes. Recently, researchers mainly aim to explore deep-learning feature engineering with competitive recognition accuracy for skeleton actions. However, a high-performance recognition network is usually stacked by complex feature extraction modules introducing massive computational costs. In this work, we designed a powerful and universal action knowledge distillation paradigm based on decoupled knowledge distillation for transferring action knowledge from heavy teachers to lightweight students more robustly. We constructed a network architecture space consisting of the shrinking versions of outdated 2s-AGCN and searched for several robust students. On this basis, this paradigm is further developed into a powerful decoupled knowledge embedded graph convolutional network (DKE-GCN), which outperforms the teacher significantly on three public datasets and achieves the state-of-the-art. In addition, a light-DKE-GCN is designed to achieve comparable performance with teacher with 16x less parameters, 26x less FLOPs and 8x FPS.