Fusing Higher-Order Features in Graph Neural Networks for Skeleton-Based Action Recognition
Fusing Higher-Order Features in Graph Neural Networks for Skeleton-Based Action Recognition
Skeleton sequences are lightweight and compact and thus are ideal candidates for action recognition on edge devices. Recent skeleton-based action recognition methods extract features from 3-D joint coordinates as spatial–temporal cues, using these representations in a graph neural network for feature fusion to boost recognition performance. The use of first-and …