Temporal Attention-Augmented Graph Convolutional Network for Efficient Skeleton-Based Human Action Recognition
Temporal Attention-Augmented Graph Convolutional Network for Efficient Skeleton-Based Human Action Recognition
Graph convolutional networks (GCNs) have been very successful in modeling non-Euclidean data structures, like sequences of body skeletons forming actions modeled as spatiotemporal graphs. Most GCN-based action recognition methods use deep feed-forward networks with high computational complexity to process all skeletons in an action. This leads to a high number …