Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
Spatial-temporal graphs have been widely used by skeleton-based action recognition algorithms to model human action dynamics. To capture robust movement patterns from these graphs, long-range and multi-scale context aggregation and spatial-temporal dependency modeling are critical aspects of a powerful feature extractor. However, existing methods have limitations in achieving (1) unbiased …