Simba: A Mamba-Augmented U-ShiftGCN Framework for Efficient Skeletal Action Recognition

The core message of this work is to introduce the first skeletal action recognition framework that incorporates the Mamba selective state space model for efficient temporal sequence modeling on graph data, resulting in state-of-the-art performance across benchmark datasets.