Simba: Mamba augmented U-ShiftGCN for Skeletal Action Recognition in Videos

arXiv:2404.07645v1 Announce Type: new
Abstract: Skeleton Action Recognition (SAR) involves identifying human actions using skeletal joint coordinates and their interconnections. While plain Transformers have been attempted for this task, they still fall short compared to the current leading methods, which are rooted in Graph Convolutional Networks (GCNs) due to the absence of structural priors. Recently, a novel selective state space model, Mamba, has surfaced as a compelling alternative to the attention mechanism in Transformers, offering efficient modeling of long sequences. …

abstract action recognition arxiv cs.cv current graph human mamba networks recognition transformers type videos