  We propose a Dynamic Graph-Based Spatial-Temporal Attention (DG-STA) method
for hand gesture recognition. The key idea is to first construct a
fully-connected graph from a hand skeleton, where the node features and edges
are then automatically learned via a self-attention mechanism that performs in
both spatial and temporal domains. We further propose to leverage the
spatial-temporal cues of joint positions to guarantee robust recognition in
challenging conditions. In addition, a novel spatial-temporal mask is applied
to significantly cut down the computational cost by 99%. We carry out extensive
experiments on benchmarks (DHG-14/28 and SHREC'17) and prove the superior
performance of our method compared with the state-of-the-art methods. The
source code can be found at https://github.com/yuxiaochen1103/DG-STA.
