Papers › Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention

Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention

20 Jul 2019arXiv:1907.08871archive 2025-07-28

Yuxiao Chen, Long Zhao, Xi Peng, Jianbo Yuan, Dimitris N. Metaxas

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.

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Tasks

Gesture RecognitionHand Gesture RecognitionHand-Gesture RecognitionSkeleton Based Action Recognition

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Hand Gesture Recognition DHG-14 DG-STA Accuracy 91.9 #6 of 13 Archive leaderboard report
Hand Gesture Recognition DHG-28 DG-STA Accuracy 88 #7 of 9 Archive leaderboard report
Hand Gesture Recognition SHREC 2017 DG-STA 14 Gestures Accuracy 94.4 #4 of 4 Archive leaderboard report
Hand Gesture Recognition SHREC 2017 DG-STA 28 Gestures Accuracy 90.7 #4 of 4 Archive leaderboard report

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