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Training a real-time gesture recognition model heavily relies on annotateddata. However, manual data annotation is costly and demands substantial humaneffort. In order to address this challenge, we propose a novel annotation modelthat can automatically annotate gesture classes and identify their temporalranges. Our ablation study demonstrates that our annotation model designsurpasses the baseline in terms of both gesture classification accuracy (3-4\%improvement) and localization accuracy (71-75\% improvement). We believe thatthis annotation model has immense potential to improve the training ofdownstream gesture recognition models using unlabeled datasets.

Simultaneous Gesture Classification and Localization with an Automatic Gesture Annotation Model