Simultaneous Gesture Classification and Localization with an Automatic
  Gesture Annotation Model
Junxiao Shen Xuhai Xu Ran Tan Amy Karlson Evan Strasnick
Training a real-time gesture recognition model heavily relies on annotated
data. However, manual data annotation is costly and demands substantial human
effort. In order to address this challenge, we propose a novel annotation model
that can automatically annotate gesture classes and identify their temporal
ranges. Our ablation study demonstrates that our annotation model design
surpasses the baseline in terms of both gesture classification accuracy (3-4\%
improvement) and localization accuracy (71-75\% improvement). We believe that
this annotation model has immense potential to improve the training of
downstream gesture recognition models using unlabeled datasets.