Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition

Understand

Skeleton data is of low dimension.

• However, there is a trend of using very deep and complicated feedforward neural networks to model the skeleton sequence without considering the complexity in recent year.
• In this paper, a simple yet effective multi-scale semantics-guided neural network (MS-SGN) is proposed for skeleton-based action recognition.
• We explicitly introduce the high level semantics of joints (joint type and frame index) into the network to enhance the feature representation capability of joints.