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

• URL: http://arxiv.org/abs/2111.03993v1
• Date: Sun, 7 Nov 2021 03:50:50 GMT
• Title: Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
• Authors: Pengfei Zhang and Cuiling Lan and Wenjun Zeng and Junliang Xing and Jianru Xue and Nanning Zheng
• Abstract summary: A simple yet effective multi-scale semantics-guided neural network is proposed for skeleton-based action recognition. MS-SGN achieves the state-of-the-art performance on the NTU60, NTU120, and SYSU datasets.
• Score: 140.18376685167857
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Moreover, a multi-scale strategy is proposed to be robust to the temporal scale variations. In addition, we exploit the relationship of joints hierarchically through two modules, i.e., a joint-level module for modeling the correlations of joints in the same frame and a frame-level module for modeling the temporal dependencies of frames. With an order of magnitude smaller model size than most previous methods, MSSGN achieves the state-of-the-art performance on the NTU60, NTU120, and SYSU datasets.

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