Learning Graph Convolutional Network for Skeleton-Based Human Action Recognition by Neural Searching


  • Wei Peng University of Oulu
  • Xiaopeng Hong Xi'an Jiaotong University
  • Haoyu Chen University of Oulu
  • Guoying Zhao University of Oulu




Human action recognition from skeleton data, fuelled by the Graph Convolutional Network (GCN) with its powerful capability of modeling non-Euclidean data, has attracted lots of attention. However, many existing GCNs provide a pre-defined graph structure and share it through the entire network, which can loss implicit joint correlations especially for the higher-level features. Besides, the mainstream spectral GCN is approximated by one-order hop such that higher-order connections are not well involved. All of these require huge efforts to design a better GCN architecture. To address these problems, we turn to Neural Architecture Search (NAS) and propose the first automatically designed GCN for this task. Specifically, we explore the spatial-temporal correlations between nodes and build a search space with multiple dynamic graph modules. Besides, we introduce multiple-hop modules and expect to break the limitation of representational capacity caused by one-order approximation. Moreover, a corresponding sampling- and memory-efficient evolution strategy is proposed to search in this space. The resulted architecture proves the effectiveness of the higher-order approximation and the layer-wise dynamic graph modules. To evaluate the performance of the searched model, we conduct extensive experiments on two very large scale skeleton-based action recognition datasets. The results show that our model gets the state-of-the-art results in term of given metrics.




How to Cite

Peng, W., Hong, X., Chen, H., & Zhao, G. (2020). Learning Graph Convolutional Network for Skeleton-Based Human Action Recognition by Neural Searching. Proceedings of the AAAI Conference on Artificial Intelligence, 34(03), 2669-2676. https://doi.org/10.1609/aaai.v34i03.5652



AAAI Technical Track: Humans and AI