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

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.

Peng Wei, Hong Xiaopeng, Chen Haoyu, Zhao Guoying

A4 Article in conference proceedings

Proceedings of the 43th AAAI Conference on Artificial Intelligence. February 7–12, 2020, New York, USA

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

https://doi.org/10.1609/aaai.v34i03.5652 http://urn.fi/urn:nbn:fi-fe202104099803