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SAR-NAS: Skeleton-based action recognition via neural architecture searching

Journal Article


Abstract


  • This paper presents a study of automatic design of neural network architectures for skeleton-based action recognition. Specifically, we encode a skeleton-based action instance into a tensor and carefully define a set of operations to build two types of network cells: normal cells and reduction cells. The recently developed DARTS (Differentiable Architecture Search) is adopted to search for an effective network architecture that is built upon the two types of cells. All operations are 2D based in order to reduce the overall computation and search space. Experiments on the challenging NTU RGB+D and Kinectics datasets have verified that most of the networks developed to date for skeleton-based action recognition are likely not compact and efficient. The proposed method provides an approach to search for such a compact network that is able to achieve comparative or even better performance than the state-of-the-art methods.

Publication Date


  • 2020

Citation


  • Zhang, H., Hou, Y., Wang, P., Guo, Z., & Li, W. (2020). SAR-NAS: Skeleton-based action recognition via neural architecture searching. Journal of Visual Communication and Image Representation, 73. doi:10.1016/j.jvcir.2020.102942

Scopus Eid


  • 2-s2.0-85095453939

Volume


  • 73

Abstract


  • This paper presents a study of automatic design of neural network architectures for skeleton-based action recognition. Specifically, we encode a skeleton-based action instance into a tensor and carefully define a set of operations to build two types of network cells: normal cells and reduction cells. The recently developed DARTS (Differentiable Architecture Search) is adopted to search for an effective network architecture that is built upon the two types of cells. All operations are 2D based in order to reduce the overall computation and search space. Experiments on the challenging NTU RGB+D and Kinectics datasets have verified that most of the networks developed to date for skeleton-based action recognition are likely not compact and efficient. The proposed method provides an approach to search for such a compact network that is able to achieve comparative or even better performance than the state-of-the-art methods.

Publication Date


  • 2020

Citation


  • Zhang, H., Hou, Y., Wang, P., Guo, Z., & Li, W. (2020). SAR-NAS: Skeleton-based action recognition via neural architecture searching. Journal of Visual Communication and Image Representation, 73. doi:10.1016/j.jvcir.2020.102942

Scopus Eid


  • 2-s2.0-85095453939

Volume


  • 73