  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.
