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.

本文提出了一种用于基于骨骼的动作识别的神经网络架构自动设计的研究。具体来说，我们将基于骨骼的动作实例编码为张量，并仔细定义一组操作以构建两种类型的网络单元：正常单元和归约单元。采用最新开发的DARTS（差异体系结构搜索）来搜索基于两种类型的单元的有效网络体系结构。所有操作均基于2D，以减少整体计算和搜索空间。在具有挑战性的NTU RGB + D和Kinectics数据集上进行的实验已经证明，迄今为止为基于骨骼的动作识别而开发的大多数网络可能都不紧凑且效率很高。