Multi-Range Mixed Graph Convolution Network for Skeleton-Based Action Recognition.

Skeleton-based action recognition is a long-standing task in computer vision which aims to distinguish different human actions by identifying their unique characteristic patterns in the input data. Most of the existing Graph Convolutional Network (GCN) models developed for this task primarily model the skeleton graph as either directed or undirected. Furthermore, these models also restrict the receptive field in the temporal domain to a fixed range which significantly inhibits their expressive power. Therefore, we propose a mixed graph network comprising both directed and undirected graph networks with a multi-range temporal module called MMGCN. In this way, the model can benefit from the different interpretations of the same action by the different graphs. Moreover, the multi-range temporal module enhances the model's expressive power as it can choose the appropriate receptive field for each layer, thus allowing the model to dynamically adapt to the input data. With this lightweight MMGCN model, we further show that deep learning models can learn the underlying patterns in the data and model large receptive fields without additional semantics or high model complexity. Finally, this model achieved state-of-the-art results on three benchmark datasets namely NTU RGB+D, NTU RGB+D 120 and Northwestern-UCLA despite its low model complexity thus proving its effectiveness.