  Recently, Transformer-based methods have been utilized to improve the
performance of human action recognition. However, most of these studies assume
that multi-view data is complete, which may not always be the case in
real-world scenarios. Therefore, this paper presents a novel Multi-view
Knowledge Distillation Transformer (MKDT) framework that consists of a teacher
network and a student network. This framework aims to handle incomplete human
action problems in real-world applications. Specifically, the multi-view
knowledge distillation transformer uses a hierarchical vision transformer with
shifted windows to capture more spatial-temporal information. Experimental
results demonstrate that our framework outperforms the CNN-based method on
three public datasets.
