(2+1)D Distilled ShuffleNet: A Lightweight Unsupervised Distillation Network for Human Action Recognition

While most existing deep neural networks (DNN) architectures are proposed for increasing performance, they also raise overall model complexity. However, practical applications require lightweight DNN models, that are able to run real-time in edge computing devices. In this work, we present a simple and elegant unsupervised distillation learning paradigm to train a lightweight network to human action recognition called (2+1)D Distilled ShuffleNet. Leveraging the distilling technique, the proposed method allows us to create a lightweight DNN model that achieves high accuracy and real-time speed. Our lightweight (2+1)D Distilled ShuffleNet is designed as an unsupervised paradigm; it does not require labelled data during distilling knowledge from the teacher to the student. Furthermore, to help the student be more "intelligent", we propose to distill the knowledge from two different teachers, i.e., 2D teacher and 3D teacher. The experimental results have shown that our lightweight (2+1)D Distilled ShuffleNet outperforms other state-of-the-art distillation networks with 86.4% and 59.9% top-1 accuracy on UCF101 and HMDB51 datasets, respectively, whereas the inference running time is at 47.16 FPS on CPU with only 17.1M parameters and 12.07 GFLOPs.