Action recognition is a key technology in building interactive metaverses.
With the rapid development of deep learning, methods in action recognition have
also achieved great advancement. Researchers design and implement the backbones
referring to multiple standpoints, which leads to the diversity of methods and
encountering new challenges. This paper reviews several action recognition
methods based on deep neural networks. We introduce these methods in three
parts: 1) Two-Streams networks and their variants, which, specifically in this
paper, use RGB video frame and optical flow modality as input; 2) 3D
convolutional networks, which make efforts in taking advantage of RGB modality
directly while extracting different motion information is no longer necessary;
3) Transformer-based methods, which introduce the model from natural language
processing into computer vision and video understanding. We offer objective
sights in this review and hopefully provide a reference for future research.

该论文综述了基于深度神经网络的几种动作识别方法，包括两流网络、三维卷积网络和基于 Transformer 的方法，并提供了客观的观点以供未来研究参考。