TSM: Temporal Shift Module for Efficient Video Understanding

Understand

The explosive growth in video streaming gives rise to challenges on performing video understanding at high accuracy and low computation cost.

• Conventional 2D CNNs are computationally cheap but cannot capture temporal relationships; 3D CNN based methods can achieve good performance but are computationally intensive, making it expensive to deploy.
• In this paper, we propose a generic and effective Temporal Shift Module (TSM) that enjoys both high efficiency and high performance.
• Specifically, it can achieve the performance of 3D CNN but maintain 2D CNN's complexity.