Towards Practical Lipreading with Distilled and Efficient Models

Understand

Lipreading has witnessed a lot of progress due to the resurgence of neural networks.

• Recent works have placed emphasis on aspects such as improving performance by finding the optimal architecture or improving generalization.
• However, there is still a significant gap between the current methodologies and the requirements for an effective deployment of lipreading in practical scenarios.
• In this work, we propose a series of innovations that significantly bridge that gap: first, we raise the state-of-the-art performance by a wide margin on LRW and LRW-1000 to 88.5% and 46.6%, respectively using self-distillation.