RAL:Redundancy-Aware Lipreading Model Based on Differential Learning with Symmetric Views
This work addresses lip reading for speech interpretation, but it is incremental as it builds on existing models by focusing on asymmetrical lip differences and redundancy reduction.
The paper tackled the problem of lip reading by addressing the lack of investigation into asymmetrical lip movements and redundant information in input images, proposing a differential learning strategy with symmetric views, redundancy-aware operation, and adaptive cross-view interaction module, achieving effectiveness demonstrated on LRW and LRW-1000 datasets.
Lip reading involves interpreting a speaker's speech by analyzing sequences of lip movements. Currently, most models regard the left and right halves of the lips as a symmetrical whole, lacking a thorough investigation of their differences. However, the left and right halves of the lips are not always symmetrical, and the subtle differences between them contain rich semantic information. In this paper, we propose a differential learning strategy with symmetric views (DLSV) to address this issue. Additionally, input images often contain a lot of redundant information unrelated to recognition results, which can degrade the model's performance. We present a redundancy-aware operation (RAO) to reduce it. Finally, to leverage the relational information between symmetric views and within each view, we further design an adaptive cross-view interaction module (ACVI). Experiments on LRW and LRW-1000 datasets fully demonstrate the effectiveness of our approach.