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RAL:Redundancy-Aware Lipreading Model Based on Differential Learning with Symmetric Views

Zejun gu, Junxia jiang

TL;DR

This paper addresses the limitation of treating the lips as a symmetric whole in lip-reading models by introducing a differential learning framework over symmetric left/right views. The proposed RAL model combines a differential learning strategy with symmetric views (DLSV), a redundancy-aware operation (RAO) to suppress non-informative content, and an adaptive cross-view interaction module (ACVI) to capture cross-view and intra-view relations, all integrated into a 3D-CNN backbone and MSTCN temporal decoder. Empirical results on LRW and LRW-1000 show consistent performance gains, with LRW reaching 89.3% accuracy (+4.0 over baselines) and LRW-1000 achieving 46.5% (+5.1). The work demonstrates that exploiting asymmetry between lip halves and removing redundancy can substantially improve lip-reading accuracy and efficiency, with potential impact on cross-language and real-time applications.

Abstract

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.

RAL:Redundancy-Aware Lipreading Model Based on Differential Learning with Symmetric Views

TL;DR

This paper addresses the limitation of treating the lips as a symmetric whole in lip-reading models by introducing a differential learning framework over symmetric left/right views. The proposed RAL model combines a differential learning strategy with symmetric views (DLSV), a redundancy-aware operation (RAO) to suppress non-informative content, and an adaptive cross-view interaction module (ACVI) to capture cross-view and intra-view relations, all integrated into a 3D-CNN backbone and MSTCN temporal decoder. Empirical results on LRW and LRW-1000 show consistent performance gains, with LRW reaching 89.3% accuracy (+4.0 over baselines) and LRW-1000 achieving 46.5% (+5.1). The work demonstrates that exploiting asymmetry between lip halves and removing redundancy can substantially improve lip-reading accuracy and efficiency, with potential impact on cross-language and real-time applications.

Abstract

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.
Paper Structure (16 sections, 8 equations, 4 figures, 3 tables)

This paper contains 16 sections, 8 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Method
  4. Overview
  5. Differential learning strategy with symmetric views
  6. Redundancy-aware operation
  7. Adaptive cross-view interaction module
  8. Experiments
  9. Experimental Settings
  10. Datasets
  11. Preprocessing
  12. Implementations
  13. Experimental Results
  14. Comparison with other related works
  15. Ablation Study
  16. ...and 1 more sections

Figures (4)

  • Figure 1: Comparison between left and right half-lip views in lip-reading video frames. In many lip-reading images, there are significant differences between the left and right half-lip views.
  • Figure 2: Comparison of Different Lip Reading Model Architectures. (a) TCN. (b) WPCL. (c) UDP. (d) our RAL model.
  • Figure 3: Schematic illustration of the proposed RAL model.
  • Figure 4: The structure of our proposed ACVI module.