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

arXiv: https://arxiv.org/abs/2409.05307 · HTML: https://mlsanity.com/paper/2409.05307/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 36 references that are in the map.
- First posted on arXiv 9 Sep 2024.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction](https://mlsanity.com/paper/2201.02184/) (2022)
- [Lipreading using Temporal Convolutional Networks](https://mlsanity.com/paper/2001.08702/) (2020)
- [Visual Speech Recognition for Multiple Languages in the Wild](https://mlsanity.com/paper/2202.13084/) (2022)
- [ASR is all you need: cross-modal distillation for lip reading](https://mlsanity.com/paper/1911.12747/) (2019)
- [LipNet: End-to-End Sentence-level Lipreading](https://mlsanity.com/paper/1611.01599/) (2016)
- [End-to-end Audiovisual Speech Recognition](https://mlsanity.com/paper/1802.06424/) (2018)
- [Combining Residual Networks with LSTMs for Lipreading](https://mlsanity.com/paper/1703.04105/) (2017)
- [LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild](https://mlsanity.com/paper/1810.06990/) (2018)
- [Towards Practical Lipreading with Distilled and Efficient Models](https://mlsanity.com/paper/2007.06504/) (2020)
- [LiRA: Learning Visual Speech Representations from Audio through Self-supervision](https://mlsanity.com/paper/2106.09171/) (2021)
- [Distinguishing Homophenes Using Multi-Head Visual-Audio Memory for Lip Reading](https://mlsanity.com/paper/2204.01725/) (2022)
- [Can We Read Speech Beyond the Lips? Rethinking RoI Selection for Deep Visual Speech Recognition](https://mlsanity.com/paper/2003.03206/) (2020)
- [Lip-reading with Densely Connected Temporal Convolutional Networks](https://mlsanity.com/paper/2009.14233/) (2020)
- [A Cascade Sequence-to-Sequence Model for Chinese Mandarin Lip Reading](https://mlsanity.com/paper/1908.04917/) (2019)
- [Deformation Flow Based Two-Stream Network for Lip Reading](https://mlsanity.com/paper/2003.05709/) (2020)
- [Speaker-adaptive Lip Reading with User-dependent Padding](https://mlsanity.com/paper/2208.04498/) (2022)
- [Pseudo-Convolutional Policy Gradient for Sequence-to-Sequence Lip-Reading](https://mlsanity.com/paper/2003.03983/) (2020)
- [Multi-Grained Spatio-temporal Modeling for Lip-reading](https://mlsanity.com/paper/1908.11618/) (2019)

## Abstract (arXiv)

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.