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MA-LipNet: Multi-Dimensional Attention Networks for Robust Lipreading

Matteo Rossi

TL;DR

The paper tackles the robustness of lipreading by addressing multi-dimensional feature redundancy in visual data. It introduces MA-LipNet, which sequentially applies Channel Attention, Joint Spatial-Temporal Attention, and Separate Spatial-Temporal Attention within the visual encoder, followed by a Seq2Seq decoder with attention. The approach achieves state-of-the-art results on CMLR and GRID, supported by ablation studies and visualizations that confirm complementary contributions from each module. This multi-dimensional refinement enhances discriminability and generalization for visual speech recognition, with potential for broader deployment in noisy or privacy-sensitive contexts; future work includes exploring speaker-independent lipreading.

Abstract

Lipreading, the technology of decoding spoken content from silent videos of lip movements, holds significant application value in fields such as public security. However, due to the subtle nature of articulatory gestures, existing lipreading methods often suffer from limited feature discriminability and poor generalization capabilities. To address these challenges, this paper delves into the purification of visual features from temporal, spatial, and channel dimensions. We propose a novel method named Multi-Attention Lipreading Network(MA-LipNet). The core of MA-LipNet lies in its sequential application of three dedicated attention modules. Firstly, a \textit{Channel Attention (CA)} module is employed to adaptively recalibrate channel-wise features, thereby mitigating interference from less informative channels. Subsequently, two spatio-temporal attention modules with distinct granularities-\textit{Joint Spatial-Temporal Attention (JSTA)} and \textit{Separate Spatial-Temporal Attention (SSTA)}-are leveraged to suppress the influence of irrelevant pixels and video frames. The JSTA module performs a coarse-grained filtering by computing a unified weight map across the spatio-temporal dimensions, while the SSTA module conducts a more fine-grained refinement by separately modeling temporal and spatial attentions. Extensive experiments conducted on the CMLR and GRID datasets demonstrate that MA-LipNet significantly reduces the Character Error Rate (CER) and Word Error Rate (WER), validating its effectiveness and superiority over several state-of-the-art methods. Our work highlights the importance of multi-dimensional feature refinement for robust visual speech recognition.

MA-LipNet: Multi-Dimensional Attention Networks for Robust Lipreading

TL;DR

The paper tackles the robustness of lipreading by addressing multi-dimensional feature redundancy in visual data. It introduces MA-LipNet, which sequentially applies Channel Attention, Joint Spatial-Temporal Attention, and Separate Spatial-Temporal Attention within the visual encoder, followed by a Seq2Seq decoder with attention. The approach achieves state-of-the-art results on CMLR and GRID, supported by ablation studies and visualizations that confirm complementary contributions from each module. This multi-dimensional refinement enhances discriminability and generalization for visual speech recognition, with potential for broader deployment in noisy or privacy-sensitive contexts; future work includes exploring speaker-independent lipreading.

Abstract

Lipreading, the technology of decoding spoken content from silent videos of lip movements, holds significant application value in fields such as public security. However, due to the subtle nature of articulatory gestures, existing lipreading methods often suffer from limited feature discriminability and poor generalization capabilities. To address these challenges, this paper delves into the purification of visual features from temporal, spatial, and channel dimensions. We propose a novel method named Multi-Attention Lipreading Network(MA-LipNet). The core of MA-LipNet lies in its sequential application of three dedicated attention modules. Firstly, a \textit{Channel Attention (CA)} module is employed to adaptively recalibrate channel-wise features, thereby mitigating interference from less informative channels. Subsequently, two spatio-temporal attention modules with distinct granularities-\textit{Joint Spatial-Temporal Attention (JSTA)} and \textit{Separate Spatial-Temporal Attention (SSTA)}-are leveraged to suppress the influence of irrelevant pixels and video frames. The JSTA module performs a coarse-grained filtering by computing a unified weight map across the spatio-temporal dimensions, while the SSTA module conducts a more fine-grained refinement by separately modeling temporal and spatial attentions. Extensive experiments conducted on the CMLR and GRID datasets demonstrate that MA-LipNet significantly reduces the Character Error Rate (CER) and Word Error Rate (WER), validating its effectiveness and superiority over several state-of-the-art methods. Our work highlights the importance of multi-dimensional feature refinement for robust visual speech recognition.
Paper Structure (18 sections, 8 equations, 3 figures, 3 tables)

This paper contains 18 sections, 8 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Sentence-Level Lipreading
  4. Attention Mechanisms in Computer Vision
  5. The MA-LipNet Method
  6. Visual Front-end
  7. Multiple Visual Attention Modules
  8. Channel Attention (CA) Module
  9. Joint Spatial-Temporal Attention (JSTA) Module
  10. Separate Spatial-Temporal Attention (SSTA) Module
  11. Seq2Seq Decoder with Attention
  12. Experiments and Results
  13. Datasets and Evaluation Metrics
  14. Implementation Details
  15. Comparison with State-of-the-Art
  16. ...and 3 more sections

Figures (3)

  • Figure 1: Overall architecture of the proposed MA-LipNet model. The input video is first processed by a 3D-CNN backbone. The resulting features are then progressively refined by the Channel Attention (CA), Joint Spatial-Temporal Attention (JSTA), and Separate Spatial-Temporal Attention (SSTA) modules. The purified features are finally decoded into text by a Seq2Seq model with attention.
  • Figure 2: Comparison of saliency maps. From top to bottom: Baseline, Baseline+JSTA, Baseline+SSTA, MA-LipNet. MA-LipNet's attention is more precisely focused on the lip region compared to other variants.
  • Figure 3: Comparison of temporal attention weights across frames. The darker shading indicates lower weight. MA-LipNet more effectively suppresses non-speech frames (e.g., at the beginning and end) compared to models with only JSTA or SSTA.