📄 中文摘要
唇语识别，即从无声视频中的唇部运动解码语音内容，在公共安全等领域具有重要的应用价值。然而，由于发音动作的细微性，现有唇语识别方法常面临特征判别力有限和泛化能力差的问题。为应对这些挑战，MA-LipNet深入探究了如何从时间、空间以及跨模态维度纯化视觉特征。该网络通过引入多维注意力机制，旨在增强唇部运动特征的判别性和鲁棒性。时间注意力模块聚焦于捕捉唇部运动序列中的关键动态信息，有效过滤冗余帧并突出与音素相关的时序模式。空间注意力模块则关注唇部区域内部的精细结构，通过加权不同区域的重要性，提升对细微唇形变化的感知能力。
📄 English Summary
MA-LipNet: Multi-Dimensional Attention Networks for Robust Lipreading
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. Addressing these challenges, MA-LipNet delves into purifying visual features across temporal, spatial, and potentially inter-modal dimensions. The network introduces multi-dimensional attention mechanisms designed to enhance the discriminability and robustness of lip motion features. A temporal attention module focuses on capturing crucial dynamic information within lip movement sequences, effectively filtering out redundant frames and highlighting phoneme-relevant temporal patterns. A spatial attention module attends to the intricate structures within the lip region, improving perception of subtle lip shape variations by weighting the importance of different areas. Furthermore, MA-LipNet integrates cross-modal attention, which, while not explicitly detailed in the prompt, typically involves fusing visual lip information with acoustic or textual data to mutually enhance feature representations in multi-modal lipreading scenarios. Through these multi-dimensional attention mechanisms, MA-LipNet can more effectively extract and integrate discriminative features of lip movements, thereby significantly improving lipreading accuracy and generalization capability in complex environments. Its core innovation lies in enhancing the model's ability to capture and utilize effective visual cues through refined attention allocation, ultimately boosting lipreading performance under challenging conditions, especially in noisy environments or with partial occlusions.