用多查询机制修复不完整生理信号，提升情绪识别准确率
Multi-Masked Querying Network for Robust Emotion Recognition from Incomplete Multi-Modal Physiological Signals
- 设计多查询网络，分别处理缺失数据、情绪特征和干扰噪声
- 在高缺失率下仍保持优异识别性能，显著优于现有方法
- 适合心理评估、可穿戴设备等真实场景中的情绪分析
从生理信号中进行情绪识别对心理健康评估至关重要，但面临两大挑战：多模态信号不完整以及身体运动与伪影干扰。本文提出一种新型多掩码查询网络（MMQ-Net），通过将多种查询机制整合到统一框架中来解决这些问题。具体而言，模态查询用于从不完整信号中重建缺失数据，类别查询聚焦于情绪状态特征，干扰查询则分离出相关信号与噪声。大量实验结果表明，相较于现有方法，MMQ-Net在高数据缺失条件下展现出更优的情绪识别性能。
原文摘要 · Abstract (English)
Emotion recognition from physiological data is crucial for mental health assessment, yet it faces two significant challenges: incomplete multi-modal signals and interference from body movements and artifacts. This paper presents a novel Multi-Masked Querying Network (MMQ-Net) to address these issues by integrating multiple querying mechanisms into a unified framework. Specifically, it uses modality queries to reconstruct missing data from incomplete signals, category queries to focus on emotional state features, and interference queries to separate relevant information from noise. Extensive experiment results demonstrate the superior emotion recognition performance of MMQ-Net compared to existing approaches, particularly under high levels of data incompleteness.
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