融合人格特质的多模态老年人抑郁检测模型
Personality-Enhanced Multimodal Depression Detection in the Elderly
- 用共注意力机制融合音频多种特征，构建跨模态表示
- 结合人格特征与视听数据，提升老年抑郁检测准确率
- 适合关注老龄化心理健康与多模态情感计算的研究者
本文针对ACM MM 2025的多模态人格感知抑郁检测挑战，提出一种面向老年人的多模态抑郁检测模型，引入人格特征以增强检测能力。音频模态中，采用共注意力机制融合低级声学特征（LLDs）、MFCCs和Wav2Vec特征；视频模态则整合OpenFace、ResNet与DenseNet提取的表征，构建全面视觉特征集。设计交互模块捕捉人格特质与多模态特征间的关联。在MPDD老年人抑郁检测赛道的实验表明，该方法显著提升性能，为老年人群多模态抑郁检测研究提供新思路。
原文摘要 · Abstract (English)
This paper presents our solution to the Multimodal Personality-aware Depression Detection (MPDD) challenge at ACM MM 2025. We propose a multimodal depression detection model in the Elderly that incorporates personality characteristics. We introduce a multi-feature fusion approach based on a co-attention mechanism to effectively integrate LLDs, MFCCs, and Wav2Vec features in the audio modality. For the video modality, we combine representations extracted from OpenFace, ResNet, and DenseNet to construct a comprehensive visual feature set. Recognizing the critical role of personality in depression detection, we design an interaction module that captures the relationships between personality traits and multimodal features. Experimental results from the MPDD Elderly Depression Detection track demonstrate that our method significantly enhances performance, providing valuable insights for future research in multimodal depression detection among elderly populations.
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