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.

翻译：本文介绍了我们在ACM MM 2025多模态人格感知抑郁检测挑战赛中的解决方案。我们提出了一种融合人格特征的老年人多模态抑郁检测模型。我们引入了一种基于协同注意力机制的多特征融合方法，以有效整合音频模态中的LLDs、MFCCs和Wav2Vec特征。对于视频模态，我们结合了从OpenFace、ResNet和DenseNet提取的表征，构建了一个全面的视觉特征集。认识到人格在抑郁检测中的关键作用，我们设计了一个交互模块，用于捕捉人格特质与多模态特征之间的关系。MPDD老年人抑郁检测赛道的实验结果表明，我们的方法显著提升了性能，为未来老年人多模态抑郁检测研究提供了有价值的见解。