A Depression Detection Method Based on Multi-Modal Feature Fusion Using Cross-Attention

Shengjie Li; Yinhao Xiao

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arXiv · 2407.12825

A Depression Detection Method Based on Multi-Modal Feature Fusion Using Cross-Attention

Shengjie Li·Yinhao Xiao

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Abstract

Depression, a prevalent and serious mental health issue, affects approximately 3.8\% of the global population. Despite the existence of effective treatments, over 75\% of individuals in low- and middle-income countries remain untreated, partly due to the challenge in accurately diagnosing depression in its early stages. This paper introduces a novel method for detecting depression based on multi-modal feature fusion utilizing cross-attention. By employing MacBERT as a pre-training model to extract lexical features from text and incorporating an additional Transformer module to refine task-specific contextual understanding, the model's adaptability to the targeted task is enhanced. Diverging from previous practices of simply concatenating multimodal features, this approach leverages cross-attention for feature integration, significantly improving the accuracy in depression detection and enabling a more comprehensive and precise analysis of user emotions and behaviors. Furthermore, a Multi-Modal Feature Fusion Network based on Cross-Attention (MFFNC) is constructed, demonstrating exceptional performance in the task of depression identification. The experimental results indicate that our method achieves an accuracy of 0.9495 on the test dataset, marking a substantial improvement over existing approaches. Moreover, it outlines a promising methodology for other social media platforms and tasks involving multi-modal processing. Timely identification and intervention for individuals with depression are crucial for saving lives, highlighting the immense potential of technology in facilitating early intervention for mental health issues.

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BibTeX RIS

Shengjie Li, Yinhao Xiao. 2024-07-02. A Depression Detection Method Based on Multi-Modal Feature Fusion Using Cross-Attention. https://arxiv.org/abs/2407.12825

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