  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.
