Summary of A Depression Detection Method Based on Multi-modal Feature Fusion Using Cross-attention, by Shengjie Li et al.
A Depression Detection Method Based on Multi-Modal Feature Fusion Using Cross-Attention
by Shengjie Li, Yinhao Xiao
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel method for detecting depression based on multi-modal feature fusion using cross-attention. By employing MacBERT as a pre-training model to extract lexical features from text and incorporating an additional Transformer module, the model’s adaptability is enhanced. The approach leverages cross-attention for feature integration, improving accuracy in depression detection and enabling comprehensive analysis of user emotions and behaviors. The proposed Multi-Modal Feature Fusion Network (MFFNC) demonstrates exceptional performance in depression identification, achieving an accuracy of 0.9495 on the test dataset. This marks a substantial improvement over existing approaches and outlines a promising methodology for other social media platforms and tasks involving multi-modal processing. The study highlights the potential of technology in facilitating early intervention for mental health issues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us find depression earlier so we can help people sooner. It uses special computer models to look at words and pictures on social media to figure out if someone might be feeling sad or depressed. This is important because right now, most people who are depressed don’t get the help they need. The new method is better than what’s been tried before and could make it easier for people to know when someone needs help. It’s really important that we can identify depression quickly so people can get treatment and feel better. This new way of using computers might be able to do just that, and it could help a lot of people. |
Keywords
* Artificial intelligence * Cross attention * Multi modal * Transformer
