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

arXiv: https://arxiv.org/abs/2407.12825 · HTML: https://mlsanity.com/paper/2407.12825/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 43 references that are in the map.
- First posted on arXiv 2 Jul 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Pre-Trained Models: Past, Present and Future](https://mlsanity.com/paper/2106.07139/) (2021)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [Should You Mask 15% in Masked Language Modeling?](https://mlsanity.com/paper/2202.08005/) (2022)
- [Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation](https://mlsanity.com/paper/2104.08771/) (2021)
- [PERT: Pre-training BERT with Permuted Language Model](https://mlsanity.com/paper/2203.06906/) (2022)
- [Is Cross-Attention Preferable to Self-Attention for Multi-Modal Emotion Recognition?](https://mlsanity.com/paper/2202.09263/) (2022)
- [LERT: A Linguistically-motivated Pre-trained Language Model](https://mlsanity.com/paper/2211.05344/) (2022)

## Built on by (most cited first, 1 of 1)

- [Multi-modal clothing recommendation model based on large model and VAE enhancement](https://mlsanity.com/paper/2410.02219/) (2024)

## Abstract (arXiv)

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.