# Depression Diagnosis and Analysis via Multimodal Multi-order Factor Fusion

arXiv: https://arxiv.org/abs/2301.00254 · HTML: https://mlsanity.com/paper/2301.00254/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 31 Dec 2022.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [YouTube-8M: A Large-Scale Video Classification Benchmark](https://mlsanity.com/paper/1609.08675/) (2016)
- [AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition](https://mlsanity.com/paper/1907.11510/) (2019)
- [ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information](https://mlsanity.com/paper/2106.16038/) (2021)
- [CubeMLP: An MLP-based Model for Multimodal Sentiment Analysis and Depression Estimation](https://mlsanity.com/paper/2207.14087/) (2022)

## Built on by (most cited first, 7 of 7)

- [Depression Detection and Analysis using Large Language Models on Textual and Audio-Visual Modalities](https://mlsanity.com/paper/2407.06125/) (2024)
- [Enhancing Depression Detection via Question-wise Modality Fusion](https://mlsanity.com/paper/2503.20496/) (2025)
- [MLlm-DR: Towards Explainable Depression Recognition with MultiModal Large Language Models](https://mlsanity.com/paper/2507.05591/) (2025)
- [CANAMRF: An Attention-Based Model for Multimodal Depression Detection](https://mlsanity.com/paper/2401.02995/) (2024)
- [MDD-Net: Multimodal Depression Detection through Mutual Transformer](https://mlsanity.com/paper/2508.08093/) (2025)
- [Dynamic Summary Generation for Interpretable Multimodal Depression Detection](https://mlsanity.com/paper/2604.11334/) (2026)
- [MA-DLE: Speech-based Automatic Depression Level Estimation via Memory Augmentation](https://mlsanity.com/paper/2606.11197/) (2026)

## Abstract (arXiv)

Depression is a leading cause of death worldwide, and the diagnosis of depression is nontrivial. Multimodal learning is a popular solution for automatic diagnosis of depression, and the existing works suffer two main drawbacks: 1) the high-order interactions between different modalities can not be well exploited; and 2) interpretability of the models are weak. To remedy these drawbacks, we propose a multimodal multi-order factor fusion (MMFF) method. Our method can well exploit the high-order interactions between different modalities by extracting and assembling modality factors under the guide of a shared latent proxy. We conduct extensive experiments on two recent and popular datasets, E-DAIC-WOZ and CMDC, and the results show that our method achieve significantly better performance compared with other existing approaches. Besides, by analyzing the process of factor assembly, our model can intuitively show the contribution of each factor. This helps us understand the fusion mechanism.