# Automatic Depression Detection via Learning and Fusing Features from Visual Cues

arXiv: https://arxiv.org/abs/2203.00304 · HTML: https://mlsanity.com/paper/2203.00304/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 52 references that are in the map.
- First posted on arXiv 1 Mar 2022.

## Builds on (most cited first, 17 of 17)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [What Makes Training Multi-Modal Classification Networks Hard?](https://mlsanity.com/paper/1905.12681/) (2019)
- [Temporal Convolutional Networks: A Unified Approach to Action Segmentation](https://mlsanity.com/paper/1608.08242/) (2016)
- [AVEC 2016 - Depression, Mood, and Emotion Recognition Workshop and Challenge](https://mlsanity.com/paper/1605.01600/) (2016)
- [Topic Modeling Based Multi-modal Depression Detection](https://mlsanity.com/paper/1803.10384/) (2018)
- [Deep Learning for Depression Recognition with Audiovisual Cues: A Review](https://mlsanity.com/paper/2106.00610/) (2021)
- [Measuring Depression Symptom Severity from Spoken Language and 3D Facial Expressions](https://mlsanity.com/paper/1811.08592/) (2018)
- [Multi-level Attention network using text, audio and video for Depression Prediction](https://mlsanity.com/paper/1909.01417/) (2019)
- [DEPA: Self-Supervised Audio Embedding for Depression Detection](https://mlsanity.com/paper/1910.13028/) (2019)
- [AudVowelConsNet: A Phoneme-Level Based Deep CNN Architecture for Clinical Depression Diagnosis](https://mlsanity.com/paper/2010.16201/) (2020)
- [Depression Severity Estimation from Multiple Modalities](https://mlsanity.com/paper/1711.06095/) (2017)
- [Affective Conditioning on Hierarchical Networks applied to Depression Detection from Transcribed Clinical Interviews](https://mlsanity.com/paper/2006.08336/) (2020)

## Built on by (most cited first, 5 of 5)

- [Multi-modal Depression Estimation based on Sub-attentional Fusion](https://mlsanity.com/paper/2207.06180/) (2022)
- [HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detection](https://mlsanity.com/paper/2408.03648/) (2024)
- [MOGAM: A Multimodal Object-oriented Graph Attention Model for Depression Detection](https://mlsanity.com/paper/2403.15485/) (2024)
- [Automatic Depression Assessment using Machine Learning: A Comprehensive Survey](https://mlsanity.com/paper/2506.18915/) (2025)
- [Hierarchical Self-Supervised Representation Learning for Depression Detection from Speech](https://mlsanity.com/paper/2510.08593/) (2025)

## Abstract (arXiv)

Depression is one of the most prevalent mental disorders, which seriously affects one's life. Traditional depression diagnostics commonly depends on rating with scales, which can be labor-intensive and subjective. In this context, Automatic Depression Detection (ADD) has been attracting more attention for its low cost and objectivity. ADD systems are able to detect depression automatically from some medical records, like video sequences. However, it remains challenging to effectively extract depression-specific information from long sequences, thereby hindering a satisfying accuracy. In this paper, we propose a novel ADD method via learning and fusing features from visual cues. Specifically, we firstly construct Temporal Dilated Convolutional Network (TDCN), in which multiple Dilated Convolution Blocks (DCB) are designed and stacked, to learn the long-range temporal information from sequences. Then, the Feature-Wise Attention (FWA) module is adopted to fuse different features extracted from TDCNs. The module learns to assign weights for the feature channels, aiming to better incorporate different kinds of visual features and further enhance the detection accuracy. Our method achieves the state-of-the-art performance on the DAIC_WOZ dataset compared to other visual-feature-based methods, showing its effectiveness.