[Paper Review] IDRL: An Individual-Aware Multimodal Depression-Related Representation Learning Framework for Depression Diagnosis

Chongxiao Wang, Junjie Liang+3 more|arXiv (Cornell University)|Mar 12, 2026

Emotion and Mood Recognition0 citations

TL;DR

IDRL disentangles multimodal depression cues into common, specific, and unrelated spaces and uses an individual-aware fusion to adaptively weigh features for robust depression diagnosis across modalities.

ABSTRACT

Depression is a severe mental disorder, and reliable identification plays a critical role in early intervention and treatment. Multimodal depression detection aims to improve diagnostic performance by jointly modeling complementary information from multiple modalities. Recently, numerous multimodal learning approaches have been proposed for depression analysis; however, these methods suffer from the following limitations: 1) inter-modal inconsistency and depression-unrelated interference, where depression-related cues may conflict across modalities while substantial irrelevant content obscures critical depressive signals, and 2) diverse individual depressive presentations, leading to individual differences in modality and cue importance that hinder reliable fusion. To address these issues, we propose Individual-aware Multimodal Depression-related Representation Learning Framework (IDRL) for robust depression diagnosis. Specifically, IDRL 1) disentangles multimodal representations into a modality-common depression space, a modality-specific depression space, and a depression-unrelated space to enhance modality alignment while suppressing irrelevant information, and 2) introduces an individual-aware modality-fusion module (IAF) that dynamically adjusts the weights of disentangled depression-related features based on their predictive significance, thereby achieving adaptive cross-modal fusion for different individuals. Extensive experiments demonstrate that IDRL achieves superior and robust performance for multimodal depression detection.

Motivation & Objective

• Motivate robust multimodal depression detection despite inter-modal inconsistency and individual presentation differences.
• Propose a framework that disentangles modality-common, modality-specific, and depression-unrelated information.
• Introduce an individual-aware fusion mechanism to adaptively weight features per individual.
• Validate effectiveness on benchmark datasets AVEC-2014 and Twitter with ablations and visualizations.

Proposed method

• Disentangle multimodal representations into modality-common (F_c^m), modality-specific (F_s^m), and depression-unrelated (N_c^m, N_s^m) spaces using modality-wise encoders.
• Reconstruct original features via self- and cross-modal reconstruction to enforce information preservation and cross-modal interaction.
• Apply Central Moment Discrepancy (CMD) to align modality-common features across modalities.
• Use a soft orthogonal regularizer to encourage separation among the disentangled spaces.
• Predict depression with a fused representation F_S from an individual-aware attention-based fusion over concatenated features.
• Introduce an auxiliary contribution and alignment loss to promote informative fusion and consistency with predictive importance.
• Optimize a total loss combining diagnosis, disentanglement, and individual-aware components with specified weights.

Experimental results

Research questions

• RQ1 Can disentangling modality-common, modality-specific, and depression-unrelated information improve cross-modal depression detection?
• RQ2 Does an individual-aware fusion module improve adaptive multimodal fusion across individuals with different depressive presentations?
• RQ3 How do the proposed loss components influence model performance and feature disentanglement?
• RQ4 Do the methods generalize across different modality pairs (video/audio and text/image) and datasets?

Key findings

• IDRL achieves state-of-the-art results on AVEC-2014 for video+audio and on Twitter for text+image.
• Disentangling modalities into common, specific, and unrelated spaces reduces interference and improves alignment.
• Individual-aware fusion provides adaptive weighting leading to better performance than non-adaptive fusion.
• Ablations show orthogonality and CMD losses are critical for performance and disentanglement quality.
• Visualizations (t-SNE, Grad-CAM++) illustrate clearer separation of feature spaces and more focused predictive cues when using the full model.