IDRL: An Individual-Aware Multimodal Depression-Related Representation Learning Framework for Depression Diagnosis

TL;DR

This paper introduces IDRL, a novel framework that disentangles and adaptively fuses multimodal depression cues to improve diagnosis accuracy and robustness across individuals.

Contribution

The paper proposes a new individual-aware multimodal learning framework that disentangles depression-related signals and adaptively fuses them based on individual differences.

Findings

IDRL outperforms existing methods in depression detection accuracy.

IDRL effectively suppresses irrelevant information and handles individual differences.

Experimental results demonstrate superior robustness and reliability.

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.