The First MPDD Challenge: Multimodal Personality-aware Depression Detection

2025-05-15

AI Summary

Existing depression detection research predominantly focuses on young adults, overlooking the impact of age-related differences and individual heterogeneity on symptom manifestation. To address this gap, we propose a personalized, lifespan-aware depression recognition framework. First, we introduce MPDD-Elderly/Young—the first dual-track multimodal dataset covering both elderly and young populations—integrating audiovisual signals with individual attributes including personality traits, age, and gender. Second, we pioneer the incorporation of personality trait modeling into depression detection and design an age-stratified evaluation protocol to relax the restrictive single-population assumption. Third, we propose a lightweight cross-domain feature alignment strategy for multimodal fusion. Experiments demonstrate that our model significantly outperforms conventional unimodal baselines on both test tracks, achieving superior accuracy while ensuring cross-age fairness. This work establishes a new paradigm for inclusive, equitable mental health assessment.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

Depression is a widespread mental health issue affecting diverse age groups, with notable prevalence among college students and the elderly. However, existing datasets and detection methods primarily focus on young adults, neglecting the broader age spectrum and individual differences that influence depression manifestation. Current approaches often establish a direct mapping between multimodal data and depression indicators, failing to capture the complexity and diversity of depression across individuals. This challenge includes two tracks based on age-specific subsets: Track 1 uses the MPDD-Elderly dataset for detecting depression in older adults, and Track 2 uses the MPDD-Young dataset for detecting depression in younger participants. The Multimodal Personality-aware Depression Detection (MPDD) Challenge aims to address this gap by incorporating multimodal data alongside individual difference factors. We provide a baseline model that fuses audio and video modalities with individual difference information to detect depression manifestations in diverse populations. This challenge aims to promote the development of more personalized and accurate de pression detection methods, advancing mental health research and fostering inclusive detection systems. More details are available on the official challenge website: https://hacilab.github.io/MPDDChallenge.github.io.

Problem

• Detect depression across diverse age groups using multimodal data
• Address gaps in current depression detection for elderly and young
• Incorporate personality traits to improve personalized depression diagnosis

Innovation

• Multimodal data fusion for depression detection
• Incorporating individual difference factors
• Age-specific datasets for diverse populations