PerDepNet: Personality-Guided Cross-Domain Multitask Learning Network for Automated Video Depression Detection

Depression ranks as one of the most prevalent psychological disorders and has received growing attention. Currently, there are two primary challenges in automatic video depression detection: the scarcity of labeled depressed data and effective feature representations characterizing human depression. To address these challenges, this work fully considers the correlation between human personality traits and depression, and proposes a Personality-guided Cross-domain Multitask Learning Network (PerDepNet) for automated video Depression detection. PerDepNet consists of a shared encoder for learning spatiotemporal joint representations, and a personality-guided decoder for cross-domain multitask learning tasks. The encoder contains a cross-scale shared feature extractor for capturing interactive spatial joint features at different scales, and a mamba-based temporal feature extractor for modeling long-term dynamics of depressed videos in personality and depression domains. The personality-guided decoder integrates the task of predicting personality traits into the depression detection task, enabling the personality traits to guide the estimate of depression levels in an adaptive weighted balance module. This is the first attempt to consider the role of personality traits in the depression detection task, thereby improving the performance of depression detection. Extensive experiments on three public depression video datasets demonstrate the superiority of the proposed method over state-of-the-art methods.