Uncovering Latent Depression Severity for Binary Depression Detection via Advantage-weighting Ranking
For researchers in affective computing and mental health monitoring, this work improves binary depression detection by better separating overlapping feature distributions.
The paper proposes a fine-grained multimodal framework with a Binary Advantage-weighting Ranking Loss for automatic depression detection from audio-visual data, achieving state-of-the-art performance on D-vlog and LMVD datasets.
Automatic depression detection using audio-visual data faces significant challenges, particularly in disentangling overlapping feature distributions and establishing robust decision boundaries. To address this, we propose a fine-grained multimodal framework featuring a temporal encoder and a mutual transformer to facilitate deep cross-modal fusion. Our core contribution is the Binary Advantage-weighting Ranking Loss, which optimizes the latent space distribution through two complementary mechanisms: Advantage-weighted Separation, which mines hard pairs by computing a pairwise prediction difference matrix and dynamically weighting them based on their difficulty; and Advantage-weighted Compactness, which minimizes intra-class variance to force features to cluster around their respective class centers. Extensive experiments on D-vlog and LMVD demonstrate that our model reconstructs the latent ordinal structure by prioritizing hard pairs, thereby achieving state-of-the-art performance.