{"ID":6023404,"CreatedAt":"2026-07-08T01:00:23.257252134Z","UpdatedAt":"2026-07-10T06:38:11.380144103Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05901","arxiv_id":"2607.05901","title":"Uncovering Latent Depression Severity for Binary Depression Detection via Advantage-weighting Ranking","abstract":"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.","short_abstract":"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 facil...","url_abs":"https://arxiv.org/abs/2607.05901","url_pdf":"https://arxiv.org/pdf/2607.05901v1","authors":"[\"Manning Gao\",\"Tingyi Liu\",\"Leheng Zhang\",\"Haifeng Hu\",\"Yuncheng Jiang\",\"Sijie Mai\"]","published":"2026-07-07T06:56:55Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Transformer\"]","has_code":false}