[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84026-en":3,"doc-seo-84026-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},84026,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Uncovering Latent Depression Severity for Binary Depression Detection via Advantage-weighting Ranking","Automatic depression detection using audio-visual data faces significant challenges in disentangling overlapping feature distributions and learning robust decision boundaries. A fine-grained multimodal framework is proposed with a temporal encoder and a mutual transformer for deep cross-modal fusion. The Binary Advantage-weighting Ranking Loss optimizes latent space using Advantage-weighted Separation for hard pair mining through dynamic pairwise weighting, and Advantage-weighted Compactness for intra-class variance minimization. Experiments on D-vlog and LMVD show state-of-the-art performance by reconstructing latent ordinal structure.","Uncovering Latent Depression Severity for Binary Depression Detection via  \nAdvantage-weighting Ranking  \nManning Gao  1, Tingyi Liu 1, Leheng Zhang  1, Haifeng Hu ID 2, Yuncheng Jiang 1, Sijie Mai  1 ,∗∗  \n1 South China Normal University, China 2 Sun Yat-sen University, China [20232005149@m.scnu.edu.cn](20232005149@m.scnu.edu.cn) , [liutingyi@m.scnu.edu.cn](liutingyi@m.scnu.edu.cn) , [lehengzhang@m.scnu.edu.cn](lehengzhang@m.scnu.edu.cn) ,  \n[huhaif@mail.sysu.edu.cn](huhaif@mail.sysu.edu.cn) , [ycjiang@scnu.edu.cn](ycjiang@scnu.edu.cn) , [sijiemai@m.scnu.edu.cn](sijiemai@m.scnu.edu.cn)  \narXiv :2607 .0590 1v 1 [ cs .AI ] 7 Jul 2026  \nAbstract  \nAutomatic 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 minimizesintra-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 stateof-the-art performance.  \nIndex Terms: affective computing, automatic depression detection, multimodal analysis, ordinal learning  \n1. Introduction and Related Work  \nDepression is a global mental health challenge. Automatic Depression Detection (ADD) using multimodal data (e.g., audio, visual) [1] has emerged as a promising non-invasive screening tool. While deep learning has improved feature extraction, detecting depression in user-generated content (e.g., vlogs) remains challenging due to the subtle and ambiguous boundaries between depressed and non-depressed behaviors. To capture these intricate temporal patterns, Transformer-based models [2, 3] have been widely adopted for their capability to model global dependencies through self-attention mechanisms. Furthermore, to fully leverage the complementary information across different streams, recent studies have emphasized deep cross-modal interactions, such as employing crossattention scaling layers coupled with advanced tensor-based pooling methods to fuse multimodal representations [4] . More recently, Mamba-based architectures [5–7] have emerged as efficient alternatives, leveraging selective state space models to handle long sequences with linear computational complexity.  \nBeyond architectural innovation, a critical bottleneck lies in optimization objectives. Most current approaches [5, 8–10] rely on pointwise supervision (e.g., Binary Cross-Entropy, BCE), which treats depressed and non-depressed samples as independent nominal classes. This formulation neglects the latent ordinal nature of depression severity, a continuous spectrum in which severe cases should be ranked higher than mild or nor-  \n**indicates the corresponding author.  \nmal ones [11–14] . Although vlog datasets provide only discrete binary annotations, the underlying physiological and behavioral symptoms exist on a continuous scale. To bridge this gap, we adopt a pairwise learning paradigm [15] . By encouraging the model to learn a fine-grained ranking of depression risk, this approach effectively reconstructs the intrinsic ordinal relationships from coarse binary labels. Integrating ordinal learning into ADD is complicated by standard datasets that offer only binary labels rather than fine-grained severity scores. The resulting sparse supervision [11] hinders precise ranking inference. Furthermore, standard pai","cbCaimzAErzSzXte","https://ap.wps.com/l/cbCaimzAErzSzXte","pdf",3119336,2,1,5,"English","en",105,"# Abstract\n# Introduction and Related Work\n# Methods","[{\"question\":\"Why does the method treat depression detection as a ranking task instead of pointwise classification?\",\"answer\":\"Pairwise ranking helps reconstruct latent ordinal relationships from coarse binary labels, encouraging a fine-grained ordering of depression risk and improving inference under sparse supervision.\"}]",1784192102,13,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"uncovering-latent-depression-severity-for-binary-depression-detection-via-advantage-weighting-ranking","",{"@graph":36,"@context":77},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/uncovering-latent-depression-severity-for-binary-depression-detection-via-advantage-weighting-ranking/84026/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"Why does the method treat depression detection as a ranking task instead of pointwise classification?","Question",{"text":75,"@type":76},"Pairwise ranking helps reconstruct latent ordinal relationships from coarse binary labels, encouraging a fine-grained ordering of depression risk and improving inference under sparse supervision.","Answer","https://schema.org",{"og:url":51,"og:type":79,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":81,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":84},[85,89,93,97,101,106,111,114,119,122,126],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":22,"slug":129},19,"General","general"]