{"ID":2842719,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.09138","arxiv_id":"2511.09138","title":"Trusted Multi-view Learning for Long-tailed Classification","abstract":"Class imbalance has been extensively studied in single-view scenarios; however, addressing this challenge in multi-view contexts remains an open problem, with even scarcer research focusing on trustworthy solutions. In this paper, we tackle a particularly challenging class imbalance problem in multi-view scenarios: long-tailed classification. We propose TMLC, a Trusted Multi-view Long-tailed Classification framework, which makes contributions on two critical aspects: opinion aggregation and pseudo-data generation. Specifically, inspired by Social Identity Theory, we design a group consensus opinion aggregation mechanism that guides decision making toward the direction favored by the majority of the group. In terms of pseudo-data generation, we introduce a novel distance metric to adapt SMOTE for multi-view scenarios and develop an uncertainty-guided data generation module that produces high-quality pseudo-data, effectively mitigating the adverse effects of class imbalance. Extensive experiments on long-tailed multi-view datasets demonstrate that our model is capable of achieving superior performance. The code is released at https://github.com/cncq-tang/TMLC.","short_abstract":"Class imbalance has been extensively studied in single-view scenarios; however, addressing this challenge in multi-view contexts remains an open problem, with even scarcer research focusing on trustworthy solutions. In this paper, we tackle a particularly challenging class imbalance problem in multi-view scenarios: lon...","url_abs":"https://arxiv.org/abs/2511.09138","url_pdf":"https://arxiv.org/pdf/2511.09138v1","authors":"[\"Chuanqing Tang\",\"Yifei Shi\",\"Guanghao Lin\",\"Lei Xing\",\"Long Shi\"]","published":"2025-11-12T09:26:23Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":607152,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2842719,"paper_url":"https://arxiv.org/abs/2511.09138","paper_title":"Trusted Multi-view Learning for Long-tailed Classification","repo_url":"https://github.com/cncq-tang/TMLC","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}