Trusted Multi-view Learning for Long-tailed Classification

• URL: http://arxiv.org/abs/2511.09138v1
• Date: Thu, 13 Nov 2025 01:35:20 GMT
• Title: Trusted Multi-view Learning for Long-tailed Classification
• Authors: Chuanqing Tang, Yifei Shi, Guanghao Lin, Lei Xing, Long Shi,
• Abstract summary: TMLC is a Trusted Multi-view Long-tailed Classification framework.<n>Inspired by Social Identity Theory, we design a group consensus opinion aggregation mechanism.<n>We develop an uncertainty-guided data generation module that produces high-quality pseudo-data.
• Score: 9.27326757577602
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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