解决多视角长尾分类中的信任问题，提升不平衡数据下的模型性能。
Trusted Multi-view Learning for Long-tailed Classification
- 基于群体共识机制融合多视角观点，增强决策可信度。
- 提出新距离度量与不确定性引导生成模块，有效生成高质量伪数据。
- 适用于多源异构数据下的长尾分类任务，适合注重模型可靠性研究者。
类别不平衡在单视角场景中已有广泛研究，但在多视角场景中仍是一个开放问题，且针对可信解决方案的研究更为稀缺。本文针对多视角场景中的长尾分类挑战，提出TMLC（可信多视角长尾分类）框架，在意见聚合和伪数据生成两方面做出贡献。具体而言，受社会认同理论启发，设计了群体共识意见聚合机制，引导决策向多数群体偏好方向发展。在伪数据生成方面，引入一种新型距离度量以适配SMOTE至多视角场景，并开发了不确定性引导的数据生成模块，生成高质量伪数据，有效缓解类别不平衡带来的负面影响。在多个长尾多视角数据集上的大量实验表明，所提模型可实现优异性能。代码已公开于https://github.com/cncq-tang/TMLC。
原文摘要 · Abstract (English)
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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