发表机构
School of Information Sciences; University of Illinois Urbana-Champaign（信息科学学院; 伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对长尾分类中模型在不同类别间可靠性不均的问题，提出模块化集成框架CLEAR，通过结构化采样生成专家并估计类级信任分数，实验显示其在多数据集上表现优异，尤其小样本性能突出。
AI 中文摘要
长尾分类带来了可靠性挑战，因为在不平衡数据上训练的模型在频繁类和代表性不足类之间的可靠性不均。现有方法通过重平衡、调整、表征学习或多专家建模来解决不平衡问题，但它们很少估计每个类应信任哪个专家。本文提出CLEAR（面向长尾识别的类级可靠性感知专家聚合，Class-wise reLiability-aware Expert Aggregation for long-tailed Recognition），这是一个用于长尾分类的模块化集成框架。CLEAR通过基于阈值的结构化采样生成多样化专家，同时保留完整标签空间，随后使用平滑的类级精度公式为每个专家估计类级信任分数。推理时，通过类级广义专家乘积聚合方法组合专家预测，允许针对不同类强调不同专家。在CIFAR-100-LT、ImageNet-LT和Places-LT数据集上使用多个骨干网络的实验表明，CLEAR实现了有竞争力的整体准确率，且小样本表现尤为强劲。这些结果支持类级专家可靠性作为长尾集成学习的有用设计原则。
英文摘要
Long-tailed classification poses a reliability challenge because models trained on imbalanced data are unevenly reliable across frequent and underrepresented classes. While existing methods address imbalance through re-balancing, adjustment, representation learning, or multi-expert modeling, they rarely estimate which expert should be trusted for each class. This paper proposes CLEAR (Class-wise reLiability-aware Expert Aggregation for long-tailed Recognition), a modular ensemble framework for long-tailed classification. CLEAR generates diverse experts through threshold-based structured sampling while preserving the full label space, then estimates a class-wise trust score for each expert using a smoothed class-wise precision formulation. During inference, expert predictions are combined through class-wise generalized product-of-experts aggregation, allowing different experts to be emphasized for different classes. Experiments on CIFAR-100-LT, ImageNet-LT, and Places-LT across multiple backbones show that CLEAR achieves competitive overall accuracy and particularly strong few-shot performance. These results support class-wise expert reliability as a useful design principle for long-tailed ensemble learning.
