Existing long-tailed recognition methods, aiming to train class-balance models from long-tailed data, generally assume the models would be evaluated on the uniform test class distribution. However, the practical test class distribution often violates such an assumption (e.g., being long-tailed or even inversely long-tailed), which would lead existing methods to fail in real-world applications. In this work, we study a more practical task setting, called test-agnostic long-tailed recognition, where the training class distribution is long-tailed while the test class distribution is unknown and can be skewed arbitrarily. In addition to the issue of class imbalance, this task poses another challenge: the class distribution shift between the training and test samples is unidentified. To address this task, we propose a new method, called Test-time Aggregating Diverse Experts (TADE), that presents two solution strategies: (1) a novel skill-diverse expert learning strategy that trains diverse experts to excel at handling different test distributions from a single long-tailed training distribution; (2) a novel test-time expert aggregation strategy that leverages self-supervision to aggregate multiple experts for handling various test distributions. Moreover, we theoretically show that our method has provable ability to simulate unknown test class distributions. Promising results on both vanilla and test-agnostic long-tailed recognition verify the effectiveness of TADE. Code is available at https://github.com/Vanint/TADE-AgnosticLT.

翻译：在这项工作中,我们研究了一个更实际的任务设置,即:测试班分布是长尾的,测试班分布也是不为人知的,可以任意扭曲;但是,实际测试班分布往往违反这种假设(例如,长尾的,甚至反尾的),这种假设会导致现有方法在现实世界应用中失败。在这项工作中,我们研究一个更实际的任务设置,即:测试级分布是长尾的,而测试班分布则是长尾的,而测试班分布则是不为人知的,而且可以任意扭曲。除了班分布问题之外,这一任务还构成另一个挑战:培训和测试样本之间的班分布变化是不明的。为了应对这项任务,我们提出了一种新的方法,称为试验时间集合多样性专家(TADE),它提出了两种解决方案战略:(1) 新的技能多样化专家学习战略,它培训各种专家从单一长尾的训练分布中处理不同的测试分布;(2) 新的测试时专家汇总战略,在自我监督的图象上将自我监督的眼光运用到综合的多类分布测试能力。