Long-tailed(LT) classification is an unavoidable and challenging problem in the real world. Most of the existing long-tailed classification methods focus only on solving the inter-class imbalance in which there are more samples in the head class than in the tail class, while ignoring the intra-lass imbalance in which the number of samples of the head attribute within the same class is much larger than the number of samples of the tail attribute. The deviation in the model is caused by both of these factors, and due to the fact that attributes are implicit in most datasets and the combination of attributes is very complex, the intra-class imbalance is more difficult to handle. For this purpose, we proposed a long-tailed classification framework, known as \textbf{\textsc{Cognisance}}, which is founded on Coarse-Grained Leading Forest (CLF) and Multi-Center Loss (MCL), aiming to build a multi-granularity joint solution model by means of invariant feature learning. In this method, we designed an unsupervised learning method, i.e., CLF, to better characterize the distribution of attributes within a class. Depending on the distribution of attributes, we can flexibly construct sampling strategies suitable for different environments. In addition, we introduce a new metric learning loss (MCL), which aims to gradually eliminate confusing attributes during the feature learning process. More importantly, this approach does not depend on a specific model structure and can be integrated with existing LT methods as an independent component. We have conducted extensive experiments and our approach has state-of-the-art performance in both existing benchmarks ImageNet-GLT and MSCOCO-GLT, and can improve the performance of existing LT methods. Our codes are available on GitHub: \url{https://github.com/jinyery/cognisance}

翻译：长尾分类是现实世界中一个不可避免且具有挑战性的问题。现有的大多数长尾分类方法仅关注解决头部类别样本数多于尾部类别的类间不平衡问题，而忽略了同一类别内头部属性样本数远大于尾部属性样本数的类内不平衡问题。模型偏差由这两个因素共同导致，且由于属性在多数数据集中是隐式的，且属性组合非常复杂，类内不平衡问题更加难以处理。为此，我们提出了一种长尾分类框架，名为\textbf{\textsc{Cognisance}}，该框架基于粗粒度引导森林（CLF）与多中心损失（MCL），旨在通过不变特征学习构建多粒度联合求解模型。在该方法中，我们设计了一种无监督学习方法，即CLF，以更好地刻画类内属性分布。基于属性分布，我们可以灵活地构建适用于不同环境的采样策略。此外，我们引入了一种新的度量学习损失（MCL），旨在特征学习过程中逐步消除混淆属性。更重要的是，该方法不依赖于特定模型结构，可作为独立组件与现有长尾方法集成。我们进行了大量实验，我们的方法在现有基准数据集ImageNet-GLT和MSCOCO-GLT上均达到最先进性能，并能提升现有长尾方法的性能。我们的代码已在GitHub上公开：\url{https://github.com/jinyery/cognisance}