This paper introduces a two-stage framework designed to enhance long-tail class incremental learning, enabling the model to progressively learn new classes, while mitigating catastrophic forgetting in the context of long-tailed data distributions. Addressing the challenge posed by the under-representation of tail classes in long-tail class incremental learning, our approach achieves classifier alignment by leveraging global variance as an informative measure and class prototypes in the second stage. This process effectively captures class properties and eliminates the need for data balancing or additional layer tuning. Alongside traditional class incremental learning losses in the first stage, the proposed approach incorporates mixup classes to learn robust feature representations, ensuring smoother boundaries. The proposed framework can seamlessly integrate as a module with any class incremental learning method to effectively handle long-tail class incremental learning scenarios. Extensive experimentation on the CIFAR-100 and ImageNet-Subset datasets validates the approach's efficacy, showcasing its superiority over state-of-the-art techniques across various long-tail CIL settings.

翻译：本文提出了一种两阶段框架，旨在增强长尾类别增量学习性能，使模型能够逐步学习新类别，同时缓解长尾数据分布下的灾难性遗忘问题。针对长尾类别增量学习中尾部类别表征不足的挑战，本方法在第二阶段通过利用全局方差作为信息度量及类别原型实现分类器对齐。该过程有效捕获类别属性，且无需数据平衡或额外层调优。在第一阶段采用传统类别增量学习损失的基础上，本方法引入混合类别以学习鲁棒特征表示，确保更平滑的决策边界。该框架可作为模块无缝集成至任意类别增量学习方法中，从而有效应对长尾类别增量学习场景。在CIFAR-100与ImageNet-Subset数据集上的大量实验验证了方法的有效性，在多种长尾类别增量学习设定下均展现出超越现有先进技术的优越性能。