  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.
