The field of incremental learning for image classification is witnessing significant advancements, particularly in addressing the challenge of catastrophic forgetting. Researchers are increasingly focusing on developing methods that balance plasticity and stability, enabling models to learn new tasks while retaining knowledge of previous ones. A notable trend is the integration of task-specific batch normalization (BN) and out-of-distribution detection mechanisms, which enhance the model's ability to adapt to new tasks without overfitting. Additionally, there is a growing emphasis on few-shot learning scenarios, where models must learn from limited data, prompting innovations in prototype-based approaches and feature synthesis techniques. These advancements are not only improving the performance of incremental learning models but also broadening their applicability to more complex and realistic scenarios. Notably, the introduction of class-independent transformations in semantic segmentation is redefining how models handle incremental learning tasks, ensuring minimal forgetting across diverse datasets.

Noteworthy Papers:

• The integration of task-specific BN into class incremental learning (CIL) and the extension of task incremental learning (TIL) methods to CIL through task-ID prediction.
• The proposal of a covariance constraint loss and perturbation approach in few-shot class incremental learning (FSCIL) to enhance class separation and mitigate overfitting.
• The development of a non-exemplar class-incremental learning method using retrospective feature synthesis, significantly improving efficiency and performance.
• The introduction of a Class Independent Transformation (CIT) in class-incremental semantic segmentation, ensuring minimal task forgetting across various datasets.