Strategic Base Representation Learning via Feature Augmentations for Few-Shot Class Incremental Learning

Parinita Nema, Vinod K Kurmi; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 6394-6403

Few-shot class incremental learning implies the model to learn new classes while retaining knowledge of previously learned classes with a small number of training instances. Existing frameworks typically freeze the parameters of the previously learned classes during the incorporation of new classes. However this approach often results in suboptimal class separation of previously learned classes leading to overlap between old and new classes. Consequently the performance of old classes degrades on new classes. To address these challenges we propose a novel feature augmentation driven contrastive learning framework designed to enhance the separation of previously learned classes to accommodate new classes. Our approach involves augmenting feature vectors and assigning proxy labels to these vectors. This strategy expands the feature space ensuring seamless integration of new classes within the expanded space. Additionally we employ a self-supervised contrastive loss to improve the separation between previous classes. We validate our framework through experiments on three FSCIL benchmark datasets: CIFAR100 miniImageNet and CUB200. The results demonstrate that our Feature Augmentation driven Contrastive Learning framework significantly outperforms other approaches achieving state-of-the-art performance.