Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners
Authors: Keon-Hee Park, Kyungwoo Song, Gyeong-Moon Park

Few-Shot Class Incremental Learning (FSCIL) is a task that requires a model to learn new classes incrementally without forgetting when only a few samples for each class are given. FSCIL encounters two significant challenges: catastrophic forgetting and overfitting and these challenges have driven prior studies to primarily rely on shallow models such as ResNet-18. Even though their limited capacity can mitigate both forgetting and overfitting issues it leads to inadequate knowledge transfer during few-shot incremental sessions. In this paper we argue that large models such as vision and language transformers pre-trained on large datasets can be excellent few-shot incremental learners. To this end we propose a novel FSCIL framework called PriViLege Pre-trained Vision and Language transformers with prompting functions and knowledge distillation. Our framework effectively addresses the challenges of catastrophic forgetting and overfitting in large models through new pre-trained knowledge tuning (PKT) and two losses: entropy-based divergence loss and semantic knowledge distillation loss. Experimental results show that the proposed PriViLege significantly outperforms the existing state-of-the-art methods with a large margin e.g. +9.38% in CUB200 +20.58% in CIFAR-100 and +13.36% in miniImageNet. Our implementation code is available at https://github.com/KHU-AGI/PriViLege.