  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.
