Prompt-based learning for few-shot class-incremental learning

Abstract

Few-Shot Class-Incremental Learning (FSCIL) aims to enable deep neural networks to incrementally learn new tasks from a limited number of labeled samples, while retaining knowledge of previously learned tasks, mimicking the way humans learn. In this paper, we introduce a novel approach called Prompt Learning for FSCIL (PL-FSCIL), which leverages the power of prompts alongside a pre-trained Vision Transformer (ViT) model to effectively tackle the challenges of FSCIL. Our approach explores the feasibility of directly applying visual prompts in FSCIL, using a simplified model architecture. PL-FSCIL integrates two key prompts: the Domain Prompt and the FSCIL Prompt. Both are tensors incorporated into the attention layer of the ViT network to enhance its capabilities. The Domain Prompt helps the model adapt to new data domains, while the FSCIL Prompt, in combination with a prototype classifier, boosts the model’s ability to handle incremental tasks. We evaluate the performance of PL-FSCIL on well-established benchmark datasets, including CIFAR-100 and CUB-200. The results demonstrate competitive performance, highlighting the method’s promising potential for real-world applications, particularly in scenarios where high-quality labeled data is scarce. The source code is at: https://github.com/JichengYuan81/PL-FSCIL.