Few-Shot Class-Incremental Learning (FSCIL) aims to enable deep neural networks to learn new tasks incrementally from a small number of labeled samples without forgetting previously learned tasks, closely mimicking human learning patterns. In this paper, we propose a novel approach called Prompt Learning for FSCIL (PL-FSCIL), which harnesses the power of prompts in conjunction with a pre-trained Vision Transformer (ViT) model to address the challenges of FSCIL effectively. Our work pioneers the use of visual prompts in FSCIL, which is characterized by its notable simplicity. PL-FSCIL consists of two distinct prompts: the Domain Prompt and the FSCIL Prompt. Both are vectors that augment the model by embedding themselves into the attention layer of the ViT model. Specifically, the Domain Prompt assists the ViT model in adapting to new data domains. The task-specific FSCIL Prompt, coupled with a prototype classifier, amplifies the model's ability to effectively handle FSCIL tasks. We validate the efficacy of PL-FSCIL on widely used benchmark datasets such as CIFAR-100 and CUB-200. The results showcase competitive performance, underscoring its promising potential for real-world applications where high-quality data is often scarce. The source code is available at: https://github.com/TianSongS/PL-FSCIL.

翻译：少样本类增量学习（FSCIL）旨在使深度神经网络能够从少量标注样本中增量式学习新任务，同时不遗忘先前学习的任务，从而紧密模拟人类的学习模式。本文提出了一种名为PL-FSCIL的新方法，该方法利用提示的力量与预训练的Vision Transformer（ViT）模型相结合，以有效应对FSCIL的挑战。我们的工作开创了在FSCIL中使用视觉提示的先河，其显著特点是结构简洁。PL-FSCIL包含两个不同的提示：领域提示和FSCIL提示。两者均为向量，通过嵌入到ViT模型的注意力层来增强模型。具体而言，领域提示协助ViT模型适应新的数据领域。而任务特定的FSCIL提示与原型分类器结合，增强了模型有效处理FSCIL任务的能力。我们在广泛使用的基准数据集（如CIFAR-100和CUB-200）上验证了PL-FSCIL的有效性。结果展示了其具有竞争力的性能，突显了其在高质量数据往往稀缺的实际应用中的巨大潜力。源代码可在以下网址获取：https://github.com/TianSongS/PL-FSCIL。