PL-FSCIL: Harnessing the Power of Prompts for Few-Shot Class-Incremental
  Learning
Songsong Tian Lusi Li Weijun Li Hang Ran Li Li Xin Ning
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.