Superclass-guided Hierarchical Prompting for Few-Shot Class-Incremental Learning

Few-Shot Class-Incremental Learning (FSCIL) aims at continuously learning new-class knowledge from extremely limited samples while preserving the recognition capability on old classes, which suffers from the dual challenges of catastrophic forgetting and overfitting. While existing prompt-based FSCIL methods achieve promising results, they overlook the semantic relationships among classes and fail to explicitly exploit the shared visual-semantic patterns across related classes. In this paper, we propose a Superclass-guided Hierarchical Prompt learning framework (SH-Prompt) to address this issue. Specifically, our method organizes base classes into semantic superclasses via a large language model (LLM) and progressively assigns newly introduced classes to the established superclasses. Based on the resulting semantic structure, we design a key-based superclass prompt pool to adaptively incorporate superclass-shared prompts for each sample through a semantic-driven retrieval mechanism. Furthermore, we derive a superclass-guided instance-level prompt learning loss, which introduces structured regularization across superclass semantics to improve the inter-class separability. Extensive experimental results on three FSCIL benchmark datasets demonstrate that our proposed method outperforms the state-of-the-art. Code will be released publicly.