Few-shot class-incremental learning (FSCIL) aims to continually learn new knowledge about few-shot novel classes while retaining the previously learned knowledge of old classes. With the rapid development of vision-language models ( e.g. , CLIP) known for their strong transferability, several studies have explored applying them to FSCIL. However, they generally retain the original architecture, where the visual and textual encoders operate independently. This limits the impact of textual semantics on the discriminative capability of the visual branch and somewhat restricts the model's performance in FSCIL. In this paper, we argue that integrating class-level semantics from the CLIP textual branch into the visual branch can provide a more robust representational foundation for FSCIL. To this end, we propose LEAD, a CLIP-based FSCIL method that explicitly leverages textual semantic guidance to enhance the discriminability and generalizability of the visual branch, improving adaptability to few-shot novel classes while mitigating interference with old ones. Specifically, the Conditional Semantic Constructor (CSC) adaptively converts textual semantic cues into class-relevant guidance, and the Semantic-Enhanced Attention (SEA) module explicitly incorporates semantic guidance into the image encoder to strengthen semantic awareness. A text-guided prototype generation strategy incrementally expands the text-guided prototype space, enhancing the distinction between different classes while promoting model stability in incremental sessions. Extensive experiments on three benchmark datasets validate the effectiveness of LEAD and its competitive performance against existing FSCIL methods.
Xu et al. (Wed,) studied this question.