Concept-Constrained Prompt Learning for Few-Shot CLIP Adaptation

CCPL introduces a lightweight framework that anchors class prompts to frozen concept prototypes, improving few-shot CLIP adaptation by reducing overfitting. It achieves better base-to-new performance on DTD and EuroSAT compared to CoOp, with consistent gains from text-space concept regularization, while maintaining neutrality on OxfordPets.