  Contrastive Language-Image Pretraining (CLIP) model has exhibited remarkable
efficacy in establishing cross-modal connections between texts and images,
yielding impressive performance across a broad spectrum of downstream
applications through fine-tuning. However, for generalization tasks, the
current fine-tuning methods for CLIP, such as CoOp and CoCoOp, demonstrate
relatively low performance on some fine-grained datasets. We recognize the
underlying reason is that these previous methods only projected global features
into the prompt, neglecting the various visual concepts, such as colors,
shapes, and sizes, which are naturally transferable across domains and play a
crucial role in generalization tasks. To address this issue, in this work, we
propose Concept-Guided Prompt Learning (CPL) for vision-language models.
Specifically, we leverage the well-learned knowledge of CLIP to create a visual
concept cache to enable concept-guided prompting. In order to refine the text
features, we further develop a projector that transforms multi-level visual
features into text features. We observe that this concept-guided prompt
learning approach is able to achieve enhanced consistency between visual and
linguistic modalities. Extensive experimental results demonstrate that our CPL
method significantly improves generalization capabilities compared to the
current state-of-the-art methods.
