  Pre-trained Vision-Language Models (VLMs), like CLIP, exhibit strong
generalization ability to downstream tasks but struggle in few-shot scenarios.
Existing prompting techniques primarily focus on global text and image
representations, yet overlooking multi-modal attribute characteristics. This
limitation hinders the model's ability to perceive fine-grained visual details
and restricts its generalization ability to a broader range of unseen classes.
To address this issue, we propose a Multi-modal Attribute Prompting method
(MAP) by jointly exploring textual attribute prompting, visual attribute
prompting, and attribute-level alignment. The proposed MAP enjoys several
merits. First, we introduce learnable visual attribute prompts enhanced by
textual attribute semantics to adaptively capture visual attributes for images
from unknown categories, boosting fine-grained visual perception capabilities
for CLIP. Second, the proposed attribute-level alignment complements the global
alignment to enhance the robustness of cross-modal alignment for
open-vocabulary objects. To our knowledge, this is the first work to establish
cross-modal attribute-level alignment for CLIP-based few-shot adaptation.
Extensive experimental results on 11 datasets demonstrate that our method
performs favorably against state-of-the-art approaches.
