Distribution-Aware CLIP-Adapter with Fine-Grained Text for Few-Shot Fine-Grained Classification

Fine-Grained Few-Shot Classification (FG-FSC) in remote sensing has become a critical task, as the scarcity of high-quality annotated data severely restricts the performance of deep learning models in fine-grained classification. Although Contrastive Language-Image Pre-Training (CLIP) exhibits strong generalization ability in few-shot learning, it fails to generate discriminative text and image features when adapted to remote sensing tasks. In this paper, a framework is proposed to adapt CLIP to remote sensing FG-FSC from both visual and text aspects. First, we introduce a Distribution-AWare Adapter (DAWA) that adaptively fuses instance-level visual knowledge from few-shot samples with distribution-aware representations derived from Gaussian Discriminant Analysis based on the original CLIP zero-shot knowledge, leading to stable visual feature representations under various few-shot settings. A hybrid loss function that incorporates transductive and contrastive regularization is employed to further prevent overfitting and improve the discriminability of features. Furthermore, we generate category-level fine-grained text captions, optimizing the image–text alignment when extremely few training images are available. Experiments on multiple remote sensing and natural image datasets verify that the proposed framework achieves state-of-the-art few-shot fine-grained classification performance with a modest training cost, providing a practical solution for few-shot remote sensing image analysis.