CLIP-FG:Selecting Discriminative Image Patches by Contrastive Language-Image Pre-Training for Fine-Grained Image Classification

Fine-grained Visual Classification (FGVC), which aims to identify objects from subcategories, presents great challenges for classification due to large intra-class differences and subtle inter-class differences. To address these issues of FGVC, this paper proposes a patch selection model referenced from CLIP for Fine Grained Visual Classification, namely CLIP-FG. Specifically, unlike the previous CLIP, which focused only on the level of text and image, we calculate the similarity between labels and image patches. Top k image patches are selected and their indexes fed into the Vision Transformer to select discriminative areas to improve the performance of fine grained image classification. Quantitative evaluations show CLIP-FG's competitive performance against mainstream methods.