FET-FGVC: Feature-enhanced transformer for fine-grained visual classification

Abstract

The challenge of Fine-grained visual classification (FGVC) comes from the small variations between classes and the large variations within classes. Inspired by the fact that identifying bird species focuses not only on the global features of the subject area but also on the subtle details of the local area, we propose a feature-enhanced Transformer to improve the performance of FGVC. Our proposed method consists of a Dynamic Swin Transformer backbone for extracting comprehensive global image features through continuous attention aggregation, a GCN-based local branch for separating and enhancing local features in different regions, and a pairwise feature interaction (PFI) module for enhancing global features through interactions between image pairs. We conducted extensive experiments on five FGVC datasets to demonstrate the superiority of our method. By fusing the enhanced global and local features, our method achieves the best accuracy compared to existing methods. Our method has an advantage in terms of computational efficiency.