  Visual classification can be divided into coarse-grained and fine-grained
classification. Coarse-grained classification represents categories with a
large degree of dissimilarity, such as the classification of cats and dogs,
while fine-grained classification represents classifications with a large
degree of similarity, such as cat species, bird species, and the makes or
models of vehicles. Unlike coarse-grained visual classification, fine-grained
visual classification often requires professional experts to label data, which
makes data more expensive. To meet this challenge, many approaches propose to
automatically find the most discriminative regions and use local features to
provide more precise features. These approaches only require image-level
annotations, thereby reducing the cost of annotation. However, most of these
methods require two- or multi-stage architectures and cannot be trained
end-to-end. Therefore, we propose a novel plug-in module that can be integrated
to many common backbones, including CNN-based or Transformer-based networks to
provide strongly discriminative regions. The plugin module can output
pixel-level feature maps and fuse filtered features to enhance fine-grained
visual classification. Experimental results show that the proposed plugin
module outperforms state-of-the-art approaches and significantly improves the
accuracy to 92.77\% and 92.83\% on CUB200-2011 and NABirds, respectively. We
have released our source code in Github
https://github.com/chou141253/FGVC-PIM.git.
