Fine-grained image classification based on attention-guided image enhancement

Extracting distinguished fine-grained features is essential for fine-grained image recognition tasks. Many researchers use expensive manual annotations to learn to distinguish part models, which may not be possible in practical applications. Unlike previous strongly supervised fine grained classification networks that require additional image annotations, weakly supervised fine grained image classification only requires label annotations. Recently, image enhancement has been increasingly used in network structures, but random enhancement will lead to background noise and filter out irrelevant areas. In this article, we propose a weakly supervised fine-grained image classification network based on attention-guided image enhancement to study the effect of image enhancement on the classification network. In detail, we use the backbone network to generate the feature map of the image, then generate the corresponding attention map through a custom mask, and use the attention map to guide the image enhancement process (including image cropping and image dropping). We conducted experiments on three commonly used fine-grained image classification datasets, and achieved sota effects in CUB, FGVC-Aircraft, and Stanford Cars.