Attention cutting and padding learning for fine-grained image recognition

Fine-grained image recognition is an important task in the field of computer vision. In fine-grained image recognition, the difference between different categories is very small. Thus, fine-grained image recognition highly depends on local features. In this paper, a novel "Attention Cutting And Padding Learning" method is proposed to learn the local features. Firstly, the image is fed to Convolutional Neural Networks, and a saliency map is gotten. According to the saliency map, the attention image is obtained. Secondly, the attention image is cut into N * N sub-images. Every sub-image is padded by 0 and the padding size is P. All sub-images are spliced into a Cutting And Padding image. Finally, the Cutting And Padding image and the attention image are fed to CNNs to train. In this method, more local features can be learned, and the high-level semantics is not damaged. Experimental results show that the recognition accuracy of Attention Cutting And Padding Learning is 87.9%, 94.6%, and 92.4% respectively on CUB-200-2011, Stanford Cars, and FGVC-Aircraft dataset. Moreover, this method can be easily applied to biodiversity automatic monitoring, intelligent retail, intelligent transportation, and other fields to improve recognition accuracy without changing the network structure.