Class Activation Mapping Guided Data Augmentation for Fine-Grained Visual Classification

The key of fine-grained image classification is to extract discriminative partial features.In order to make full use of data,a visual attention guided data augmentation method is proposed to generate targeted im-ages based on Class Activation Mapping(CAM).Attention area found by CAM will be cropped and enlarged.A flow field grid will be generated to guide the sampling of original image so that the discriminative area can be exaggerated,therefore the network can learn more subtle features from two types of augmented images.An image with discriminative area dropped will be generated to encourage the network to learn other effective features.The algorithm only needs image-level labels without bounding boxes or parts labeling and can be trained end-to-end without other auxiliary networks.The experiments on CUB-200-2011,FGVC-Aircraft and Stanford Cars datasets demonstrate that capability of model is effectively improved,and the Top-1 accuracy metric is better than certain existing advanced algorithms.