Method of Fine-Grained Visual Data Augmentation Based on Adversarial Erasing
Different from conventional image recognition,the visual differences of different categories in fine-grained image recognition usually only depend on the subtle parts of the object.Therefore,it is extremely important to find out the discriminative subtle parts of the object for fine-grained image recognition.In this paper,a data augmentation based on adversarial erasing(AE-DA)is proposed.In the training stage,the most discriminative subtle part of the object is firstly located as the augmented part image through the feature map,then the most discriminative part of the object is erased as the augmented complementary image.The net-work can learn the most discriminative subtle part of the object by part image,and find out other discriminative subtle parts of the object by complementary image.The Experiments show that the proposed data augmentation can greatly improve the model perfor-mance and outperforms than the classical method Cutout based on erasing.Furthermore,the proposed method is improved and achieving the classification accuracy of 88.7%,94.2%and 95.3%on CUB,Aircraft and Cars by incorporating the location module(AOLM).At the same time,it also greatly improves the location performance,demonstrating its potential in the weakly supervised object localization task.
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