Commonsense Oriented Fine-Grained Data Augmentation

The representative researches on data augmentation are mainly carried out on common classification benchmark datasets such as ImageNet. Considering intra- class and inter- class relation in fine- grained visual classification (FGVC) datasets is so different from ordinary classification datasets, data augmentation methods for FGVC need to be further studied. Therefore, this paper proposes a fine- grained semantic image patch mixing method by commonsense (ComSipmix), starting from the fine-grained recognition task and the special properties of the dataset. The proposed method exploits common sense knowledge to mine potential associations between sample labels, and designs a multi-branch convolutional neural network structure for structured image mixing strategy based on this, so that the image mixing process pays more attention to the subtle differences of targets. Through extensive performance tests, it can be verified that the performance of the proposed method is significantly better than the mainstream image mixing-based data augmentation methods. At the same time, through experimental verification, the common sense knowledge proposed in this paper helps to improve the performance of various data augmentation models based on mixed image classes. © 2017 Editorial Office of Tunnel Construction. All rights reserved.