In-and-Out: a data augmentation technique for computer vision tasks

Abstract

This study focuses on the over-fitting problem in the training process of the deep convolutional neural network model and the problem of poor robustness when the model is applied in an occlusion environment. We propose a unique data augmentation method, In-and-Out. First, the information variance is enhanced through dynamic local operation while maintaining the overall geometric structure of the training image; compared with the global data augmentation method, our method effectively alleviates the overfitting problem of model training and significantly improves the generalization ability of the model. Then through the dynamic information removal operation, the image is hidden according to the dynamic patch generated by multiple parameters. Compared with other information removal methods, our method can better simulate the real-world occlusion environment, thus improving the robustness of the model in various occlusion scenes. This method is simple and easy to implement and can be integrated with most CNN-based computer vision tasks. Our extensive experiments show that our method surpasses previous methods on the Canadian Institute for Advanced Research dataset for image classification, the PASCAL Visual Object Classes dataset for object detection, and the Cityscapes dataset for semantic segmentation. In addition, our robustness experiments show that our method has good robustness to occlusion in various scenes.