The Robustness of Computer Vision Models against Common Corruptions: a Survey

The performance of computer vision models is susceptible to unexpected changes in input images when deployed in real scenarios. These changes are referred to as common corruptions. While they can hinder the applicability of computer vision models in real-world scenarios, they are not always considered as a testbed for model generalization and robustness. In this survey, we present a comprehensive and systematic overview of methods that improve corruption robustness of computer vision models. Unlike existing surveys that focus on adversarial attacks and label noise, we cover extensively the study of robustness to common corruptions that can occur when deploying computer vision models to work in practical applications. We describe different types of image corruption and provide the definition of corruption robustness. We then introduce relevant evaluation metrics and benchmark datasets. We categorize methods into four groups. We also cover indirect methods that show improvements in generalization and may improve corruption robustness as a byproduct. We report benchmark results collected from the literature and find that they are not evaluated in a unified manner, making it difficult to compare and analyze. We thus built a unified benchmark framework to obtain directly comparable results on benchmark datasets. Furthermore, we evaluate relevant backbone networks pre-trained on ImageNet using our framework, providing an overview of the base corruption robustness of existing models to help choose appropriate backbones for computer vision tasks. We identify that developing methods to handle a wide range of corruptions and efficiently learn with limited data and computational resources is crucial for future development. Additionally, we highlight the need for further investigation into the relationship among corruption robustness, OOD generalization, and shortcut learning.