A Survey on the Robustness of Computer Vision Models against Common Corruptions

Understand

The performance of computer vision models are susceptible to unexpected changes in input images caused by sensor errors or extreme imaging environments, known as common corruptions (e.g.

• noise, blur, illumination changes).
• These corruptions can significantly hinder the reliability of these models when deployed in real-world scenarios, yet they are often overlooked when testing model generalization and robustness.
• In this survey, we present a comprehensive overview of methods that improve the robustness of computer vision models against common corruptions.