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

arXiv:2305.06024v3 Announce Type: replace
Abstract: The performance of computer vision models are susceptible to unexpected changes in input images, known as common corruptions (e.g. noise, blur, illumination changes, etc.), that can hinder their reliability when deployed in real scenarios. These corruptions are not always considered to test 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. We categorize methods into four groups based on …

abstract arxiv computer computer vision cs.cv etc hinder images noise performance reliability robustness survey test type vision vision models