A survey on unsupervised domain adaptation in computer vision tasks

Abstract

As one of the typical applications of Industrial Internet, Internet of Vehicles (IoV) develops rapidly in recent years. It relies on the interconnection of information, where the transferability of the accurate perception is of great importance. Though deep learning has accelerated the development of computer vision tasks, traditional deep learning-based methods still have strong reliance on manually annotated training data and are poor at generalizing knowledge to new environments. For computer vision tasks, since it is difficult to collect training data with ground truth, it is agent to improve the generalization ability of deep learning models and alleviate their dependence on manually annotated labels. Unsupervised domain adaptation (UDA) methods apply deep learning models to extract and align features from data in different domains, which ensures the satisfactory generalization performance of deep learning-based computer vision algorithms. This paper focuses on the challenges and applications of UDA in some typical computer vision tasks. Firstly, the definition, significances, application difficulties, basic methods and relevant datasets of deep learning-based UDA methods are introduced. Then, the mainstream UDA methods in typical computer vision tasks are introduced separately. Finally, the prospective technical development trends and a summary are given.