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A survey of unsupervised domain adaptive semantic segmentation algorithms based on deep learning
Ying Junjie1，2，Lou Lufei1，2，Xin Yu1，2
1.College of Information Science and Engineering， Ningbo University， Ningbo 315211， China； 2.Key Laboratory of Mobile Network Application Technology of Zhejiang Province， Ningbo 315211， China
Abstract： As modern life becomes increasingly intelligent, more and more applications require inferring semantic information from images before proceeding with further processing, such as virtual reality, autonomous driving, and video surveillance. Current semantic segmentation models achieve ideal performance through supervised training with a large amount of annotated data, but their performance severely deteriorates when inferring on data with a distribution different from the training data. This means that once the application scenario changes, new data needs to be annotated and the model needs to be retrained with the new data in order to achieve normal performance. This is undoubtedly time-consuming and expensive. Therefore, domain adaptive semantic segmentation algorithms provide a solution to the problem of the model's performance degradation on data with different distributions. This article summarizes the cutting-edge progress of domain adaptive semantic segmentation algorithms and looks forward to future research directions.
Key words : domain adaptive；semantic segmentation；deep learning
