摘要
随着现代生活逐步智能化,越来越多的应用需要从图像中推断相应的语义信息再进行后续的处理,如虚拟现实、自动驾驶和视频监控等应用。目前的语义分割模型利用大量标注数据进行有监督训练能达到理想的性能,但模型对与训练数据不同分布的数据进行推理时,其性能严重下降。这意味着一旦应用场景发生变化,就需对新场景的数据进行标注。模型重新利用新数据进行训练,才能达到正常的性能。这无疑是耗时的、代价昂贵的。为此,领域自适应语义分割算法提供了解决模型在分布不一致数据上语义分割性能下降问题的思路。总结了领域自适应语义分割算法的前沿进展,并对未来研究方向进行展望。
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.
作者
应俊杰
楼陆飞
辛宇
Ying Junjie;Lou Lufei;Xin Yu(College of Information Science and Engineering,Ningbo University,Ningbo 315211,China;Key Laboratory of Mobile Network Application Technology of Zhejiang Province,Ningbo 315211,China)
出处
《电子技术应用》
2024年第1期1-9,共9页
Application of Electronic Technique
基金
浙江省自然科学基金(LY22F020001)
宁波市“泛3315”计划(2019B-18-G)。
关键词
领域自适应
语义分割
深度学习
domain adaptive
semantic segmentation
deep learning
