现代信息科技2024，Vol.8Issue(2)：1-4,4.DOI:10.19850/j.cnki.2096-4706.2024.02.001
基于UPS策略自我训练的半监督语义分割
Semi-supervised Semantic Segmentation Based on UPS Strategy Self Training
摘要
Abstract
To improve the effectiveness of semi-supervised semantic segmentation,this paper proposes a semi-supervised semantic segmentation network SPNS that combines loss normalization technology with UPS strategy.Using loss normalization techniques to alleviate the instability of self training in standard loss functions;the UPS strategy is a technique that combines uncertainty estimation and passive learning,by calculating the incompleteness of the output value as another threshold,reliable pseudo labels are selected,and finally the semi-supervised semantic segmentation task is completed using the generated pseudo labels and labeled data.The SPNS method has +2.06 improvement compared to training with only labeled data on the PASCAL·VOC dataset,and also has some improvement compared to other methods.关键词
半监督/语义分割/自我训练/UPS/消极学习Key words
semi-supervised/semantic segmentation/self training/UPS/negative learning分类
信息技术与安全科学引用本文复制引用
李雨杭,朱小东,杨高明..基于UPS策略自我训练的半监督语义分割[J].现代信息科技,2024,8(2):1-4,4.基金项目
安徽高校自然科学研究项目(KJ2017A084) （KJ2017A084）
安徽省自然科学基金面上项目(1808085MF179) （1808085MF179）
