Domain Adaptive Semantic Segmentation via Entropy-Ranking and Uncertain Learning-Based Self-Training  

作　　者：Chengli Peng Jiayi Ma 

机构地区：[1]Electronic Information School,Wuhan University,Wuhan 430072,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2022年第8期1524-1527,共4页自动化学报（英文版）

基　　金：supported by the Key Research and Development Program of Hubei Province(2020BAB113);the Natural Science Fund of Hubei Province(2019CFA037)。

摘　　要：Dear Editor,This letter develops two new self-training strategies for domain adaptive semantic segmentation,which formulate self-training into the processes of mining more training samples and reducing influence of the false pseudo-labels.Particularly,a self-training strategy based on entropy-ranking is proposed to mine intra-domain information.

关 键 词：false MINING ENTROPY 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]