中文版 | English
Title

GuidedMix-Net: Semi-Supervised Semantic Segmentation by Using Labeled Images as Reference

Author
Corresponding AuthorFeng Zheng
Publication Years
2022
Conference Name
36th AAAI Conference on Artificial Intelligence / 34th Conference on Innovative Applications of Artificial Intelligence / 12th Symposium on Educational Advances in Artificial Intelligence
ISSN
2159-5399
EISSN
2374-3468
Source Title
Conference Date
FEB 22-MAR 01, 2022
Conference Place
null,null,ELECTR NETWORK
Publication Place
2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA
Publisher
Abstract
Semi-supervised learning is a challenging problem which aims to construct a model by learning from limited labeled examples. Numerous methods for this task focus on utilizing the predictions of unlabeled instances consistency alone to regularize networks. However, treating labeled and unlabeled data separately often leads to the discarding of mass prior knowledge learned from the labeled examples. In this paper, we propose a novel method for semi-supervised semantic segmentation named GuidedMix-Net, by leveraging labeled information to guide the learning of unlabeled instances. Specifically, GuidedMix-Net employs three operations: 1) interpolation of similar labeled-unlabeled image pairs; 2) transfer of mutual information; 3) generalization of pseudo masks. It enables segmentation models can learning the higher-quality pseudo masks of unlabeled data by transfer the knowledge from labeled samples to unlabeled data. Along with supervised learning for labeled data, the prediction of unlabeled data is jointly learned with the generated pseudo masks from the mixed data. Extensive experiments on PASCAL VOC 2012, and Cityscapes demonstrate the effectiveness of our GuidedMix-Net, which achieves competitive segmentation accuracy and significantly improves the mIoU over 7% compared to previous approaches.
SUSTech Authorship
First ; Corresponding
Language
English
URL[Source Record]
Indexed By
Funding Project
National Natural Science Foundation of China["61972188","62122035"]
WOS Research Area
Computer Science
WOS Subject
Computer Science, Artificial Intelligence
WOS Accession No
WOS:000893636202051
Data Source
Web of Science
Citation statistics
Cited Times [WOS]:2
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/415790
DepartmentDepartment of Computer Science and Engineering
Affiliation
1.Southern University of Science and Technology, China
2.Shenzhen Microbt Electronics Technology Co., Ltd, China
3.Tencent Jarvis Lab, Shenzhen, China
4.Harbin Institute of Technology, Shenzhen, China
5.Xiamen University, Xiamen, China
6.National Center for Artificial Intelligence, Saudi Data and Artificial Intelligence Authority, Riyadh, Saudi Arabia
First Author AffilicationSouthern University of Science and Technology
Corresponding Author AffilicationSouthern University of Science and Technology
First Author's First AffilicationSouthern University of Science and Technology
Recommended Citation
GB/T 7714
Peng Tu,Yawen Huang,Feng Zheng,et al. GuidedMix-Net: Semi-Supervised Semantic Segmentation by Using Labeled Images as Reference[C]. 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA:ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE,2022.
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