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Conference Paper: DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation

TitleDecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation
Authors
KeywordsSemantic segmentation
Unsupervised domain adaptation
Issue Date2022
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, v. 13693 LNCS, p. 369-387 How to Cite?
AbstractUnsupervised domain adaptation in semantic segmentation alleviates the reliance on expensive pixel-wise annotation. It uses a labeled source domain dataset as well as unlabeled target domain images to learn a segmentation network. In this paper, we observe two main issues of existing domain-invariant learning framework. (1) Being distracted by the feature distribution alignment, the network cannot focus on the segmentation task. (2) Fitting source domain data well would compromise the target domain performance. To address these issues, we propose DecoupleNet to alleviate source domain overfitting and let the final model focus more on the segmentation task. Also, we put forward Self-Discrimination (SD) and introduce an auxiliary classifier to learn more discriminative target domain features with pseudo labels. Finally, we propose Online Enhanced Self-Training (OEST) to contextually enhance the quality of pseudo labels in an online manner. Experiments show our method outperforms existing state-of-the-art methods. Extensive ablation studies verify the effectiveness of each component. Code is available at https://github.com/dvlab-research/DecoupleNet.
Persistent Identifierhttp://hdl.handle.net/10722/333708
ISSN
2023 SCImago Journal Rankings: 0.606
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLai, Xin-
dc.contributor.authorTian, Zhuotao-
dc.contributor.authorXu, Xiaogang-
dc.contributor.authorChen, Yingcong-
dc.contributor.authorLiu, Shu-
dc.contributor.authorZhao, Hengshuang-
dc.contributor.authorWang, Liwei-
dc.contributor.authorJia, Jiaya-
dc.date.accessioned2023-10-06T05:21:45Z-
dc.date.available2023-10-06T05:21:45Z-
dc.date.issued2022-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, v. 13693 LNCS, p. 369-387-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/333708-
dc.description.abstractUnsupervised domain adaptation in semantic segmentation alleviates the reliance on expensive pixel-wise annotation. It uses a labeled source domain dataset as well as unlabeled target domain images to learn a segmentation network. In this paper, we observe two main issues of existing domain-invariant learning framework. (1) Being distracted by the feature distribution alignment, the network cannot focus on the segmentation task. (2) Fitting source domain data well would compromise the target domain performance. To address these issues, we propose DecoupleNet to alleviate source domain overfitting and let the final model focus more on the segmentation task. Also, we put forward Self-Discrimination (SD) and introduce an auxiliary classifier to learn more discriminative target domain features with pseudo labels. Finally, we propose Online Enhanced Self-Training (OEST) to contextually enhance the quality of pseudo labels in an online manner. Experiments show our method outperforms existing state-of-the-art methods. Extensive ablation studies verify the effectiveness of each component. Code is available at https://github.com/dvlab-research/DecoupleNet.-
dc.languageeng-
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.subjectSemantic segmentation-
dc.subjectUnsupervised domain adaptation-
dc.titleDecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-031-19827-4_22-
dc.identifier.scopuseid_2-s2.0-85142741130-
dc.identifier.volume13693 LNCS-
dc.identifier.spage369-
dc.identifier.epage387-
dc.identifier.eissn1611-3349-
dc.identifier.isiWOS:000903572500022-

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