All about structure: Adapting structural information across domains for boosting semantic segmentation

Wei Lun Chang, Hui Po Wang, Wen-Hsiao Peng, Wei-Chen Chiu

研究成果: Conference contribution同行評審

238 引文 斯高帕斯(Scopus)

摘要

In this paper we tackle the problem of unsupervised domain adaptation for the task of semantic segmentation, where we attempt to transfer the knowledge learned upon synthetic datasets with ground-truth labels to real-world images without any annotation. With the hypothesis that the structural content of images is the most informative and decisive factor to semantic segmentation and can be readily shared across domains, we propose a Domain Invariant Structure Extraction (DISE) framework to disentangle images into domain-invariant structure and domain-specific texture representations, which can further realize image-translation across domains and enable label transfer to improve segmentation performance. Extensive experiments verify the effectiveness of our proposed DISE model and demonstrate its superiority over several state-of-the-art approaches.

原文English
主出版物標題Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
發行者IEEE Computer Society
頁面1900-1909
頁數10
DOIs
出版狀態Published - 6月 2019
事件32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 - Long Beach, 美國
持續時間: 16 6月 201920 6月 2019

出版系列

名字Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
2019-June
ISSN(列印)1063-6919

Conference

Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
國家/地區美國
城市Long Beach
期間16/06/1920/06/19

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