Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation

Myeongjin Kim, Hyeran Byun

Research output: Contribution to journalConference articlepeer-review

127 Citations (Scopus)


Since annotating pixel-level labels for semantic segmentation is laborious, leveraging synthetic data is an attractive solution. However, due to the domain gap between synthetic domain and real domain, it is challenging for a model trained with synthetic data to generalize to real data. In this paper, considering the fundamental difference between the two domains as the texture, we propose a method to adapt to the target domain's texture. First, we diversity the texture of synthetic images using a style transfer algorithm. The various textures of generated images prevent a segmentation model from overfitting to one specific (synthetic) texture. Then, we fine-Tune the model with self-Training to get direct supervision of the target texture. Our results achieve state-of-The-Art performance and we analyze the properties of the model trained on the stylized dataset with extensive experiments.

Original languageEnglish
Article number9157766
Pages (from-to)12972-12981
Number of pages10
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Publication statusPublished - 2020
Event2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 - Virtual, Online, United States
Duration: 2020 Jun 142020 Jun 19

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea grant funded by Korean government (No. NRF-2019R1A2C2003760).

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition


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