Bidirectional Learning for Domain Adaptation of Semantic Segmentation

Yunsheng Li, Lu Yuan, Nuno Vasconcelos; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 6936-6945

Abstract


Domain adaptation for semantic image segmentation is very necessary since manually labeling large datasets with pixel-level labels is expensive and time consuming. Existing domain adaptation techniques either work on limited datasets, or yield not so good performance compared with supervised learning. In this paper, we propose a novel bidirectional learning framework for domain adaptation of segmentation. Using the bidirectional learning, the image translation model and the segmentation adaptation model can be learned alternatively and promote to each other.Furthermore, we propose a self-supervised learning algorithm to learn a better segmentation adaptation model and in return improve the image translation model. Experiments show that our method superior to the state-of-the-art methods in domain adaptation of segmentation with a big margin. The source code is available at https://github.com/liyunsheng13/BDL

Related Material


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[bibtex]
@InProceedings{Li_2019_CVPR,
author = {Li, Yunsheng and Yuan, Lu and Vasconcelos, Nuno},
title = {Bidirectional Learning for Domain Adaptation of Semantic Segmentation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}