Bidirectional Learning for Domain Adaptation of Semantic Segmentation
Open accessAbstract
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.
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Several UDA approaches are not specifically designed for UDA in satellite pose estimation because the domain-agnostic geometrical constraints are not fully explored.
“Nonetheless, these methods are not specifically designed for UDA in satellite pose estimation, since the domain-agnostic geometrical constraints are not fully explored.”
From Bridging the Domain Gap in Satellite Pose Estimation: A Self-Training Approach Based on Geometrical Constraints (2024) · cites this paper as [24]
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- Authors
- Li, Y., Yuan, L., & Vasconcelos, N.
- Affiliations
- University of California San Diego (US)
- Microsoft (United States) (US)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 6929-6938
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2940597502
- Cited by
- 704
- References
- 42
- Citations by year
- 2026: 20 · 2025: 61 · 2024: 96 · 2023: 151 · 2022: 145 · 2021: 158 · 2020: 65 · 2019: 7 · 2012: 1
- FWCI
- 35.87
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1637941, 1546305)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- domain adaptation, semantic segmentation, bidirectional learning, image-to-image translation, self-supervised learning, cross-domain segmentation
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