• DOI:10.3390/s23020621
• Corpus ID: 249210121

Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models

@article{Gmez2022CoTrainingFU,
  title={Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models},
  author={Jose Luis G{\'o}mez and Gabriel Villalonga and Antonio M. L'opez},
  journal={Sensors (Basel, Switzerland)},
  year={2022},
  volume={23}
}

• J. L. Gómez, Gabriel Villalonga, Antonio M. L'opez
• Published 31 May 2022
• Computer Science
• Sensors (Basel, Switzerland)

Semantic image segmentation is a core task for autonomous driving, which is performed by deep models. Since training these models draws to a curse of human-based image labeling, the use of synthetic images with automatically generated labels together with unlabeled real-world images is a promising alternative. This implies addressing an unsupervised domain adaptation (UDA) problem. In this paper, we propose a new co-training procedure for synth-to-real UDA of semantic segmentation models. It performs iterations where the (unlabeled) real-world training images are labeled by intermediate deep models trained with both the (labeled) synthetic images and the real-world ones labeled in previous iterations. More specifically, a self-training stage provides two domain-adapted models and a model collaboration loop allows the mutual improvement of these two models. The final semantic segmentation labels (pseudo-labels) for the real-world images are provided by these two models. The overall procedure treats the deep models as black boxes and drives their collaboration at the level of pseudo-labeled target images, i.e., neither modifying loss functions is required, nor explicit feature alignment. We test our proposal on standard synthetic and real-world datasets for onboard semantic segmentation. Our procedure shows improvements ranging from approximately 13 to 31 mIoU points over baselines.