Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes

@article{Zhang2017CurriculumDA,
  title={Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes},
  author={Yonghui Zhang and Philip David and Boqing Gong},
  journal={2017 IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  pages={2039-2049}
}
During the last half decade, convolutional neural networks (CNNs) have triumphed over semantic segmentation, which is a core task of various emerging industrial applications such as autonomous driving and medical imaging. However, to train CNNs requires a huge amount of data, which is difficult to collect and laborious to annotate. Recent advances in computer graphics make it possible to train CNN models on photo-realistic synthetic data with computer-generated annotations. Despite this, the… CONTINUE READING
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Curriculum domain adaptation for semantic segmentation of urban scenes

  • Y. Zhang, P. David, B. Gong
  • The IEEE International Conference on Computer…
  • 2017
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