0a7a7133ca8dedf3b5e487ee96b63ca9  pmc_9864152.pdf
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3b8bc23f44588133fd6863f26b46cb13b6a2fc54dfc28c45238bb9e0545e808c  pmc_9864152.pdf

Title:          Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models
Subject:        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.
Keywords:       domain adaptation; semi-supervised learning; semantic segmentation; autonomous driving
Author:         Jose L. Gómez, Gabriel Villalonga and Antonio M. López
Creator:        LaTeX with hyperref
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      Title: Introduction
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     Item: 
      Title: Related Works
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     Item: 
      Title: Method
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       Item: 
        Title: Self-Training Stage
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       Item: 
        Title: Co-Training Procedure
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        Title: Ablative Study and Qualitative Results
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      Title: Conclusions
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     Item: 
      Title: References
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    Title: Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models
    Author: Jose L. Gómez, Gabriel Villalonga and Antonio M. López
    Subject: 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.
    Keywords: domain adaptation; semi-supervised learning; semantic segmentation; autonomous driving
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   Type: CRC32
  Checksum: 0a7a7133ca8dedf3b5e487ee96b63ca9
   Type: MD5
  Checksum: b75eee5db6f9df22fe637c73811252b5ab2f12b1
   Type: SHA-1