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Semi-Supervised Semantic Segmentation with High- and Low-level Consistency

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019
Abstract: The ability to understand visual information from limited labeled data is an important aspect of machine learning. While image-level classification has been extensively studied in a semi-supervised setting, dense pixel-level classification with limited data has only drawn attention recently. In this work, we propose an approach for semi-supervised semantic segmentation that learns from limited pixel-wise annotated samples while exploiting additional annotation-free images. It uses two network branches that link semi-supervised classification with semi-supervised segmentation including self-training. The dual-branch approach reduces both the low-level and the high-level artifacts typical when training with few labels. The approach attains significant improvement over existing methods, especially when trained with very few labeled samples. On several standard benchmarks - PASCAL VOC 2012, PASCAL-Context, and Cityscapes - the approach achieves new state-of-the-art in semi-supervised learning.
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BibTex reference

@Article{MTB19,
  author       = "S. Mittal and M. Tatarchenko and T. Brox",
  title        = "Semi-Supervised Semantic Segmentation with High- and Low-level Consistency",
  journal      = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
  month        = " ",
  year         = "2019",
  url          = "http://lmb.informatik.uni-freiburg.de/Publications/2019/MTB19"
}

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