Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation

@article{Lin2016EfficientPT,
  title={Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation},
  author={Guosheng Lin and Chunhua Shen and Anton van dan Hengel and Ian Reid},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={3194-3203}
}
  • Guosheng Lin, Chunhua Shen, +1 author Ian Reid
  • Published 2016
  • Computer Science
  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • Recent advances in semantic image segmentation have mostly been achieved by training deep convolutional neural networks (CNNs). We show how to improve semantic segmentation through the use of contextual information, specifically, we explore 'patch-patch' context between image regions, and 'patch-background' context. For learning from the patch-patch context, we formulate Conditional Random Fields (CRFs) with CNN-based pairwise potential functions to capture semantic correlations between… CONTINUE READING
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