Fully Convolutional Networks for Semantic Segmentation

@article{Shelhamer2015FullyCN,
  title={Fully Convolutional Networks for Semantic Segmentation},
  author={Evan Shelhamer and Jonathan Long and Trevor Darrell},
  journal={2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2015},
  pages={3431-3440}
}
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation. Our key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application… CONTINUE READING
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