Fully Convolutional Networks for Semantic Segmentation

IEEE Trans. Pattern Anal. Mach. Intell., Volume abs/1605.06211, Issue 4, 2017, Pages 640-651.

Cited by: 5124|Bibtex|Views264|DOI:https://doi.org/10.1109/TPAMI.2016.2572683
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Other Links: dblp.uni-trier.de|dl.acm.org|academic.microsoft.com|arxiv.org

Abstract:

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 produc...More

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