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      Fully Convolutional Networks for Semantic Segmentation.

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          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 produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet, the VGG net, and GoogLeNet) into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional networks achieve improved segmentation of PASCAL VOC (30% relative improvement to 67.2% mean IU on 2012), NYUDv2, SIFT Flow, and PASCAL-Context, while inference takes one tenth of a second for a typical image.

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          Author and article information

          Journal
          IEEE Trans Pattern Anal Mach Intell
          IEEE transactions on pattern analysis and machine intelligence
          Institute of Electrical and Electronics Engineers (IEEE)
          1939-3539
          0098-5589
          Apr 2017
          : 39
          : 4
          Article
          10.1109/TPAMI.2016.2572683
          27244717
          d1ed55a3-1b19-41b5-92e4-5dd8c37a7ee5
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