{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fully-convolutional-networks-for-semantic","title":"Fully Convolutional Networks for Semantic Segmentation","arxiv_id":"1605.06211","date":"2016-05-20","proceeding":"CVPR 2015","authors":["Evan Shelhamer","Jonathan Long","Trevor Darrell"],"abstract":"Convolutional networks are powerful visual models that yield hierarchies of\nfeatures. We show that convolutional networks by themselves, trained\nend-to-end, pixels-to-pixels, improve on the previous best result in semantic\nsegmentation. Our key insight is to build \"fully convolutional\" networks that\ntake input of arbitrary size and produce correspondingly-sized output with\nefficient inference and learning. We define and detail the space of fully\nconvolutional networks, explain their application to spatially dense prediction\ntasks, and draw connections to prior models. We adapt contemporary\nclassification networks (AlexNet, the VGG net, and GoogLeNet) into fully\nconvolutional networks and transfer their learned representations by\nfine-tuning to the segmentation task. We then define a skip architecture that\ncombines semantic information from a deep, coarse layer with appearance\ninformation from a shallow, fine layer to produce accurate and detailed\nsegmentations. Our fully convolutional network achieves improved segmentation\nof PASCAL VOC (30% relative improvement to 67.2% mean IU on 2012), NYUDv2, SIFT\nFlow, and PASCAL-Context, while inference takes one tenth of a second for a\ntypical image.","url_abs":"http://arxiv.org/abs/1605.06211v1","url_pdf":"http://arxiv.org/pdf/1605.06211v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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