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Fully Convolutional Networks for Semantic Segmentation

Fully Convolutional Networks for Semantic Segmentation

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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, exceed the state-of-the-art in semantic segmentation. Our key insight is to build “fully convolutional”networks that take input of arbitrary size and producecorrespondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially denseprediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet [19],the VGG net [31], and GoogLeNet [32]) into fully convolutional networks and transfer their learned representationsby fine-tuning [4] to the segmentation task. We then de-fine a novel architecture that combines semantic information from a deep, coarse layer with appearance informationfrom a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achievesstate-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2,and SIFT Flow, while inference takes less than one fifth of asecond for a typical imag
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