Our fully convolutional network achieves state-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 a second for a typical image. Jon…

We then define a novel architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Jonathan Long, et al., "Fully Convoluti…

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. Jonathan Long, et al., "Fully Convolu…

We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. Jonathan Long, et al., "Fully Convolutional Networks for Semantic Segmenta…

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. Jonathan Long, et al., "Fully Convolutional Networks for Semantic …

We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. Jonathan Long, et al., "Fully Convolutional Networks for Semantic Segmentation" https://arxiv.or…

Convolutional networks are powerful visual models that yield hierarchies of features. Jonathan Long, et al., "Fully Convolutional Networks for Semantic Segmentation" https://arxiv.org/abs/1411.4038 全結合層を使わないCNNで、あらゆる画像サイ…