The paper introduces fully convolutional networks (FCNs) for semantic segmentation, demonstrating that end-to-end trained FCNs can outperform state-of-the-art methods. The key insight is to build FCNs that can handle arbitrary-sized inputs and produce correspondingly-sized outputs with efficient inference and learning. The authors adapt popular classification networks (AlexNet, VGG, and GoogLeNet) into FCNs and fine-tune them for the segmentation task. They also propose 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. The FCN achieves state-of-the-art performance on PASCAL VOC, NYUv2, and SIFT Flow datasets, with inference taking less than one-fifth of a second for typical images. The paper discusses the design of FCNs, dense prediction trade-offs, and experimental results, highlighting the efficiency and effectiveness of the proposed approach.The paper introduces fully convolutional networks (FCNs) for semantic segmentation, demonstrating that end-to-end trained FCNs can outperform state-of-the-art methods. The key insight is to build FCNs that can handle arbitrary-sized inputs and produce correspondingly-sized outputs with efficient inference and learning. The authors adapt popular classification networks (AlexNet, VGG, and GoogLeNet) into FCNs and fine-tune them for the segmentation task. They also propose 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. The FCN achieves state-of-the-art performance on PASCAL VOC, NYUv2, and SIFT Flow datasets, with inference taking less than one-fifth of a second for typical images. The paper discusses the design of FCNs, dense prediction trade-offs, and experimental results, highlighting the efficiency and effectiveness of the proposed approach.