  We consider the problem of semantic image segmentation using deep
convolutional neural networks. We propose a novel network architecture called
the label refinement network that predicts segmentation labels in a
coarse-to-fine fashion at several resolutions. The segmentation labels at a
coarse resolution are used together with convolutional features to obtain finer
resolution segmentation labels. We define loss functions at several stages in
the network to provide supervisions at different stages. Our experimental
results on several standard datasets demonstrate that the proposed model
provides an effective way of producing pixel-wise dense image labeling.
