LAUSR

To parse images into fine-grained semantic parts, the complex elements will put it in trouble when using off-the-shelf semantic segmentation networks, because it is difficult for them to utilize the contextual information of fine-grained parts. In this paper we propose a progressive decomposition method to parse images in a coarse-to-fine manner with refined semantic classes. It consists of two aspects: stacked networks and progressive supervisions. The stacked network is achieved by stacking some segmentation layers in a segmentation network. The former segmentation module parses images at a coarser-grained level, and the result will be fed to the following one to provide effective contextual clues for the finer-grained parsing. The skip connections from shallow layers of the network to fine-grained parsing modules are also added to recover the details of small structures. For the training of the stacked networks which have coarse-to-fine outputs, a strategy of progressive supervision is proposed to merge classes in ground truth to get coarse-to-fine label maps, and then train the stacked network end-to-end with the hierarchical supervisions. The proposed framework can be injected into many advanced neural networks to improve the parsing results. Extensive evaluations on several public datasets including face parsing and human parsing well demonstrate the superiority of our method.