Semantic Segmentation via Structured Patch Prediction, Context CRF and Guidance CRF

This paper describes a fast and accurate semantic image segmentation approach that encodes not only segmentation-specified features but also high-order context compatibilities and boundary guidance constraints. We introduce a structured patch prediction technique to make a trade-off between classification discriminability and boundary sensibility for features. Both label and feature contexts are embedded to ensure recognition accuracy and compatibility, while the complexity of the high order cliques is reduced by a distance-aware sampling and pooling strategy. The proposed joint model also employs a guidance CRF to further enhance the segmentation performance. The message passing step is augmented with the guided filtering which enables an efficient and joint training of the whole system in an end-to-end fashion. Our proposed joint model outperforms the state-of-art on Pascal VOC 2012 and Cityscapes, with mIoU(%) of 82.5 and 79.2 respectively. It also reaches a leading performance on ADE20K, which is the dataset of the scene parsing track in ILSVRC 2016. The code is available at https://github.com/FalongShen/SegModel.