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This paper describes a fast and accurate semantic image segmentation approachthat encodes not only the discriminative features from deep neural networks,but also the high-order context compatibility among adjacent objects as well aslow level image features. We formulate the underlying problem as theconditional random field that embeds local feature extraction, clique potentialconstruction, and guided filtering within the same framework, and provide anefficient coarse-to-fine solver. At the coarse level, we combine local featurerepresentation and context interaction using a deep convolutional network, anddirectly learn the interaction from high order cliques with a message passingroutine, avoiding time-consuming explicit graph inference for joint probabilitydistribution. At the fine level, we introduce a guided filtering interpretationfor the mean field algorithm, and achieve accurate object boundaries with 100+faster than classic learning methods. The two parts are connected and jointlytrained in an end-to-end fashion. Experimental results on Pascal VOC 2012dataset have shown that the proposed algorithm outperforms thestate-of-the-art, and that it achieves the rank 1 performance at the time ofsubmission, both of which prove the effectiveness of this unified framework forsemantic image segmentation.

Fast Semantic Image Segmentation with High Order Context and Guided Filtering