Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation
Recent advances in semantic image segmentation have mostly been achieved by training deep convolutional neural networks (CNNs) We show how to improve semantic segmentation through the use of contextual information, specifically, we explore 'patch patch' context between image regions, and 'patch background' context. For learning from the patch patch context, we formulate Conditional Random Fields (CRFs) with CNN based pairwise potential functions to capture semantic correlations between neighboring patches. Efficient piecewise training of the proposed deep structured model is then applied to avoid repeated expensive CRF inference for back propagation. For capturing the patch background context, we show that a network design with traditional multi scale image input and sliding pyramid pooling is effective for improving performance. Our experimental results set new state of the art performance on a number of popular semantic segmentation datasets, including NYUDv2, PASCAL VOC 2012, PASCAL Context, and SIFT flow. In particular, we achieve an intersection overunion score of 78:0 on the challenging PASCAL VOC 2012 dataset.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)