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…

• AC
  Australian Centre for Robotic Vision

  Award: CE140100016
• AR
  Australian Research Council

  Awards: FT12010096, FL130100102, CE14010001, CE140100016, FT120100969