Convolutional CRFs for Semantic Segmentation

Understand

For the challenging semantic image segmentation task the most efficient models have traditionally combined the structured modelling capabilities of Conditional Random Fields (CRFs) with the feature extraction power of CNNs.

• In more recent works however, CRF post-processing has fallen out of favour.
• We argue that this is mainly due to the slow training and inference speeds of CRFs, as well as the difficulty of learning the internal CRF parameters.
• To overcome both issues we propose to add the assumption of conditional independence to the framework of fully-connected CRFs.