For the challenging semantic image segmentation task the best performing 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. This allows us to reformulate the inference in terms of
 convolutions, which can be implemented highly efficiently on GPUs.Doing so
 speeds up inference and training by two orders of magnitude. All parameters of
 the convolutional CRFs can easily be optimized using backpropagation. Towards
 the goal of facilitating further CRF research we have made our implementations
 publicly available.
We propose Convolutional CRFs a fast, powerful and trainable alternative to Fully Connected CRFs. The authors replace the large filtering step in the permutohedral lattice with a spatially varying convolutional kernel and show that inference is more efficient and training is easier.  Proposes to perform message passing on a truncated Gaussian kernel CRF using a defined kernel and parallelized message passing on GPU.