  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. 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 a
factor of more then 100. All parameters of the convolutional CRFs can easily be
optimized using backpropagation. To facilitating further CRF research we make
our implementation publicly available. Please visit:
https://github.com/MarvinTeichmann/ConvCRF
