title: Convolutional CRFs for Semantic Segmentation
abstract: 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.
authors: Marvin Teichmann Roberto Cipolla
hIndex: Roberto Cipolla 81