Convolutional CRFs for Semantic Segmentation
This addresses efficiency bottlenecks for researchers and practitioners in computer vision, though it is incremental as it builds on existing CRF and CNN methods.
The paper tackles the slow training and inference speeds of Conditional Random Fields (CRFs) in semantic image segmentation by proposing convolutional CRFs, which reformulate inference as convolutions, speeding it up by over 100 times and enabling easy parameter optimization via backpropagation.
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