Convolutional CRFs for Semantic Segmentation

Marvin Teichmann, Roberto Cipolla

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.

Keywords: conditional random fields, semantic segmentation, computer vision, structured learning

TL;DR: We propose Convolutional CRFs a fast, powerful and trainable alternative to Fully Connected CRFs.

Code: [ MarvinTeichmann/ConvCRF](https://github.com/MarvinTeichmann/ConvCRF)

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:1805.04777/code)