Convolutional CRFs for Semantic Segmentation release_jd5thcsiwvedvftkfwiasphjua

by Marvin Teichmann, Roberto Cipolla, Apollo-University Of Cambridge Repository, Apollo-University Of Cambridge Repository

Published by Apollo - University of Cambridge Repository.

2019  

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.
In text/plain format

Archived Files and Locations

application/pdf   3.7 MB
file_yodtdyexvncphewghfga4awilm
openreview.net (web)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   published
Date   2019-07-26
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: fb77af30-0682-4942-bc7b-c8e61f4eb042
API URL: JSON