Show simple item record

dc.contributor.authorTeichmann, Marvinen
dc.contributor.authorCipolla, Robertoen
dc.date.accessioned2019-07-26T23:30:08Z
dc.date.available2019-07-26T23:30:08Z
dc.date.issued2020-01-01en
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/294983
dc.description.abstractFor 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.
dc.rightsAll rights reserved
dc.titleConvolutional CRFs for semantic segmentationen
dc.typeConference Object
prism.publicationDate2020en
prism.publicationName30th British Machine Vision Conference 2019, BMVC 2019en
dc.identifier.doi10.17863/CAM.42064
dcterms.dateAccepted2019-06-22en
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2020-01-01en
dc.contributor.orcidCipolla, Roberto [0000-0002-8999-2151]
rioxxterms.typeConference Paper/Proceeding/Abstracten
cam.orpheus.successThu Nov 05 11:54:22 GMT 2020 - Embargo updated*
rioxxterms.freetoread.startdate2021-01-01


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record