---
res:
  bibo_abstract:
  - We present LS-CRF, a new method for training cyclic Conditional Random Fields
    (CRFs) from large datasets that is inspired by classical closed-form expressions
    for the maximum likelihood parameters of a generative graphical model with tree
    topology. Training a CRF with LS-CRF requires only solving a set of independent
    regression problems, each of which can be solved efficiently in closed form or
    by an iterative solver. This makes LS-CRF orders of magnitude faster than classical
    CRF training based on probabilistic inference, and at the same time more flexible
    and easier to implement than other approximate techniques, such as pseudolikelihood
    or piecewise training. We apply LS-CRF to the task of semantic image segmentation,
    showing that it achieves on par accuracy to other training techniques at higher
    speed, thereby allowing efficient CRF training from very large training sets.
    For example, training a linearly parameterized pairwise CRF on 150,000 images
    requires less than one hour on a modern workstation.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Alexander
      foaf_name: Kolesnikov, Alexander
      foaf_surname: Kolesnikov
      foaf_workInfoHomepage: http://www.librecat.org/personId=2D157DB6-F248-11E8-B48F-1D18A9856A87
  - foaf_Person:
      foaf_givenName: Matthieu
      foaf_name: Guillaumin, Matthieu
      foaf_surname: Guillaumin
  - foaf_Person:
      foaf_givenName: Vittorio
      foaf_name: Ferrari, Vittorio
      foaf_surname: Ferrari
  - foaf_Person:
      foaf_givenName: Christoph
      foaf_name: Lampert, Christoph
      foaf_surname: Lampert
      foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-8622-7887
  bibo_doi: 10.1007/978-3-319-10578-9_36
  bibo_issue: PART 3
  bibo_volume: 8691
  dct_date: 2014^xs_gYear
  dct_language: eng
  dct_publisher: Springer@
  dct_title: Closed-form approximate CRF training for scalable image segmentation@
...