Frank R. Schmidt

Postdoctoral Researcher


Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs

Emanuel Laude, Jan-Hendrik Lange, Jonas Schüpfer, Csaba Domokos, Laura Leal-Taixé, Frank R. Schmidt, Björn Andres, Daniel Cremers
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) - Jun 2018
Download the publication : 15 MB 
This paper introduces a novel algorithm for transductive inference in higher-order MRFs, where the unary energies are parameterized by a variable classifier. The considered task is posed as a joint optimization problem in the continuous classifier parameters and the discrete label vari- ables. In contrast to prior approaches such as convex relaxations, we propose an advantageous decoupling of the objective function into discrete and continuous subproblems and a novel, efficient optimization method related to ADMM. This approach preserves integrality of the discrete label variables and guarantees global convergence to a critical point. We demonstrate the advantages of our approach in several experiments including video object segmentation on the DAVIS data set and interactive image segmentation.

For a more recent version please visit: arXiv

Images and movies

teaser.png (150 KB)

BibTex references

  author       = "Laude, Emanuel and Lange, Jan-Hendrik and Sch{\"u}pfer, Jonas and Domokos, Csaba and Leal-Taix\'e, Laura and Schmidt, Frank R. and Andres, Bj{\"o}rn and Cremers, Daniel",
  title        = "Discrete-Continuous ADMM for Transductive Inference in Higher-Order MRFs",
  booktitle    = "IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
  month        = "Jun",
  year         = "2018",
  url          = ""