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MAP Inference via Block-Coordinate Frank-Wolfe Algorithm

Paul Swoboda, Vladimir Kolmogorov
CVPR 2019
doi:10.1109/CVPR.2019.01140 /cvpr/2019/swoboda2019cvpr-map/

Abstract

We present a new proximal bundle method for Maximum-A-Posteriori (MAP) inference in structured energy minimization problems. The method optimizes a Lagrangean relaxation of the original energy minimization problem using a multi plane block-coordinate Frank-Wolfe method that takes advantage of the specific structure of the Lagrangean decomposition. We show empirically that our method outperforms state-of-the-art Lagrangean decomposition based algorithms on some challenging Markov Random Field, multi-label discrete tomography and graph matching problems.

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Cite

Text

Swoboda and Kolmogorov. "MAP Inference via Block-Coordinate Frank-Wolfe Algorithm." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.01140

Markdown

[Swoboda and Kolmogorov. "MAP Inference via Block-Coordinate Frank-Wolfe Algorithm." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/swoboda2019cvpr-map/) doi:10.1109/CVPR.2019.01140

BibTeX

@inproceedings{swoboda2019cvpr-map,
  title     = {{MAP Inference via Block-Coordinate Frank-Wolfe Algorithm}},
  author    = {Swoboda, Paul and Kolmogorov, Vladimir},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2019},
  doi       = {10.1109/CVPR.2019.01140},
  url       = {https://mlanthology.org/cvpr/2019/swoboda2019cvpr-map/}
}