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Distributed Message Passing for Large Scale Graphical Models

Alexander G. Schwing, Tamir Hazan, Marc Pollefeys, Raquel Urtasun
CVPR 2011 pp. 1833-1840
doi:10.1109/CVPR.2011.5995642 /cvpr/2011/schwing2011cvpr-distributed/

Abstract

In this paper we propose a distributed message-passing algorithm for inference in large scale graphical models. Our method can handle large problems efficiently by distributing and parallelizing the computation and memory requirements. The convergence and optimality guarantees of recently developed message-passing algorithms are preserved by introducing new types of consistency messages, sent between the distributed computers. We demonstrate the effectiveness of our approach in the task of stereo reconstruction from high-resolution imagery, and show that inference is possible with more than 200 labels in images larger than 10 MPixels.

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Cite

Text

Schwing et al. "Distributed Message Passing for Large Scale Graphical Models." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011. doi:10.1109/CVPR.2011.5995642

Markdown

[Schwing et al. "Distributed Message Passing for Large Scale Graphical Models." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011.](https://mlanthology.org/cvpr/2011/schwing2011cvpr-distributed/) doi:10.1109/CVPR.2011.5995642

BibTeX

@inproceedings{schwing2011cvpr-distributed,
  title     = {{Distributed Message Passing for Large Scale Graphical Models}},
  author    = {Schwing, Alexander G. and Hazan, Tamir and Pollefeys, Marc and Urtasun, Raquel},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2011},
  pages     = {1833-1840},
  doi       = {10.1109/CVPR.2011.5995642},
  url       = {https://mlanthology.org/cvpr/2011/schwing2011cvpr-distributed/}
}