Distributed Inference in Random Fields

Probabilistic graphical models such as MRF/CRF have become ubiquitous in computer vision for a variety of important, high-dimensional, discrete inference problems such as per-pixel object labelling, image denoising, disparity and optical flow estimation, etc. While recent advances in combinatorial optimization have focused on important guarantees of convergence, this is not sufficient to achieve desired efficiency on large scale problems (millions of pixels with thousands of labels).

As a consequence, algorithms that work well on smaller benchmarks can become impractical on very large scale problems. This concern is at the heart of present work; in particular, given limited number of cpu cores, speed limitations of hard-drives and high costs of shared memory systems, massively parallel processors present an appealing computing paradigm. Thus, it becomes of paramount importance that new optimization algorithms can run in a parallel and distributed fashion on modern clusters and GPUs.


Distributed Non-Convex ADMM-inference in Large-scale Random Fields
Miksik O., Vineet V., Perez P. and Torr P.H.S.
In Proceedings of the British Machine Vision Conference (BMVC) 2014, Nottingham, UK
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