Paper:
Message Passing Inference for Large Scale Graphical Models with High Order Potentials
To keep up with the Big Data challenge, parallelized algorithms based on dual decomposition have been proposed to perform inference in Markov random fields. Despite this parallelization, current algorithms struggle when the energy has high order terms and the graph is densely connected. In this paper we propose a partitioning strategy followed by a message passing algorithm which is able to exploit pre-computations. It only updates the high-order factors when passing messages across machines. We demonstrate the effectiveness of our approach on the task of joint layout and semantic segmentation estimation from single images, and show that our approach is orders of magnitude faster than current methods.

Review:
The paper improves on previous work on parallelizing MRF inference. The main insight is that when passing messages, summations over large state spaces can be delayed until the appropraite messages across computers are received. This is performed elegantly by adding new regions (typically correspodning to variable cliques)containing unions of high order parent regions' children, thus separating the higher order region for computation until the message from another computer arrives. Results are impressive, far better than the baselines compared with. The paper was carefully written (perhaps overoptimized in some ways).The two pieces of criticisms are:1. The basic idea is relatively straightforward (but, has not been done before, apparently).2. The writing is so dense and technical that it takes a while to get through the math and understand the relatively simple idea. Perhaps the authors should have a less abstract example (e.g., define an image task and a small number of regions, and then explain what the various messages mean). The details of the experiments (the expplanation/description of the task and the dataset) are also very compressed. Generally, the paper is succinct to the point that it has to be reread several times to be understood (but all details are there - that's why I say it was overoptimized).THe above pieces of criticism are there just to help the authors in writing. I do, however, support acceptance (even in current form).