Learning Message-Passing Inference Machines for Structured Prediction

Nearly every structured prediction problem in computer vision requires approximate inference due to large and complex dependencies among output labels. While graphical models provide a clean separation between modeling and inference, learning these models with approximate inference is not well understood. Furthermore, even if a good model is learned, predictions are often inaccurate due to approximations. In this work, instead of performing inference over a graphical model, we instead consider the inference procedure as a composition of predictors. Specifically, we focus on message-passing algorithms, such as Belief Propagation, and show how they can be viewed as procedures that sequentially predict label distributions at each node over a graph. Given labeled graphs, we can then train the sequence of predictors to output the correct labelings. The result no longer corresponds to a graphical model but simply defines an inference procedure, with strong theoretical properties, that can ...

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Stephane Ross, Daniel Munoz, J. Andrew Bagnell
• Keywords: Approximate Inference, Computer Vision, CVPR 2011, Graphical Model, Inference