Efficient Structured Prediction with Latent Variables for General Graphical Models
Machine Learning
2012-07-03 v1 Machine Learning
Abstract
In this paper we propose a unified framework for structured prediction with latent variables which includes hidden conditional random fields and latent structured support vector machines as special cases. We describe a local entropy approximation for this general formulation using duality, and derive an efficient message passing algorithm that is guaranteed to converge. We demonstrate its effectiveness in the tasks of image segmentation as well as 3D indoor scene understanding from single images, showing that our approach is superior to latent structured support vector machines and hidden conditional random fields.
Cite
@article{arxiv.1206.6436,
title = {Efficient Structured Prediction with Latent Variables for General Graphical Models},
author = {Alexander Schwing and Tamir Hazan and Marc Pollefeys and Raquel Urtasun},
journal= {arXiv preprint arXiv:1206.6436},
year = {2012}
}
Comments
Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012)