@conference {Krull2017,
	title = {PoseAgent: Budget-constrained 6D object pose estimation via reinforcement learning},
	booktitle = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
	volume = {2017-Janua},
	year = {2017},
	month = {dec},
	pages = {2566{\textendash}2574},
	abstract = {State-of-the-art computer vision algorithms often achieve efficiency by making discrete choices about which hypotheses to explore next. This allows allocation of computational resources to promising candidates, however, such decisions are non-differentiable. As a result, these algorithms are hard to train in an end-to-end fashion. In this work we propose to learn an efficient algorithm for the task of 6D object pose estimation. Our system optimizes the parameters of an existing state-of-the art pose estimation system using reinforcement learning, where the pose estimation system now becomes the stochastic policy, parametrized by a CNN. Additionally, we present an efficient training algorithm that dramatically reduces computation time. We show empirically that our learned pose estimation procedure makes better use of limited resources and improves upon the state-of-the-art on a challenging dataset. Our approach enables differentiable end-to-end training of complex algorithmic pipelines and learns to make optimal use of a given computational budget.},
	isbn = {9781538604571},
	doi = {10.1109/CVPR.2017.275},
	url = {http://arxiv.org/abs/1612.03779},
	author = {Krull, Alexander and Brachmann, Eric and Nowozin, Sebastian and Michel, Frank and Shotton, Jamie and Carsten Rother}
}
@conference {Pletscher2011,
	title = {Putting MAP back on the map},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6835 LNCS},
	year = {2011},
	pages = {111{\textendash}121},
	abstract = {Conditional Random Fields (CRFs) are popular models in computer vision for solving labeling problems such as image denoising. This paper tackles the rarely addressed but important problem of learning the full form of the potential functions of pairwise CRFs. We examine two popular learning techniques, maximum likelihood estimation and maximum margin training. The main focus of the paper is on models such as pairwise CRFs, that are simplistic (misspecified) and do not fit the data well. We empirically demonstrate that for misspecified models maximum-margin training with MAP prediction is superior to maximum likelihood estimation with any other prediction method. Additionally we examine the common belief that MLE is better at producing predictions matching image statistics. {\textcopyright} 2011 Springer-Verlag.},
	isbn = {9783642231223},
	issn = {03029743},
	doi = {10.1007/978-3-642-23123-0_12},
	author = {Pletscher, Patrick and Nowozin, Sebastian and Kohli, Pushmeet and Carsten Rother}
}
@conference {Pletscher2011a,
	title = {Putting MAP back on the map},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6835 LNCS},
	year = {2011},
	pages = {111{\textendash}121},
	abstract = {Conditional Random Fields (CRFs) are popular models in computer vision for solving labeling problems such as image denoising. This paper tackles the rarely addressed but important problem of learning the full form of the potential functions of pairwise CRFs. We examine two popular learning techniques, maximum likelihood estimation and maximum margin training. The main focus of the paper is on models such as pairwise CRFs, that are simplistic (misspecified) and do not fit the data well. We empirically demonstrate that for misspecified models maximum-margin training with MAP prediction is superior to maximum likelihood estimation with any other prediction method. Additionally we examine the common belief that MLE is better at producing predictions matching image statistics. {\textcopyright} 2011 Springer-Verlag.},
	isbn = {9783642231223},
	issn = {03029743},
	doi = {10.1007/978-3-642-23123-0_12},
	author = {Pletscher, Patrick and Nowozin, Sebastian and Kohli, Pushmeet and Carsten Rother}
}
