@conference {Tourani2018,
	title = {MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {11208 LNCS},
	year = {2018},
	pages = {264{\textendash}281},
	abstract = {Dense, discrete Graphical Models with pairwise potentials are a powerful class of models which are employed in state-of-the-art computer vision and bio-imaging applications. This work introduces a new MAP-solver, based on the popular Dual Block-Coordinate Ascent principle. Surprisingly, by making a small change to a low-performing solver, the Max Product Linear Programming (MPLP) algorithm [7], we derive the new solver MPLP++ that significantly outperforms all existing solvers by a large margin, including the state-of-the-art solver Tree-Reweighted Sequential (TRW-S) message-passing algorithm [17]. Additionally, our solver is highly parallel, in contrast to TRW-S, which gives a further boost in performance with the proposed GPU and multi-thread CPU implementations. We verify the superiority of our algorithm on dense problems from publicly available benchmarks as well as a new benchmark for 6D Object Pose estimation. We also provide an ablation study with respect to graph density.},
	keywords = {Block-Coordinate-Ascent, graphical models, Message passing algorithms},
	isbn = {9783030012243},
	issn = {16113349},
	doi = {10.1007/978-3-030-01225-0_16},
	author = {Tourani, Siddharth and Shekhovtsov, Alexander and Carsten Rother and Savchynskyy, Bogdan}
}
@conference {Richmond2016,
	title = {Mapping auto-context decision forests to deep convnets for semantic segmentation},
	booktitle = {British Machine Vision Conference 2016, BMVC 2016},
	volume = {2016-Septe},
	year = {2016},
	month = {jul},
	pages = {144.1{\textendash}144.12},
	abstract = {We consider the task of pixel-wise semantic segmentation given a small set of labeled training images. Among two of the most popular techniques to address this task are Random Forests (RF) and Neural Networks (NN). In this work, we explore the relationship between two special forms of these techniques: stacked RFs (namely Auto-context) and deep Convolutional Neural Networks (ConvNet). Our main contribution is to show that Auto-context can be mapped to a deep ConvNet with novel architecture, and thereby trained end-to-end. This mapping can be viewed as an intelligent initialization of a deep ConvNet, enabling training even in the face of very limited amounts of training data. We also demonstrate an approximate mapping back from the refined ConvNet to a second stacked RF, with improved performance over the original. We experimentally verify that these mappings outperform stacked RFs for two different applications in computer vision and biology: Kinect-based body part labeling from depth images, and somite segmentation in microscopy images of developing zebrafish.},
	doi = {10.5244/C.30.144},
	url = {http://arxiv.org/abs/1507.07583},
	author = {Richmond, David L. and Kainmueller, Dagmar and Yang, Michael Y. and Myers, Eugene W. and Carsten Rother}
}
@conference {Richmond2016a,
	title = {Mapping auto-context decision forests to deep convnets for semantic segmentation},
	booktitle = {British Machine Vision Conference 2016, BMVC 2016},
	volume = {2016-Septe},
	year = {2016},
	pages = {144.1{\textendash}144.12},
	abstract = {We consider the task of pixel-wise semantic segmentation given a small set of labeled training images. Among two of the most popular techniques to address this task are Random Forests (RF) and Neural Networks (NN). In this work, we explore the relationship between two special forms of these techniques: stacked RFs (namely Auto-context) and deep Convolutional Neural Networks (ConvNet). Our main contribution is to show that Auto-context can be mapped to a deep ConvNet with novel architecture, and thereby trained end-to-end. This mapping can be viewed as an intelligent initialization of a deep ConvNet, enabling training even in the face of very limited amounts of training data. We also demonstrate an approximate mapping back from the refined ConvNet to a second stacked RF, with improved performance over the original. We experimentally verify that these mappings outperform stacked RFs for two different applications in computer vision and biology: Kinect-based body part labeling from depth images, and somite segmentation in microscopy images of developing zebrafish.},
	doi = {10.5244/C.30.144},
	url = {https://github.com/BVLC/caffe/wiki/Model-Zoo\#fcn},
	author = {Richmond, David L and Kainmueller, Dagmar and Yang, Michael Y and Myers, Eugene W and Carsten Rother}
}
@conference {Richmond2016b,
	title = {Mapping auto-context decision forests to deep convnets for semantic segmentation},
	booktitle = {British Machine Vision Conference 2016, BMVC 2016},
	volume = {2016-Septe},
	year = {2016},
	pages = {144.1{\textendash}144.12},
	abstract = {We consider the task of pixel-wise semantic segmentation given a small set of labeled training images. Among two of the most popular techniques to address this task are Random Forests (RF) and Neural Networks (NN). In this work, we explore the relationship between two special forms of these techniques: stacked RFs (namely Auto-context) and deep Convolutional Neural Networks (ConvNet). Our main contribution is to show that Auto-context can be mapped to a deep ConvNet with novel architecture, and thereby trained end-to-end. This mapping can be viewed as an intelligent initialization of a deep ConvNet, enabling training even in the face of very limited amounts of training data. We also demonstrate an approximate mapping back from the refined ConvNet to a second stacked RF, with improved performance over the original. We experimentally verify that these mappings outperform stacked RFs for two different applications in computer vision and biology: Kinect-based body part labeling from depth images, and somite segmentation in microscopy images of developing zebrafish.},
	doi = {10.5244/C.30.144},
	author = {Richmond, David L and Kainmueller, Dagmar and Yang, Michael Y and Myers, Eugene W and Carsten Rother}
}
@conference {Kirillov2015,
	title = {M-best-diverse labelings for submodular energies and beyond},
	booktitle = {Advances in Neural Information Processing Systems},
	volume = {2015-Janua},
	year = {2015},
	pages = {613{\textendash}621},
	abstract = {We consider the problem of findingM best diverse solutions of energy minimization problems for graphical models. Contrary to the sequential method of Batra et al., which greedily finds one solution after another, we infer all M solutions jointly. It was shown recently that such jointly inferred labelings not only have smaller total energy but also qualitatively outperform the sequentially obtained ones. The only obstacle for using this new technique is the complexity of the corresponding inference problem, since it is considerably slower algorithm than the method of Batra et al. In this work we show that the joint inference of M best diverse solutions can be formulated as a submodular energy minimization if the original MAP-inference problem is submodular, hence fast inference techniques can be used. In addition to the theoretical results we provide practical algorithms that outperform the current state-of-the-art and can be used in both submodular and non-submodular case.},
	issn = {10495258},
	author = {Kirillov, Alexander and Schlesinger, Dmitrij and Vetrov, Dmitry and Carsten Rother and Savchynskyy, Bogdan}
}
@conference {Rother2010,
	title = {Minimizing sparse higher order energy functions of discrete variables},
	year = {2010},
	pages = {1382{\textendash}1389},
	abstract = {Higher order energy functions have the ability to encode high level structural dependencies between pixels, which have been shown to be extremely powerful for image labeling problems. Their use, however, is severely hampered in practice by the intractable complexity of representing and minimizing such functions. We observed that higher order functions encountered in computer vision are very often {\textquotedblleft}sparse{\textquotedblright}, i.e. many labelings of a higher order clique are equally unlikely and hence have the same high cost. In this paper, we address the problem of minimizing such sparse higher order energy functions. Our method works by transforming the problem into an equivalent quadratic function minimization problem. The resulting quadratic function can be minimized using popular message passing or graph cut based algorithms for MAP inference. Although this is primarily a theoretical paper, it also shows how higher order functions can be used to obtain impressive results for the binary texture restoration problem.},
	doi = {10.1109/cvpr.2009.5206739},
	author = {Carsten Rother and Kohli, Pushmeet and Wei Feng and Jiaya Jia}
}
@booklet {Kolmogorov2007,
	title = {Minimizing nonsubmodular functions with graph cuts - A review},
	howpublished = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {29},
	number = {7},
	year = {2007},
	pages = {1274{\textendash}1279},
	abstract = {Optimization techniques based on graph cuts have become a standard tool for many vision applications. These techniques allow to minimize efficiently certain energy functions corresponding to pairwise Markov Random Fields (MRFs). Currently, there is an accepted view within the computer vision community that graph cuts can only be used for optimizing a limited class of MRF energies (e.g., submodular functions). In this survey, we review some results that show that graph cuts can be applied to a much larger class of energy functions (in particular, nonsubmodular functions). While these results are well-known in the optimization community, to our knowledge they were not used in the context of computer vision and MRF optimization. We demonstrate the relevance of these results to vision on the problem of binary texture restoration. {\textcopyright} 2007 IEEE.},
	keywords = {energy minimization, Markov random fields, Min cut/max flow, Quadratic pseudo-Boolean optimization, Texture restoration},
	issn = {01628828},
	doi = {10.1109/TPAMI.2007.1031},
	author = {Kolmogorov, Vladimir and Carsten Rother}
}
@phdthesis {Rother2003,
	title = {Multi-View Reconstruction and Camera Recovery using a Real or Virtual Reference Plane},
	year = {2003},
	type = {phd},
	abstract = {Reconstructing a 3-dimensional scene from a set of 2-dimensional images is a fundamental problem in computer vision. A systemcapable of performing this task can be used in many applications in robotics, architecture, archaeology, biometrics, human computer interaction and the movie and entertainment industry. Most existing reconstruction approaches exploit one source of information to tackle the problem. This is the motion of the camera, the 2D images are taken from different viewpoints.We exploit an additional information source, the reference plane,whichmakes it possible to reconstruct difficult scenes where other methods fail. A real scene plane may serve as the reference plane. Furthermore, there are many alternative techniques to obtain virtual reference planes. For instance, orthogonal directions in the scene provide a virtual reference plane, the plane at infinity, or images taken with a parallel projection camera. A collection of known and novel reference plane scenarios is presented in this thesis. The main contribution of the thesis is a novel multi-view reconstruction approach us- ing a reference plane. The technique is applicable to three different feature types, points, lines and planes. The novelty of our approach is that all cameras and all features (off the reference plane) are reconstructed simultaneously from a single linear system of im- age measurements. It is based on the novel observation that cameras and features have a linear relationship if a reference plane is known. In the absence of a reference plane, this relationship is non-linear. Thus many previous methods must reconstruct features and cameras sequentially. Another class of methods, popular in the literature, is factorization, but, in contrast to our approach, this has the serious practical drawback that all features are required to be visible in all views. Extensive experiments show that our approach is superior to all previously suggested reference plane and non-reference plane methods for difficult reference plane scenarios. Furthermore, the thesis studies scenes which do not have a unique reconstruction, so- called critical configurations. It is proven that in the presence of a reference plane the set of critical configurations is small. Finally, the thesis introduces a complete, automatic multi-view reconstruction system based on the reference plane approach. The input data is a set of images and the output a 3D point reconstruction together with the corresponding cameras.},
	isbn = {9172834226},
	url = {http://www.google.com/url?sa=t\&rct=j\&q=\&esrc=s\&source=web\&cd=4\&cad=rja\&uact=8\&ved=0CDUQFjAD\&url=http\%3A\%2F\%2Fwww.nada.kth.se\%2Futbildning\%2Fforsk.utb\%2Favhandlingar\%2Fdokt\%2Frother.pdf\&ei=AyX_VPKmIomeNqeOgpgL\&usg=AFQjCNHCmc75P5EHYWLtBUaHtUAs4yOnJw\&bvm=bv.},
	author = {Carsten Rother}
}
