@proceedings {Haller2020,
	title = {A Primal-Dual Solver for Large-Scale Tracking-by-Assignment},
	year = {2020},
	abstract = {We propose a fast approximate solver for the combinatorial problem known as tracking-by-assignment, which we apply to cell tracking. The latter plays a key role in discovery in many life sciences, especially in cell and developmental biology. So far, in the most general setting this problem was addressed by off-the-shelf solvers like Gurobi, whose run time and memory requirements rapidly grow with the size of the input. In contrast, for our method this growth is nearly linear. Our contribution consists of a new (1) de-composable compact representation of the problem; (2) dual block-coordinate ascent method for optimizing the decomposition-based dual; and (3) primal heuristics that reconstructs a feasible integer solution based on the dual information. Compared to solving the problem with Gurobi, we observe an up to 60 times speed-up, while reducing the memory footprint significantly. We demonstrate the efficacy of our method on real-world tracking problems.},
	author = {Stefan Haller and Prakash, Mangal and Hutschenreiter, Lisa and Pietzsch, Tobias and Carsten Rother and Jug, Florian and Swoboda, Paul and Savchynskyy, Bogdan}
}
@conference {Tourani2020,
	title = {Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization},
	booktitle = {AISTATS 2020},
	year = {2020},
	abstract = {We consider the maximum-a-posteriori inference problem in discrete graphical models and study solvers based on the dual block-coordinate ascent rule. We map all existing solvers in a single framework, allowing for a better understanding of their design principles. We theoretically show that some block-optimizing updates are sub-optimal and how to strictly improve them. On a wide range of problem instances of varying graph connec-tivity, we study the performance of existing solvers as well as new variants that can be obtained within the framework. As a result of this exploration we build a new state-of-the art solver, performing uniformly better on the whole range of test instances.},
	url = {https://gitlab.com/},
	author = {Tourani, Siddharth and Shekhovtsov, Alexander and Carsten Rother and Savchynskyy, Bogdan}
}
@article {Savchynskyy2019,
	title = {Discrete Graphical Models {\textemdash} An Optimization Perspective},
	journal = {Foundations and Trends{\textregistered} in Computer Graphics and Vision},
	volume = {11},
	number = {3-4},
	year = {2019},
	pages = {160{\textendash}429},
	publisher = {Now Publishers},
	abstract = {This monograph is about combinatorial optimization. More precisely, about a special class of combinatorial problems known as energy minimization or maximum a posteriori (MAP) inference in graphical models, closely related to weighted and valued constraint satisfaction problems and having tight connections to Markov random fields and quadratic pseudo-boolean optimization. What distinguishes this monograph from a number of other monographs on graphical models is its focus: It considers graphical models, or, more precisely, MAP-inference for graphical models, purely as a combinatorial optimization problem. Modeling, applications, probabilistic interpretations and many other aspects are either ignored here or find their place in examples and remarks only. Combinatorial optimization as a field is largely based on five fundamental topics: (i) integer linear programming and polyhedral optimization; (ii) totally unimodular matrices and the class of min-cost-flow problems; (iii) Lagrange decompositions and relaxations; (iv) dynamic programming and (v) submodularity, matroids and greedy algorithms. Each of these topics found its place in this monograph, although to a variable extent. The covering of each respective topic reflects its importance for the considered MAP-inference problem. Since optimization is the primary topic of this monograph, we mostly stick to the terminology widely used in optimization and where it was possible we tried to avoid the graphical models community-specific technical terms. The latter differ from sub-community to sub-community and, in our view, significantly complicate the information exchange between them. The same holds also for the presentation of material in this monograph. If there is a choice when introducing mathematical constructs or proving statements, we prefer more general mathematical tools applicable in the whole field of operations research rather than to stick to graphical modelspecific constructions. We additionally provide the graphical model-specific constructions if it turns out to be easier than the more general one. This way of presentation has two advantages. A reader familiar with a more general technique can grasp the new material faster. On the other hand, the monograph may serve as an introduction to combinatorial optimization for readers unfamiliar with this subject. To make the monograph even more suitable for both categories of readers we explicitly separate the mathematical optimization background chapters from those specific to graphical models. We believe, therefore, that the monograph can be useful for undergraduate and graduate students studying optimization or graphical models, as well as for experts in optimization who want to have a look into graphical models. Moreover, we believe that even experts in graphical models can find new views on the known facts as well as a novel presentation of less known results in the monograph. These are for instance (i) a simple and general proof of equivalence of different acyclic Lagrange decompositions of a graphical model; (ii) a general scheme for analyzing convergence of dual block-coordinate descent methods; (iii) a short and self-contained analysis of a linear programming relaxation for binary graphical models, its persistency properties and its relation to quadratic pseudo-boolean optimization. The present monograph is based on lectures given to undergraduate students of Technical University of Dresden and Heidelberg University. The selection of material is done in a way that it may serve as a basis for a semester course.},
	issn = {1572-2740},
	doi = {10.1561/0600000084},
	author = {Savchynskyy, Bogdan}
}
@article {Arnab2018,
	title = {Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation},
	journal = {Cvpr},
	volume = {XX},
	number = {Xx},
	year = {2018},
	pages = {1{\textendash}15},
	abstract = {{\textemdash}Semantic Segmentation is the task of labelling every pixel in an image with a pre-defined object category. It has numer-ous applications in scenarios where the detailed understanding of an image is required, such as in autonomous vehicles and medical diagnosis. This problem has traditionally been solved with probabilistic models known as Conditional Random Fields (CRFs) due to their ability to model the relationships between the pixels being predicted. However, Deep Neural Networks (DNNs) have recently been shown to excel at a wide range of computer vision problems due to their ability to learn rich feature representations automatically from data, as opposed to traditional hand-crafted features. The idea of combining CRFs and DNNs have achieved state-of-the-art results in a number of domains. We review the literature on combining the modelling power of CRFs with the representation-learning ability of DNNs, ranging from early work that combines these two techniques as independent stages of a common pipeline to recent approaches that embed inference of probabilistic models directly in the neural network itself. Finally, we summarise future research directions.},
	keywords = {conditional random fields, deep learning, seman-},
	url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.308.8889\&rep=rep1\&type=pdf\%0Ahttp://dx.doi.org/10.1109/CVPR.2012.6248050},
	author = {Arnab, Anurag and Zheng, Shuai and Jayasumana, Sadeep and Romera-paredes, Bernardino and Kirillov, Alexander and Savchynskyy, Bogdan and Carsten Rother and Kahl, Fredrik and Torr, Philip}
}
@article {Shekhovtsov2018,
	title = {Maximum Persistency via Iterative Relaxed Inference in Graphical Models},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {40},
	number = {7},
	year = {2018},
	pages = {1668{\textendash}1682},
	abstract = {We consider the NP-hard problem of MAP-inference for undirected discrete graphical models. We propose a polynomial time and practically efficient algorithm for finding a part of its optimal solution. Specifically, our algorithm marks some labels of the considered graphical model either as (i) optimal, meaning that they belong to all optimal solutions of the inference problem; (ii) non-optimal if they provably do not belong to any solution. With access to an exact solver of a linear programming relaxation to the MAP-inference problem, our algorithm marks the maximal possible (in a specified sense) number of labels. We also present a version of the algorithm, which has access to a suboptimal dual solver only and still can ensure the (non-)optimality for the marked labels, although the overall number of the marked labels may decrease. We propose an efficient implementation, which runs in time comparable to a single run of a suboptimal dual solver. Our method is well-scalable and shows state-of-the-art results on computational benchmarks from machine learning and computer vision.},
	keywords = {discrete optimization, energy minimization, graphical models, LP relaxation, partial optimality, persistency, WCSP},
	issn = {01628828},
	doi = {10.1109/TPAMI.2017.2730884},
	url = {http://www.icg.tugraz.at/},
	author = {Shekhovtsov, Alexander and Swoboda, Paul and Savchynskyy, Bogdan}
}
@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 {Michel2017,
	title = {Global hypothesis generation for 6D object pose estimation},
	booktitle = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
	volume = {2017-Janua},
	year = {2017},
	month = {dec},
	pages = {115{\textendash}124},
	abstract = {This paper addresses the task of estimating the 6D pose of a known 3D object from a single RGB-D image. Most modern approaches solve this task in three steps: i) Compute local features; ii) Generate a pool of pose-hypotheses; iii) Select and refine a pose from the pool. This work focuses on the second step. While all existing approaches generate the hypotheses pool via local reasoning, e.g. RANSAC or Hough-voting, we are the first to show that global reasoning is beneficial at this stage. In particular, we formulate a novel fully-connected Conditional Random Field (CRF) that outputs a very small number of pose-hypotheses. Despite the potential functions of the CRF being non-Gaussian, we give a new and efficient two-step optimization procedure, with some guarantees for optimality. We utilize our global hypotheses generation procedure to produce results that exceed state-of-the-art for the challenging "Occluded Object Dataset".},
	isbn = {9781538604571},
	doi = {10.1109/CVPR.2017.20},
	url = {http://arxiv.org/abs/1612.02287},
	author = {Michel, Frank and Kirillov, Alexander and Brachmann, Eric and Krull, Alexander and Gumhold, Stefan and Savchynskyy, Bogdan and Carsten Rother}
}
@conference {Kirillov2017a,
	title = {InstanceCut: From edges to instances with MultiCut},
	booktitle = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
	volume = {2017-Janua},
	year = {2017},
	pages = {7322{\textendash}7331},
	abstract = {This work addresses the task of instance-aware semantic segmentation. Our key motivation is to design a simple method with a new modelling-paradigm, which therefore has a different trade-off between advantages and disadvantages compared to known approaches.Our approach, we term InstanceCut, represents the problem by two output modalities: (i) an instance-agnostic semantic segmentation and (ii) all instance-boundaries. The former is computed from a standard convolutional neural network for semantic segmentation, and the latter is derived from a new instanceaware edge detection model. To reason globally about the optimal partitioning of an image into instances, we combine these two modalities into a novel MultiCut formulation. We evaluate our approach on the challenging CityScapes dataset. Despite the conceptual simplicity of our approach, we achieve the best result among all published methods, and perform particularly well for rare object classes.},
	isbn = {9781538604571},
	doi = {10.1109/CVPR.2017.774},
	author = {Kirillov, Alexander and Levinkov, Evgeny and Bj{\"o}rn Andres and Savchynskyy, Bogdan and Carsten Rother}
}
@article {Kappes2016,
	title = {Multicuts and Perturb \& MAP for Probabilistic Graph Clustering},
	journal = {Journal of Mathematical Imaging and Vision},
	volume = {56},
	number = {2},
	year = {2016},
	month = {jan},
	pages = {221{\textendash}237},
	abstract = {We present a probabilistic graphical model formulation for the graph clustering problem. This enables us to locally represent uncertainty of image partitions by approximate marginal distributions in a mathematically substantiated way, and to rectify local data term cues so as to close contours and to obtain valid partitions. We exploit recent progress on globally optimal MAP inference by integer programming and on perturbation-based approximations of the log-partition function, in order to sample clusterings and to estimate marginal distributions of node-pairs both more accurately and more efficiently than state-of-the-art methods. Our approach works for any graphically represented problem instance. This is demonstrated for image segmentation and social network cluster analysis. Our mathematical ansatz should be relevant also for other combinatorial problems.},
	keywords = {Correlation clustering, graphical models, Multicut, Perturb and MAP},
	issn = {15737683},
	doi = {10.1007/s10851-016-0659-3},
	url = {http://arxiv.org/abs/1601.02088},
	author = {Kappes, Jorg Hendrik and Swoboda, Paul and Savchynskyy, Bogdan and Hazan, Tamir and Christoph Schn{\"o}rr}
}
@article {Swoboda2016,
	title = {Partial Optimality by Pruning for MAP-Inference with General Graphical Models},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {38},
	number = {7},
	year = {2016},
	month = {jul},
	pages = {1370{\textendash}1382},
	publisher = {IEEE Computer Society},
	abstract = {We consider the energy minimization problem for undirected graphical models, also known as MAP-inference problem for Markov random fields which is NP-hard in general. We propose a novel polynomial time algorithm to obtain a part of its optimal non-relaxed integral solution. Our algorithm is initialized with variables taking integral values in the solution of a convex relaxation of the MAP-inference problem and iteratively prunes those, which do not satisfy our criterion for partial optimality. We show that our pruning strategy is in a certain sense theoretically optimal. Also empirically our method outperforms previous approaches in terms of the number of persistently labelled variables. The method is very general, as it is applicable to models with arbitrary factors of an arbitrary order and can employ any solver for the considered relaxed problem. Our method{\textquoteright}s runtime is determined by the runtime of the convex relaxation solver for the MAP-inference problem.},
	keywords = {energy minimization, Local polytope, MAP-inference, Markov random fields, partial optimality, persistency},
	issn = {01628828},
	doi = {10.1109/TPAMI.2015.2484327},
	author = {Swoboda, Paul and Shekhovtsov, Alexander and Kappes, Jorg Hendrik and Christoph Schn{\"o}rr and Savchynskyy, Bogdan}
}
@article {Kappes2015b,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {International Journal of Computer Vision},
	volume = {115},
	number = {2},
	year = {2015},
	pages = {155{\textendash}184},
	abstract = {Szeliski et al. published an influential study in 2006 on energy minimization methods for Markov random fields. This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenomenal success of random field models means that the kinds of inference problems that have to be solved changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of more than 27 state-of-the-art optimization techniques on a corpus of 2453 energy minimization instances from diverse applications in computer vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.},
	keywords = {Benchmark, Combinatorial optimization, Discrete graphical models},
	issn = {15731405},
	doi = {10.1007/s11263-015-0809-x},
	author = {Kappes, J{\"o}rg H and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Kausler, Bernhard X and Kr{\"o}ger, Thorben and Lellmann, Jan and Komodakis, Nikos and Savchynskyy, Bogdan and Carsten Rother}
}
@article {Kappes2015a,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {International Journal of Computer Vision},
	volume = {115},
	number = {2},
	year = {2015},
	pages = {155{\textendash}184},
	abstract = {Szeliski et al. published an influential study in 2006 on energy minimization methods for Markov random fields. This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenomenal success of random field models means that the kinds of inference problems that have to be solved changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of more than 27 state-of-the-art optimization techniques on a corpus of 2453 energy minimization instances from diverse applications in computer vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.},
	keywords = {Benchmark, Combinatorial optimization, Discrete graphical models},
	isbn = {25164671.25},
	issn = {15731405},
	doi = {10.1007/s11263-015-0809-x},
	author = {Kappes, J{\"o}rg H and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Kausler, Bernhard X and Kr{\"o}ger, Thorben and Lellmann, Jan and Komodakis, Nikos and Savchynskyy, Bogdan and Carsten Rother}
}
@article {Kappes2015,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {International Journal of Computer Vision},
	volume = {115},
	number = {2},
	year = {2015},
	pages = {155{\textendash}184},
	abstract = {Szeliski et al. published an influential study in 2006 on energy minimization methods for Markov random fields. This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenomenal success of random field models means that the kinds of inference problems that have to be solved changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of more than 27 state-of-the-art optimization techniques on a corpus of 2453 energy minimization instances from diverse applications in computer vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.},
	keywords = {Benchmark, Combinatorial optimization, Discrete graphical models},
	issn = {15731405},
	doi = {10.1007/s11263-015-0809-x},
	url = {http://hci.iwr.uni-heidelberg.de/opengm2/},
	author = {Kappes, J{\"o}rg H and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Kausler, Bernhard X and Kr{\"o}ger, Thorben and Lellmann, Jan and Komodakis, Nikos and Savchynskyy, Bogdan and Carsten Rother}
}
@conference {Kirillov2015a,
	title = {Inferring M-best diverse labelings in a single one},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2015 Inter},
	year = {2015},
	pages = {1814{\textendash}1822},
	abstract = {We consider the task of finding M-best diverse solutions in a graphical model. In a previous work by Batra et al. an algorithmic approach for finding such solutions was proposed, and its usefulness was shown in numerous applications. Contrary to previous work we propose a novel formulation of the problem in form of a single energy minimization problem in a specially constructed graphical model. We show that the method of Batra et al. can be considered as a greedy approximate algorithm for our model, whereas we introduce an efficient specialized optimization technique for it, based on alpha-expansion. We evaluate our method on two application scenarios, interactive and semantic image segmentation, with binary and multiple labels. In both cases we achieve considerably better error rates than state-of-the art diversity methods. Furthermore, we empirically discover that in the binary label case we were able to reach global optimality for all test instances.},
	isbn = {9781467383912},
	issn = {15505499},
	doi = {10.1109/ICCV.2015.211},
	author = {Kirillov, Alexander and Savchynskyy, Bogdan and Schlesinger, Dmitrij and Vetrov, Dmitry 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 {Kappes2015c,
	title = {Probabilistic correlation clustering and image partitioning using perturbed Multicuts},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {9087},
	year = {2015},
	pages = {231{\textendash}242},
	abstract = {We exploit recent progress on globally optimal MAP inference by integer programming and perturbation-based approximations of the log-partition function. This enables to locally represent uncertainty of image partitions by approximate marginal distributions in a mathematically substantiated way, and to rectify local data term cues so as to close contours and to obtain valid partitions. Our approach works for any graphically represented problem instance of correlation clustering, which is demonstrated by an additional social network example.},
	keywords = {Correlation clustering, Multicut, Perturb and MAP},
	isbn = {9783319184609},
	issn = {16113349},
	doi = {10.1007/978-3-319-18461-6_19},
	author = {Kappes, Jorg Hendrik and Swoboda, Paul and Savchynskyy, Bogdan and Hazan, Tamir and Christoph Schn{\"o}rr}
}
@article {kappes-1014-bench-arxiv,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {CoRR},
	volume = {abs/1404.0533},
	year = {2014},
	url = {http://hci.iwr.uni-heidelberg.de/opengm2/},
	author = {J{\"o}rg H. Kappes and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Bernhard X. Kausler and Thorben Kr{\"o}ger and Lellmann, Jan and Komodakis, Nikos and Savchynskyy, Bogdan and Carsten Rother}
}
@conference {Swoboda-2014,
	title = {Partial Optimality by Pruning for MAP-inference with General Graphical Models},
	booktitle = {IEEE Conference on Computer Vision and Pattern Recognition 2014},
	year = {2014},
	author = {Swoboda, Paul and Savchynskyy, Bogdan and Kappes, J{\"o}rg H. and Christoph Schn{\"o}rr}
}
@conference {Swoboda-2014,
	title = {Partial Optimality by Pruning for MAP-inference with General Graphical Models},
	booktitle = {IEEE Conference on Computer Vision and Pattern Recognition 2014},
	year = {2014},
	author = {Swoboda, Paul and Savchynskyy, Bogdan and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@conference {SavchynskyyNIPS2013,
	title = {Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation},
	booktitle = {NIPS},
	year = {2013},
	note = {Accepted},
	author = {Savchynskyy, Bogdan and J{\"o}rg H. Kappes and Swoboda, Paul and Christoph Schn{\"o}rr}
}
@conference {SavchynskyyNIPS2013,
	title = {Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation},
	booktitle = {NIPS},
	year = {2013},
	note = {Accepted},
	author = {Savchynskyy, Bogdan and Kappes, Jorg Hendrik and Swoboda, Paul and Christoph Schn{\"o}rr}
}
@conference {SwobodaSSVM13,
	title = {Partial Optimality via Iterative Pruning for the Potts Model},
	booktitle = {Scale Space and Variational Methods (SSVM 2013)},
	year = {2013},
	author = {Swoboda, Paul and Savchynskyy, Bogdan and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@conference {Kappes2012,
	title = {A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation},
	booktitle = {CVPR},
	year = {2012},
	note = {in press},
	author = {J{\"o}rg H. Kappes and Savchynskyy, Bogdan and Christoph Schn{\"o}rr}
}
@conference {SavchynskyyUAI2012,
	title = {Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing},
	booktitle = {UAI 2012},
	year = {2012},
	note = {in press},
	author = {Savchynskyy, Bogdan and Schmidt, Stefan and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@conference {Schmidt11,
	title = {Evaluation of a First-Order Primal-Dual Algorithm for MRF Energy Minimization},
	booktitle = {EMMCVPR},
	volume = {6819},
	year = {2011},
	pages = {89-103},
	publisher = {Springer},
	organization = {Springer},
	author = {Schmidt, Stefan and Savchynskyy, Bogdan and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
