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 Haller, S, Prakash, M, Hutschenreiter, L, Pietzsch, T, Rother, C, Jug, F, Swoboda, P and Savchynskyy, B (2020). A Primal-Dual Solver for Large-Scale Tracking-by-Assignment. AISTATS 2020\par \par  Tourani, S, Shekhovtsov, A, Rother, C and Savchynskyy, B (2020). Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization. AISTATS 2020. https://gitlab.com/\par \par  Savchynskyy, B (2019). Discrete Graphical Models ? An Optimization Perspective. Foundations and Trends\'ae in Computer Graphics and Vision. Now Publishers. 11 160?429\par \par  Arnab, A, Zheng, S, Jayasumana, S, Romera-paredes, B, Kirillov, A, Savchynskyy, B, Rother, C, Kahl, F and Torr, P (2018). Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation. Cvpr. XX 1?15. 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\par \par  Shekhovtsov, A, Swoboda, P and Savchynskyy, B (2018). Maximum Persistency via Iterative Relaxed Inference in Graphical Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40 1668?1682. http://www.icg.tugraz.at/\par \par  Tourani, S, Shekhovtsov, A, Rother, C and Savchynskyy, B (2018). MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11208 LNCS 264?281\par \par  Michel, F, Kirillov, A, Brachmann, E, Krull, A, Gumhold, S, Savchynskyy, B and Rother, C (2017). Global hypothesis generation for 6D object pose estimation. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017-Janua 115?124. http://arxiv.org/abs/1612.02287\par \par  Kirillov, A, Levinkov, E, Andres, B, Savchynskyy, B and Rother, C (2017). InstanceCut: From edges to instances with MultiCut. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017-Janua 7322?7331\par \par  Kappes, J Hendrik, Swoboda, P, Savchynskyy, B, Hazan, T and Schn\'f6rr, C (2016). Multicuts and Perturb & MAP for Probabilistic Graph Clustering. Journal of Mathematical Imaging and Vision. 56 221?237. http://arxiv.org/abs/1601.02088\par \par  Swoboda, P, Shekhovtsov, A, Kappes, J Hendrik, Schn\'f6rr, C and Savchynskyy, B (2016). Partial Optimality by Pruning for MAP-Inference with General Graphical Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE Computer Society. 38 1370?1382\par \par  Kappes, J H, Andres, B, Hamprecht, F A, Schn\'f6rr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kr\'f6ger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. International Journal of Computer Vision. 115 155?184\par \par  Kappes, J H, Andres, B, Hamprecht, F A, Schn\'f6rr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kr\'f6ger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. International Journal of Computer Vision. 115 155?184\par \par  Kappes, J H, Andres, B, Hamprecht, F A, Schn\'f6rr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kr\'f6ger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. International Journal of Computer Vision. 115 155?184. http://hci.iwr.uni-heidelberg.de/opengm2/\par \par  Kirillov, A, Savchynskyy, B, Schlesinger, D, Vetrov, D and Rother, C (2015). Inferring M-best diverse labelings in a single one. Proceedings of the IEEE International Conference on Computer Vision. 2015 Inter 1814?1822\par \par  Kirillov, A, Schlesinger, D, Vetrov, D, Rother, C and Savchynskyy, B (2015). M-best-diverse labelings for submodular energies and beyond. Advances in Neural Information Processing Systems. 2015-Janua 613?621\par \par  Kappes, J Hendrik, Swoboda, P, Savchynskyy, B, Hazan, T and Schn\'f6rr, C (2015). Probabilistic correlation clustering and image partitioning using perturbed Multicuts. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9087 231?242\par \par  Kappes, J H, Andres, B, Hamprecht, F A, Schn\'f6rr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kr\'f6ger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2014). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. CoRR. abs/1404.0533. http://hci.iwr.uni-heidelberg.de/opengm2/\par \par  Swoboda, P, Savchynskyy, B, Kappes, J H and Schn\'f6rr, C (2014). Partial Optimality by Pruning for MAP-inference with General Graphical Models. IEEE Conference on Computer Vision and Pattern Recognition 2014\par \par  Swoboda, P, Savchynskyy, B, Kappes, J H and Schn\'f6rr, C (2014). Partial Optimality by Pruning for MAP-inference with General Graphical Models. IEEE Conference on Computer Vision and Pattern Recognition 2014\par \par  Savchynskyy, B, Kappes, J Hendrik, Swoboda, P and Schn\'f6rr, C (2013). Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation. NIPS\par \par  Savchynskyy, B, Kappes, J H, Swoboda, P and Schn\'f6rr, C (2013). Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation. NIPS\par \par  Swoboda, P, Savchynskyy, B, Kappes, J H and Schn\'f6rr, C (2013). Partial Optimality via Iterative Pruning for the Potts Model. Scale Space and Variational Methods (SSVM 2013)\par \par  Kappes, J H, Savchynskyy, B and Schn\'f6rr, C (2012). A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation. CVPR\par \par  Savchynskyy, B, Schmidt, S, Kappes, J H and Schn\'f6rr, C (2012). Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing. UAI 2012\par \par  Schmidt, S, Savchynskyy, B, Kappes, J H and Schn\'f6rr, C (2011). Evaluation of a First-Order Primal-Dual Algorithm for MRF Energy Minimization. EMMCVPR. Springer. 6819 89-103\par \par }