Instancecut: from edges to instances with multicut A Kirillov, E Levinkov, B Andres, B Savchynskyy, C Rother Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 319 | 2017 |
A comparative study of modern inference techniques for structured discrete energy minimization problems JH Kappes, B Andres, FA Hamprecht, C Schnörr, S Nowozin, D Batra, ... International Journal of Computer Vision 115, 155-184, 2015 | 243 | 2015 |
Conditional random fields meet deep neural networks for semantic segmentation: Combining probabilistic graphical models with deep learning for structured prediction A Arnab, S Zheng, S Jayasumana, B Romera-Paredes, M Larsson, ... IEEE Signal Processing Magazine 35 (1), 37-52, 2018 | 173 | 2018 |
Global hypothesis generation for 6D object pose estimation F Michel, A Kirillov, E Brachmann, A Krull, S Gumhold, B Savchynskyy, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 155 | 2017 |
A study of Nesterov's scheme for Lagrangian decomposition and MAP labeling B Savchynskyy, J Kappes, S Schmidt, C Schnörr Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on …, 2011 | 75 | 2011 |
A bundle approach to efficient MAP-inference by Lagrangian relaxation JH Kappes, B Savchynskyy, C Schnörr Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on …, 2012 | 71 | 2012 |
Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing B Savchynskyy, S Schmidt, J Kappes, C Schnörr Uncertainty in Artificial Intelligence, UAI-2012, 746-755, 2012 | 67 | 2012 |
A study of lagrangean decompositions and dual ascent solvers for graph matching P Swoboda, C Rother, H Abu Alhaija, D Kainmuller, B Savchynskyy Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 65 | 2017 |
Inferring M-best diverse labelings in a single one A Kirillov, B Savchynskyy, D Schlesinger, D Vetrov, C Rother Proceedings of the IEEE International Conference on Computer Vision, 1814-1822, 2015 | 50 | 2015 |
Discrete graphical models—an optimization perspective B Savchynskyy Foundations and Trends® in Computer Graphics and Vision 11 (3-4), 160-429, 2019 | 41 | 2019 |
Partial optimality by pruning for MAP-inference with general graphical models P Swoboda, B Savchynskyy, JH Kappes, C Schnörr CVPR, 2014 | 37 | 2014 |
Discriminative learning of max-sum classifiers V Franc, B Savchynskyy The Journal of Machine Learning Research 9, 67-104, 2008 | 37 | 2008 |
A dual ascent framework for Lagrangean decomposition of combinatorial problems P Swoboda, J Kuske, B Savchynskyy Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 32 | 2017 |
Maximum persistency via iterative relaxed inference with graphical models A Shekhovtsov, P Swoboda, B Savchynskyy Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015 | 32 | 2015 |
Global MAP-optimality by shrinking the combinatorial search area with convex relaxation B Savchynskyy, JH Kappes, P Swoboda, C Schnörr Advances in Neural Information Processing Systems 26, 2013 | 32 | 2013 |
Towards globally optimal normal orientations for large point clouds N Schertler, B Savchynskyy, S Gumhold Computer Graphics Forum 36 (1), 197-208, 2017 | 31 | 2017 |
M-best-diverse labelings for submodular energies and beyond A Kirillov, D Shlezinger, DP Vetrov, C Rother, B Savchynskyy Advances in Neural Information Processing Systems 28, 2015 | 26 | 2015 |
Partial optimality via iterative pruning for the Potts model P Swoboda, B Savchynskyy, J Kappes, C Schnörr Scale Space and Variational Methods in Computer Vision: 4th International …, 2013 | 23 | 2013 |
MPLP++: Fast, parallel dual block-coordinate ascent for dense graphical models S Tourani, A Shekhovtsov, C Rother, B Savchynskyy Proceedings of the European Conference on Computer Vision (ECCV), 251-267, 2018 | 22 | 2018 |
A comparative study of graph matching algorithms in computer vision S Haller, L Feineis, L Hutschenreiter, F Bernard, C Rother, D Kainmüller, ... European Conference on Computer Vision, 636-653, 2022 | 20 | 2022 |