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B
J. H. Kappes, Savchynskyy, B., and Schnörr, C., “A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation”, in CVPR. Proceedings, 2012, pp. 1688-1695.
C
J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, CoRR, 2014.
J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, International Journal of Computer Vision, pp. 1-30, 2015.PDF icon Technical Report (1.5 MB)
J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Kröger, T., Lellmann, J., Komodakis, N., Savchynskyy, B., and Rother, C., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, Int.~J.~Comp.~Vision, 2015.PDF icon Technical Report (5.12 MB)
D
P. Swoboda, Kuske, J., and Savchynskyy, B., “A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems”, arXiv, preprint, 2016.
E
B. Savchynskyy, Schmidt, S., Kappes, J. H., and Schnörr, C., “Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing”, UAI. Proceedings, pp. 746-755, 2012.
S. Schmidt, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Evaluation of a First-Order Primal-Dual Algorithm for MRF Energy Minimization”, in EMMCVPR 2011, 2011, vol. 6819, pp. 89-103.
G
B. Savchynskyy and Schmidt, S., “Getting Feasible Variable Estimates From Infeasible Ones: MRF Local Polytope Study”, in Workshop on Inference for Probabilistic Graphical Models at ICCV. Proceedings, 2013.
B. Savchynskyy and Schmidt, S., “Getting Feasible Variable Estimates From Infeasible Ones: MRF Local Polytope Study”, arXiv:1210.4081, 2012.
B. Savchynskyy, Kappes, J. H., Swoboda, P., and Schnörr, C., “Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation”, in NIPS. Proceedings, 2013, pp. 1950-1958.
M
J. H. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Multicuts and Perturb & MAP for Probabilistic Graph Clustering”, J. Math. Imag. Vision, vol. 56, pp. 221–237, 2016.
P
P. Swoboda, Shekhovtsov, A., Kappes, J. H., Schnörr, C., and Savchynskyy, B., “Partial Optimality by Pruning for MAP-Inference with General Graphical Models”, IEEE Trans. Patt. Anal. Mach. Intell., vol. 38, pp. 1370–1382, 2016.
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality by Pruning for MAP-inference with General GraphicalModels”, CVPR. Proceedings. pp. 1170-1177, 2014.
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality via Iterative Pruning for the Potts Model”, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision SSVM, 2013, pp. 477-488.
P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Persistency by Pruning for General Graphical Models”, in submitted to NIPS 2013., 2013.
J. H. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts”, in Proc.~SSVM, 2015.PDF icon Technical Report (1.1 MB)
J. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts”, in Proc. SSVM, 2015.
S
B. Savchynskyy, Kappes, J. H., Schmidt, S., and Schnörr, C., “A Study of Nesterov's Scheme for Lagrangian Decomposition and MAP Labeling”, in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2011.PDF icon Technical Report (408.99 KB)
B. Savchynskyy, Kappes, J. H., Schmidt, S., and Schnörr, C., “A Study of Nesterov's Scheme for Lagrangian Decomposition and MAP Labeling”, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), accepted as oral presentation, pp. 1817 - 1823, 2011.
B. Savchynskyy, Kappes, J. H., Schmidt, S., and Schnörr, C., “A Study of Nesterov's Scheme for Lagrangian Decomposition and MAP Labeling”, in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2011.