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G
Savchynskyy, B, Kappes, J H, Swoboda, P and Schnörr, C (2013). Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation. NIPSPDF icon Technical Report (499.17 KB)
Savchynskyy, B, Kappes, J Hendrik, Swoboda, P and Schnörr, C (2013). Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation. NIPS
M
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/
Kappes, J Hendrik, Swoboda, P, Savchynskyy, B, Hazan, T and Schnörr, 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
P
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2014). Partial Optimality by Pruning for MAP-inference with General Graphical Models. IEEE Conference on Computer Vision and Pattern Recognition 2014PDF icon Technical Report (703.34 KB)
Swoboda, P, Shekhovtsov, A, Kappes, J Hendrik, Schnörr, 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
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2014). Partial Optimality by Pruning for MAP-inference with General Graphical Models. IEEE Conference on Computer Vision and Pattern Recognition 2014
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2013). Partial Optimality via Iterative Pruning for the Potts Model. Scale Space and Variational Methods (SSVM 2013)PDF icon Technical Report (159.71 KB)
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 2020PDF icon PDF (1.04 MB)
Kappes, J Hendrik, Swoboda, P, Savchynskyy, B, Hazan, T and Schnörr, 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
V
Swoboda, P and Schnörr, C (2013). Variational Image Segmentation and Cosegmentation with the Wasserstein Distance. Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer. 8081 321--334PDF icon Technical Report (8.06 MB)