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Savchynskyy, B, Kappes, J H, Schmidt, S and Schnörr, C (2011). 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. 1817 - 1823
Savchynskyy, B, Kappes, J H, Swoboda, P and Schnörr, C (2013). Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation. NIPS. Proceedings. 1950-1958
Savchynskyy, B and Schmidt, S (2013). Getting Feasible Variable Estimates From Infeasible Ones: MRF Local Polytope Study. Workshop on Inference for Probabilistic Graphical Models at ICCV. Proceedings
Savchynskyy, B and Schmidt, S (2012). Getting Feasible Variable Estimates From Infeasible Ones: MRF Local Polytope Study. arXiv:1210.4081
Savchynskyy, B, Schmidt, S, Kappes, J H and Schnörr, C (2012). Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing. UAI. Proceedings. 746-755
Savchynskyy, B, Kappes, J H, Schmidt, S and Schnörr, C (2011). A Study of Nesterov's Scheme for Lagrangian Decomposition and MAP Labeling. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)PDF icon Technical Report (408.99 KB)
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, Schmidt, S, Kappes, J H and Schnörr, C (2012). Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing. UAI 2012PDF icon Technical Report (529 KB)
Savchynskyy, B (2019). Discrete Graphical Models — An Optimization Perspective. Foundations and Trends® in Computer Graphics and Vision. Now Publishers. 11 160–429
Savchynskyy, B, Kappes, J H, Schmidt, S and Schnörr, C (2011). A Study of Nesterov's Scheme for Lagrangian Decomposition and MAP Labeling. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)
Savchynskyy, B, Kappes, J Hendrik, Swoboda, P and Schnörr, C (2013). Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation. NIPS
Savarino, F and Schnörr, C (2019). Continuous-Domain Assignment Flows. preprint: arXiv. https://arxiv.org/abs/1910.07287
Savarino, F and Schnörr, C (2019). A Variational Perspective on the Assignment Flow. Proc. SSVM. Springer
Savarino, F, Hühnerbein, R, Aström, F, Recknagel, J and Schnörr, C (2017). Numerical Integration of Riemannian Gradient Flows for Image Labeling. Proc. SSVM. Springer. 10302
Saussen, B (2007). Retention Time Domain Registration Of Liquid Chromatography/Mass Spectrometry Data. University of Heidelberg
Sauer, P (2008). Pattern Recognition On Statistically Textured Surfaces. University of Heidelberg
Saracoglu, K (2001). Bildanalyse von M-FISH. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg. http://www.ub.uni-heidelberg.de/archiv/1805/
E Sanmartin, F, Damrich, S and Hamprecht, F A (2019). Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning. Advances in Neural Information Processing Systems
Sanakoyeu, A, Bautista, M and Ommer, B (2018). Deep Unsupervised Learning of Visual Similarities. Pattern Recognition. 78. https://authors.elsevier.com/a/1WXUt77nKSb25 PDF icon PDF (8.35 MB)
Sanakoyeu, A, Kotovenko, D, Lang, S and Ommer, B (2018). A Style-Aware Content Loss for Real-time HD Style Transfer. Proceedings of the European Conference on Computer Vision (ECCV) (Oral)
Sanakoyeu, A, Tschernezki, V, Büchler, U and Ommer, B (2019). Divide and Conquer the Embedding Space for Metric Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://github.com/CompVis/metric-learning-divide-and-conquer
Sanakoyeu, A, Ma, P, Tschernezki, V and Ommer, B (2021). Improving Deep Metric Learning by Divide and Conquer. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). https://arxiv.org/abs/2109.04003

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