All Publications

2019

Desana, M and Schnörr, C (2019). Sum-Product Graphical Models. Machine Learning. https://doi.org/10.1007/s10994-019-05813-2
Kostrykin, L, Schnörr, C and Rohr, K (2019). Globally Optimal Segmentation of Cell Nuclei in Fluoroscence Microscopy Images using Shape and Intensity Information. Medical Image Analysis. https://doi.org/10.1016/j.media.2019.101536
Schnörr, (2019). Assignment Flows. Variational Methods for Nonlinear Geometric Data and Applications. Springer
Savarino, F and Schnörr, C (2019). Continuous-Domain Assignment Flows. preprint: arXiv. https://arxiv.org/abs/1910.07287
Hühnerbein, R, Savarino, F, Petra, S and Schnörr, C (2019). Learning Adaptive Regularization for Image Labeling Using Geometric Assignment. preprint: arXiv. https://arxiv.org/abs/1910.09976
Zisler, M, Zern, A, Petra, S and Schnörr, C (2019). Self-Assignment Flows for Unsupervised Data Labeling on Graphs. preprint: arXiv. https://arxiv.org/abs/1911.03472
Censor, Y, Petra, S and Schnörr, C (2019). Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case. preprint: arXiv. https://arxiv.org/abs/1911.05498
Brachmann, E and Rother, C (2019). Neural-guided RANSAC: Learning where to sample model hypotheses. Proceedings of the IEEE International Conference on Computer Vision. 2019-Octob 4321–4330. http://arxiv.org/abs/1905.04132PDF icon PDF (8.02 MB)
Brachmann, E and Rother, C (2019). Expert sample consensus applied to camera re-localization. Proceedings of the IEEE International Conference on Computer Vision. 2019-Octob 7524–7533. http://arxiv.org/abs/1908.02484
Leistner, T, Schilling, H, Mackowiak, R, Gumhold, S and Rother, C (2019). Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift. Proceedings - 2019 International Conference on 3D Vision, 3DV 2019. 249–257. http://arxiv.org/abs/1909.09059 http://dx.doi.org/10.1109/3DV.2019.00036PDF icon PDF (8.94 MB)
Li, W, Hosseini Jafari, O and Rother, C (2019). Deep Object Co-segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11363 LNCS 638–653
Hosseini Jafari, O, Mustikovela, S Karthik, Pertsch, K, Brachmann, E and Rother, C (2019). iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11363 LNCS 477–492
Abu Alhaija, H, Mustikovela, S Karthik, Geiger, A and Rother, C (2019). Geometric Image Synthesis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11366 LNCS 85–100. https://youtu.be/W2tFCz9xJoU
Savchynskyy, B (2019). Discrete Graphical Models — An Optimization Perspective. Foundations and Trends® in Computer Graphics and Vision. Now Publishers. 11 160–429
Mackowiak, R, Lenz, P, Ghori, O, Diego, F, Lange, O and Rother, C (2019). CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation. British Machine Vision Conference 2018, BMVC 2018
Ardizzone, L, Lüth, C, Kruse, J, Rother, C and Köthe, U (2019). Guided Image Generation with Conditional Invertible Neural Networks. http://arxiv.org/abs/1907.02392
Bhowmik, A, Gumhold, S, Rother, C and Brachmann, E (2019). Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task. http://arxiv.org/abs/1912.00623
Hehn, T M, Kooij, J F P and Hamprecht, F A (2019). End-to-End Learning of Decision Trees and Forests. International Journal of Computer Vision. 128 997-1011
Kirschbaum, E, Haußmann, M, Wolf, S, Sonntag, H, Schneider, J, Elzoheiry, S, Kann, O, Durstewitz, D and Hamprecht, F A (2019). LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos. ICLR. Proceedings
Bendinger, A L, Debus, C, Glowa, C, Karger, C P, Peter, J and Storath, M (2019). Bolus arrival time estimation in dynamic contrast-enhanced magnetic resonance imaging of small animals based on spline models, in press. Physics in Medicine and Biology. 64
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
Kotovenko, D, Sanakoyeu, A, Lang, S, Ma, P and Ommer, B (2019). Using a Transformation Content Block For Image Style Transfer. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Lorenz, D, Bereska, L, Milbich, T and Ommer, B (2019). Unsupervised Part-Based Disentangling of Object Shape and Appearance. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Oral + Best paper finalist: top 45 / 5160 submissions)
Cerrone, L, Zeilmann, A and Hamprecht, F A (2019). End-to-End Learned Random Walker for Seeded Image Segmentation. CVPR. Proceedings. 12559-12568
Li, J (2019). Robust Single Object Tracking Via Fully Convolutional Siamese Networks. Heidelberg University
Li, Y (2019). Semantic Instance Segmentation With The Multiway Mutex Watershed. Heidelberg University
Bengio, Y, Deleu, T, Rahaman, N, Ke, R, Lachapelle, S, Bilaniuk, O, Goyal, A and Pal, C (2019). A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms. arXiv preprint arXiv:1901.10912PDF icon Technical Report (871.59 KB)
Haußmann, M, Hamprecht, F A and Kandemir, M (2019). Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation. UAI. ProceedingsPDF icon Technical Report (1.04 MB)
Haußmann, M, Hamprecht, F A and Kandemir, M (2019). Deep Active Learning with Adaptive Acquisition. IJCAI. Proceedings. 2470-2476PDF icon Technical Report (137.6 KB)
Haußmann, M, Gerwinn, S and Kandemir, M (2019). Bayesian Prior Networks with PAC Training. arXiv preprint arXiv:1906.00816
Brattoli, B, Roth, K and Ommer, B (2019). MIC: Mining Interclass Characteristics for Improved Metric Learning. Proceedings of the Intl. Conf. on Computer Vision (ICCV)
Kotovenko, D, Sanakoyeu, A, Lang, S and Ommer, B (2019). Content and Style Disentanglement for Artistic Style Transfer. Proceedings of the Intl. Conf. on Computer Vision (ICCV)
Esser, P, Haux, J and Ommer, B (2019). Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis. Proceedings of the Intl. Conf. on Computer Vision (ICCV). https://compvis.github.io/robust-disentangling/
Ufer, N, Lui, K To, Schwarz, K, Warkentin, P and Ommer, B (2019). Weakly Supervised Learning of Dense SemanticCorrespondences and Segmentation. German Conference on Pattern Recognition (GCPR)PDF icon article (6.1 MB)
Rathke, F and Schnörr, C (2019). Fast Multivariate Log-Concave Density Estimation. Comp. Statistics & Data Analysis. 140 41-58
Remme, R (2019). Instance Segmentation Via Associative Pixel Embeddings. Heidelberg University
Fita, E (2019). Semi-Supervised Distance-Based Segmentation. Heidelberg University
Hanslovsky, P (2019). Isotropic Reconstruction of Neural Morphology from Large Non-Isotropic 3D Electron MIcroscopy. Heidelberg University
Kamann, C and Rother, C (2019). Benchmarking the Robustness of Semantic Segmentation Models. http://arxiv.org/abs/1908.05005
Li, W, Hosseini Jafari, O and Rother, C (2019). Localizing Common Objects Using Common Component Activation Map

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