All Publications

2022

Garrido, Q, Damrich, S, Jäger, A, Cerletti, D, Claassen, M, Najman, L and Hamprecht, F A (2022). Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder. Bioinformatics. arXiv preprint. 38 (Suppl 1) i316-i324

2021

Sitenko, D, Boll, B and Schnörr, C (2021). Assignment Flow For Order-Constrained OCT Segmentation. Int J Computer Vision. 129
Gonzalez-Alvarado, D, Zeilmann, A and Schnörr, C (2021). Assignment Flows and Nonlocal PDEs on Graphs. GCPR, in press
Sitenko, D, Boll, B and Schnörr, C (2021). Assignment Flows and Nonlocal PDEs on Graphs. GCPR, in press
Milbich, T, Roth, K, Sinha, S, Schmidt, L, Ghassemi, M and Ommer, B (2021). Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning. https://arxiv.org/abs/2107.09562
Roth, K, Milbich, T, Ommer, B, Cohen, J Paul and Ghassemi, M (2021). S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning. Proceedings of International Conference on Machine Learning (ICML). https://arxiv.org/abs/2009.08348
Damrich, S and Hamprecht, F H (2021). UMAP does not reproduce high-dimensional similarities due to negative sampling. arXiv preprint
Walter, F C, Damrich, S and Hamprecht, F A (2021). MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons. ISBI. 295-298PDF icon Technical Report (1.83 MB)

2020

Milbich, T, Roth, K, Bharadhwaj, H, Sinha, S, Bengio, Y, Ommer, B and Cohen, J Paul (2020). DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning. IEEE European Conference on Computer Vision (ECCV). https://arxiv.org/abs/2004.13458
Roth, K, Milbich, T, Sinha, S, Gupta, P, Ommer, B and Cohen, J Paul (2020). Revisiting Training Strategies and Generalization Performance in Deep Metric Learning. International Conference on Machine Learning (ICML). https://arxiv.org/pdf/2002.08473.pdf
Milbich, T, Roth, K, Brattoli, B and Ommer, B (2020). Sharing Matters for Generalization in Deep Metric Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). https://arxiv.org/abs/2004.05582
Milbich, T, Roth, K and Ommer, B (2020). PADS: Policy-Adapted Sampling for Visual Similarity Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1. https://arxiv.org/abs/2003.11113

2019

Rathke, F and Schnörr, C (2019). Fast Multivariate Log-Concave Density Estimation. Comp. Statistics & Data Analysis. 140 41-58
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)
Haußmann, M, Gerwinn, S and Kandemir, M (2019). Bayesian Prior Networks with PAC Training. arXiv preprint arXiv:1906.00816
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, Hamprecht, F A and Kandemir, M (2019). Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation. UAI. Proceedings. 563-573PDF icon Technical Report (1.04 MB)
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

2018

Hühnerbein, R, Savarino, F, Aström, F and Schnörr, C (2018). Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment. SIAM Journal on Imaging Sciences. 11 1317-1362PDF icon Technical Report (2.62 MB)
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)
Wahl, A - S, Erlebach, E, Brattoli, B, Büchler, U, Kaiser, J, Ineichen, V B, Mosberger, A C, Schneeberger, S, Imobersteg, S, Wieckhorst, M, Stirn, M, Schroeter, A, Ommer, B and Schwab, M E (2018). Early reduced behavioral activity induced by large strokes affects the efficiency of enriched environment in rats. Sage Journals. Journal of Cerebral Blood Flow & Metabolism. http://journals.sagepub.com/doi/abs/10.1177/0271678X18777661PDF icon 0271678x18777661.pdf (770.87 KB)
Schilling, H, Diebold, M, Rother, C and Jähne, B (2018). Trust your Model: Light Field Depth Estimation with inline Occlusion Handling. CVPR. ProceedingsPDF icon Technical Report (5.46 MB)
Abu Alhaija, H, Mustikovela, S K, Mescheder, A, Geiger, C and Rother, C (2018). Augmented Reality Meets Computer Vision Efficient Data Generation for Urban Driving Scenes. IJCV. 1-12PDF icon Technical Report (3.83 MB)
Rathke, F and Schnörr, C (2018). Fast Multivariate Log-Concave Density Estimation. preprint: ArXiv. https://arxiv.org/pdf/1805.07272.pdfPDF icon Technical Report (3.54 MB)
Sayed, N, Brattoli, B and Ommer, B (2018). Cross and Learn: Cross-Modal Self-Supervision. German Conference on Pattern Recognition (GCPR) (Oral). Stuttgart, Germany. https://arxiv.org/abs/1811.03879v1PDF icon Article (891.47 KB)PDF icon Oral slides (9.17 MB)
Blum, O, Brattoli, B and Ommer, B (2018). X-GAN: Improving Generative Adversarial Networks with ConveX Combinations. German Conference on Pattern Recognition (GCPR) (Oral). Stuttgart, GermanyPDF icon Article (6.65 MB)PDF icon Supplementary material (7.96 MB)PDF icon Oral slides (14.96 MB)
Abu Alhaija, H, Mustikovela, S K, Geiger, A and Rother, C (2018). Geometric Image Synthesis. ACCV. Proceedings, in pressPDF icon Technical Report (1.83 MB)
Hosseini Jafari, O, Mustikovela, S K, Pertsch, K, Brachmann, E and Rother, C (2018). iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects. ACCV. Proceedings, in pressPDF icon Technical Report (3.28 MB)
Zern, A, Zisler, M, Aström, F, Petra, S and Schnörr, C (2018). Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment. GCPR. Proceedings. 698-713PDF icon Technical Report (5.23 MB)
Büchler, U, Brattoli, B and Ommer, B (2018). Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning. Proceedings of the European Conference on Computer Vision (ECCV). (UB and BB contributed equally), Munich, GermanyPDF icon Article (5.34 MB)PDF icon buechler_eccv18_poster.pdf (1.65 MB)

2017

Haußmann, M, Hamprecht, F A and Kandemir, M (2017). Variational Bayesian Multiple Instance Learning with Gaussian Processes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 6570-6579PDF icon Technical Report (1.29 MB)
Brattoli, B, Büchler, U, Wahl, A - S, Schwab, M E and Ommer, B (2017). LSTM Self-Supervision for Detailed Behavior Analysis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (BB and UB contributed equally)PDF icon Article (8.75 MB)
Vianello, A, Manfredi, G, Diebold, M and Jähne, B (2017). 3D reconstruction by a combined structure tensor and Hough transform light field approach. tm - Technisches Messen
Kandemir, M, Hamprecht, F A, Wojek, C and Schmidt, U (2017). Active machine learning for training an event classification. Patent, Patent Number WO2017032775 A1
Peter, S, Kirschbaum, E, Both, M, Campbell, L A, Harvey, B K, Heins, C, Durstewitz, D, Diego, F and Hamprecht, F A (2017). Sparse convolutional coding for neuronal assembly detection. NIPS, poster
Kandemir, M, Hamprecht, F A, Wojek, C and Schmidt, U (2017). Maschinelles Lernen. Patent, Patent Number WO2017032775A1PDF icon Technical Report (317.04 KB)
Peter, S, Diego, F, Hamprecht, F A and Nadler, B (2017). Cost-efficient Gradient Boosting. NIPS, poster
Rathke, F, Desana, M and Schnörr, C (2017). Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans. MICCAI. Proceedings. 177-184PDF icon Technical Report (4.79 MB)

2016

Stefanoiu, A, Weinmann, A, Storath, M, Navab, N and Baust, M (2016). Joint Segmentation and Shape Regularization with a Generalized Forward Backward Algorithm. IEEE Transactions on Image Processing. 25 3384 - 3394PDF icon Technical Report (3.55 MB)
von Schmude, N, Lothe, P and Jähne, B (2016). Relative Pose Estimation from Straight Lines using Parallel Line Clustering and its Application to Monocular Visual Odometry. Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

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