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 DOI Google Scholar BibTeX RTF EndNote XML

2021
Sitenko, D, Boll, B and Schnörr, C (2021). Assignment Flow For Order-Constrained OCT Segmentation. Int J Computer Vision. 129 Google Scholar BibTeX RTF EndNote XML
Gonzalez-Alvarado, D, Zeilmann, A and Schnörr, C (2021). Assignment Flows and Nonlocal PDEs on Graphs. GCPR, in press Google Scholar BibTeX RTF EndNote XML
Sitenko, D, Boll, B and Schnörr, C (2021). Assignment Flows and Nonlocal PDEs on Graphs. GCPR, in press Google Scholar BibTeX RTF EndNote XML
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 Google Scholar BibTeX RTF EndNote XML
Walter, F C, Damrich, S and Hamprecht, F A (2021). MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons. ISBI. 295-298 DOI Google Scholar BibTeX RTF EndNote XML Technical Report (1.83 MB)
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 Google Scholar BibTeX RTF EndNote XML
Damrich, S and Hamprecht, F H (2021). UMAP does not reproduce high-dimensional similarities due to negative sampling. arXiv preprint Google Scholar BibTeX RTF EndNote XML

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 Google Scholar BibTeX RTF EndNote XML
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 Google Scholar BibTeX RTF EndNote XML
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 Google Scholar BibTeX RTF EndNote XML
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 DOI Google Scholar BibTeX RTF EndNote XML

2019
Haußmann, M, Gerwinn, S and Kandemir, M (2019). Bayesian Prior Networks with PAC Training. arXiv preprint arXiv:1906.00816 Google Scholar BibTeX RTF EndNote XML
Haußmann, M, Hamprecht, F A and Kandemir, M (2019). Deep Active Learning with Adaptive Acquisition. IJCAI. Proceedings. 2470-2476 DOI Google Scholar BibTeX RTF EndNote XML Technical Report (137.6 KB)
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 Google Scholar BibTeX RTF EndNote XML
Rathke, F and Schnörr, C (2019). Fast Multivariate Log-Concave Density Estimation. Comp. Statistics & Data Analysis. 140 41-58 DOI Google Scholar BibTeX RTF EndNote XML
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) Google Scholar BibTeX RTF EndNote XML
Haußmann, M, Hamprecht, F A and Kandemir, M (2019). Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation. UAI. Proceedings. 563-573 Google Scholar BibTeX RTF EndNote XML Technical Report (1.04 MB)

2018
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. 1-12 DOI Google Scholar BibTeX RTF EndNote XML Technical Report (3.83 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/1811.03879v1 Google Scholar BibTeX RTF EndNote XML Article (891.47 KB) Oral slides (9.17 MB)
Sanakoyeu, A, Bautista, M and Ommer, B (2018). Deep Unsupervised Learning of Visual Similarities. Pattern Recognition. 78. https://authors.elsevier.com/a/1WXUt77nKSb25 DOI Google Scholar BibTeX RTF EndNote XML 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/10.1177/0271678X18777661 Google Scholar BibTeX RTF EndNote XML 0271678x18777661.pdf (770.87 KB)
Rathke, F and Schnörr, C (2018). Fast Multivariate Log-Concave Density Estimation. preprint: ArXiv. https://arxiv.org/pdf/1805.07272.pdf Google Scholar BibTeX RTF EndNote XML Technical Report (3.54 MB)
Abu Alhaija, H, Mustikovela, S K, Geiger, A and Rother, C (2018). Geometric Image Synthesis. ACCV. Proceedings, in press Google Scholar BibTeX RTF EndNote XML Technical Report (1.83 MB)
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 1317-1362 DOI Google Scholar BibTeX RTF EndNote XML Technical Report (2.62 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, Germany Google Scholar BibTeX RTF EndNote XML Article (5.34 MB) buechler_eccv18_poster.pdf (1.65 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 press Google Scholar BibTeX RTF EndNote XML Technical Report (3.28 MB)
Schilling, H, Diebold, M, Rother, C and Jähne, B (2018). Trust your Model: Light Field Depth Estimation with inline Occlusion Handling. CVPR. Proceedings DOI Google Scholar BibTeX RTF EndNote XML Technical Report (5.46 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. 698-713 DOI Google Scholar BibTeX RTF EndNote XML Technical Report (5.23 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, Germany Google Scholar BibTeX RTF EndNote XML Article (6.65 MB) Supplementary material (7.96 MB) Oral slides (14.96 MB)

2017
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 DOI Google Scholar BibTeX RTF EndNote XML
Kandemir, M, Hamprecht, F A, Wojek, C and Schmidt, U (2017). Active machine learning for training an event classification. Patent, Patent Number WO2017032775 A1 Google Scholar BibTeX RTF EndNote XML
Peter, S, Diego, F, Hamprecht, F A and Nadler, B (2017). Cost-efficient Gradient Boosting. NIPS, poster Google Scholar BibTeX RTF EndNote XML
Rathke, F, Desana, M and Schnörr, C (2017). Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans. MICCAI. Proceedings. 177-184 DOI Google Scholar BibTeX RTF EndNote XML Technical Report (4.79 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) Google Scholar BibTeX RTF EndNote XML Article (8.75 MB)
Kandemir, M, Hamprecht, F A, Wojek, C and Schmidt, U (2017). Maschinelles Lernen. Patent, Patent Number WO2017032775A1 Google Scholar BibTeX RTF EndNote XML Technical Report (317.04 KB)
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 Google Scholar BibTeX RTF EndNote XML
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). 6570-6579 DOI Google Scholar BibTeX RTF EndNote XML Technical Report (1.29 MB)

2016
Honauer, K, Johannsen, O, Kondermann, D and Goldlücke, B (2016). A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields. Computer Vision - ACCV 2016 : 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part III. Springer, Cham DOI Google Scholar BibTeX RTF EndNote XML
Bautista, M, Sanakoyeu, A, Sutter, E and Ommer, B (2016). CliqueCNN: Deep Unsupervised Exemplar Learning. Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS). MIT Press, Barcelona. https://arxiv.org/abs/1608.08792 Google Scholar BibTeX RTF EndNote XML Article (5.79 MB)

Pages