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2022
Q. Garrido, Damrich, S., Jäger, A., Cerletti, D., Claassen, M., Najman, L., and Hamprecht, F. A., “Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder”, Bioinformatics, vol. 38 (Suppl 1), pp. i316-i324, 2022.
2021
D. Gonzalez-Alvarado, Zeilmann, A., and Schnörr, C., “Assignment Flows and Nonlocal PDEs on Graphs”, GCPR, in press. 2021.
T. Milbich, Roth, K., Sinha, S., Schmidt, L., Ghassemi, M., and Ommer, B., “Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning”. 2021.
M. Haußmann, Gerwinn, S., Look, A., Rakitsch, B., and Kandemir, M., “Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes”, International Conference on Artificial Intelligence and Statistics , vol. PMLR 130. pp. 478-486, 2021.
C. Pape, Remme, R., Wolny, A., Olberg, S., Wolf, S., Cerrone, L., Cortese, M., Klaus, S., Lucic, B., Ullrich, S., Anders-Össwein, M., Wolf, S., Cerikan, B., Neufeldt, C. J., Ganter, M., Schnitzler, P., Merle, U., Lusic, M., Boulant, S., Stanifer, M., Bartenschlager, R., Hamprecht, F. A., Kreshuk, A., Tischer, C., Kräusslich, H. - G., Müller, B., and Laketa, V., “Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera”, BioEssays, vol. 43, no. 3, 2021.
K. Roth, Milbich, T., Ommer, B., Cohen, J. Paul, and Ghassemi, M., “S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning”, Proceedings of International Conference on Machine Learning (ICML). 2021.
H. Arlt, Sui, X., Folger, B., Adams, C., Chen, X., Remme, R., Hamprecht, F. A., DiMaio, F., Liao, M., Goodman, J. M., Farese, R. V., and Walther, T. C., “Seipin forms a flexible cage at lipid droplet formation sites”. bioRxiv, 2021.
2020
C. Schnörr, “Assignment Flows”, Handbook of Variational Methods for Nonlinear Geometric Data. Springer, p. 235—260, 2020.
M. Haußmann, Gerwinn, S., and Kandemir, M., “Bayesian Evidential Deep Learning with PAC Regularization ”, 3rd Symposium on Advances in Approximate Bayesian Inference . 2020.
H. Schilling, Gutsche, M., Brock, A., Späth, D., Rother, C., and Krispin, K., “Mind the Gap – A Benchmark for Dense Depth Prediction beyond Lidar”, in 2nd Workshop on Safe Artificial Intelligence for Automated Driving, in conjunction with CVPR 2020, 2020.
A. Bhowmik, Gumhold, S., Rother, C., and Brachmann, E., “Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task”, in CVPR 2020 (oral), 2020.PDF icon PDF (2.74 MB)
K. Roth, Milbich, T., Sinha, S., Gupta, P., Ommer, B., and Cohen, J. Paul, “Revisiting Training Strategies and Generalization Performance in Deep Metric Learning”, International Conference on Machine Learning (ICML). 2020.
T. Milbich, Ghori, O., and Ommer, B., “Unsupervised Representation Learning by Discovering Reliable Image Relations”, Pattern Recognition, vol. 102, 2020.
2019
C. Schnörr, “Assignment Flows”, Variational Methods for Nonlinear Geometric Data and Applications. Springer, 2019.
M. Haußmann, Gerwinn, S., and Kandemir, M., “Bayesian Prior Networks with PAC Training”, arXiv preprint arXiv:1906.00816, 2019.
A. L. Bendinger, Debus, C., Glowa, C., Karger, C. P., Peter, J., and Storath, M., “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, vol. 64, no. 4, 2019.
R. Mackowiak, Lenz, P., Ghori, O., Diego, F., Lange, O., and Rother, C., “CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation”, in British Machine Vision Conference 2018, BMVC 2018, 2019.
A. Imle, Kumberger, P., Schnellbächer, N. D., Fehr, J., Carillo-Bustamente, P., Ales, J., Schmidt, P., Ritter, C., Godinez, W. J., Müller, B., Rohr, K., Hamprecht, F. A., Schwarz, U. S., Graw, F., and Fackler, O. T., “Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures”, Nature Communications, vol. 13;10(1), 2019.
A. Imle, Kumberger, P., Schnellbächer, N. D., Fehr, J., Carillo-Bustamente, P., Ales, J., Schmidt, P., Ritter, C., Godinez, W. J., Müller, B., Rohr, K., Hamprecht, F. A., Schwarz, U. S., Graw, F., and Fackler, O. T., “Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures”, Nature Communications, vol. 13;10(1), 2019.
H. Abu Alhaija, Mustikovela, S. Karthik, Geiger, A., and Rother, C., “Geometric Image Synthesis”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, vol. 11366 LNCS, pp. 85–100.
T. Leistner, Schilling, H., Mackowiak, R., Gumhold, S., and Rother, C., “Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift”, in Proceedings - 2019 International Conference on 3D Vision, 3DV 2019, 2019, pp. 249–257.PDF icon PDF (8.94 MB)
Y. Bengio, Deleu, T., Rahaman, N., Ke, R., Lachapelle, S., Bilaniuk, O., Goyal, A., and Pal, C., “A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms”, arXiv preprint arXiv:1901.10912, 2019.PDF icon Technical Report (871.59 KB)
A. Bhowmik, Gumhold, S., Rother, C., and Brachmann, E., “Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task”, 2019.
M. Großkinsky, “Synaptic Cleft Prediction on Electron Microsope Images”, Heidelberg University, 2019.
M. Esposito, Hennersperger, C., Göbl, R., Demaret, L., Storath, M., Navab, N., Baust, M., and Weinmann, A., “Total variation regularization of pose signals with an application to 3D freehand ultrasound”, IEEE Transactions on Medical Imaging, vol. 38(10), pp. 2245-2258, 2019.
2018
H. Abu Alhaija, Mustikovela, S. Karthik, Mescheder, L., Geiger, A., and Rother, C., “Augmented Reality Meets Computer Vision”, International Journal of Computer Vision, vol. In press, pp. 1–13, 2018.
H. Abu Alhaija, Mustikovela, S. Karthik, Mescheder, L., Geiger, A., and Rother, C., “Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes”, International Journal of Computer Vision, vol. 126, pp. 961–972, 2018.
H. Abu Alhaija, Mustikovela, S. K., Mescheder, A., Geiger, C., and Rother, C., “Augmented Reality Meets Computer Vision Efficient Data Generation for Urban Driving Scenes”, IJCV, pp. 1-12, 2018.PDF icon Technical Report (3.83 MB)
H. Abu Alhaija, Mustikovela, S. K., Geiger, A., and Rother, C., “Geometric Image Synthesis”, ACCV. Proceedings, in press. 2018.PDF icon Technical Report (1.83 MB)
O. Ghori, Mackowiak, R., Bautista, M., Beuter, N., Drumond, L., Diego, F., and Ommer, B., “Learning to Forecast Pedestrian Intention from Pose Dynamics”, in Intelligent Vehicles, IEEE, 2018, 2018.
2017
O. Hosseini Jafari, Groth, O., Kirillov, A., Yang, M. Ying, and Rother, C., “Analyzing modular CNN architectures for joint depth prediction and semantic segmentation”, in Proceedings - IEEE International Conference on Robotics and Automation, 2017, pp. 4620–4627.
H. Abu Alhaija, Mustikovela, S. Karthik, Mescheder, L., Geiger, A., and Rother, C., “Augmented reality meets deep learning for car instance segmentation in urban scenes”, in British Machine Vision Conference 2017, BMVC 2017, 2017.
A. Behl, Hosseini Jafari, O., Mustikovela, S. Karthik, Abu Alhaija, H., Rother, C., and Geiger, A., “Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?”, in Proceedings of the IEEE International Conference on Computer Vision, 2017, vol. 2017-Octob, pp. 2593–2602.
A. Behl, Hosseini Jafari, O., Mustikovela, S. Karthik, Abu Alhaija, H., Rother, C., and Geiger, A., “Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?”, in Proceedings of the IEEE International Conference on Computer Vision, 2017, vol. 2017-Octob, pp. 2593–2602.
H. Schilling, Diebold, M., Gutsche, M., and Jähne, B., “On the design of a fractal calibration pattern for improved camera calibration”, tm - Technisches Messen, vol. 84, pp. 440–451, 2017.

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