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2022

S. Damrich, “Discovering Structure without Labels”, Heidelberg University. 2022.

2021

A. Ruiz, “Deep k-segments: a generalization of k-means”, Heidelberg University, 2021.

A. Bailoni, “Deep Learning for Graph-Based Image Instance Segmentation”. Heidelberg University, 2021.

A. Vijayan, Tofanelli, R., Strauss, S., Cerrone, L., Wolny, A., Strohmeier, J., Kreshuk, A., Hamprecht, F. A., Smith, R. S., and Schneitz, K., “A Digital 3D Reference Atlas Reveals Cellular Growth Patterns Shaping the Arabidopsis Ovule”, eLife, 2021.

E. Fita, Damrich, S., and Hamprecht, F. A., “Directed Probabilistic Watershed”, NeurIPS. Proceedings, vol. 34. 2021.

Technical Report (957.78 KB)

2020

S. Lang and Ommer, B., “Das Objekt jenseits der Digitalisierung”, Das digitale Objekt, vol. 7. 2020.

lang_ommer_digitalhumanities_2020_.pdf (599.56 KB)

T. Dencker, Klinkisch, P., Maul, S. M., and Ommer, B., “Deep learning of cuneiform sign detection with weak supervision using transliteration alignment”, PLoS ONE, vol. 15, no. 12, 2020.

S. Bollweg, Haußmann, M., Kasieczka, G., Luchmann, M., Plehn, T., and Thompson, J., “Deep-Learning Jets with Uncertainties and More”, SciPost Phys, vol. 8, no. 1, 2020.

Technical Report (1.65 MB)

E. Kirschbaum, Bailoni, A., and Hamprecht, F. A., “DISCo: Deep Learning, Instance Segmentation, and Correlations for Cell Segmentation in Calcium Imaging”, MICCAI. Proceedings. pp. 151-162, 2020.

P. Sorrenson, Rother, C., and Köthe, U., “Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)”, in Intl. Conf. Learning Representations (ICLR), 2020.

PDF (2.43 MB)

P. Esser, Rombach, R., and Ommer, B., “A Disentangling Invertible Interpretation Network for Explaining Latent Representations”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2020.

Article (13.07 MB)

T. Milbich, Roth, K., Bharadhwaj, H., Sinha, S., Bengio, Y., Ommer, B., and Cohen, J. Paul, “DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning”, IEEE European Conference on Computer Vision (ECCV). 2020.

2019

G. -hung Lu, Tsai, W. -ting, and Jähne, B., “Decomposing infrared images of wind waves for quantitative separation into characteristic flow processes”, IEEE Transactions on Geoscience and Remote Sensing, vol. 57, pp. 8304–8316, 2019.

M. Haußmann, Hamprecht, F. A., and Kandemir, M., “Deep Active Learning with Adaptive Acquisition”, IJCAI. Proceedings. pp. 2470-2476, 2019.

Technical Report (137.6 KB)

W. Li, Hosseini Jafari, O., and Rother, C., “Deep Object Co-segmentation”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, vol. 11363 LNCS, pp. 638–653.

M. Papst, “Development of a method for quantitative imaging of air-water gas exchange”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2019.

B. Savchynskyy, “Discrete Graphical Models — An Optimization Perspective”, Foundations and Trends® in Computer Graphics and Vision, vol. 11, pp. 160–429, 2019.

A. Sanakoyeu, Tschernezki, V., Büchler, U., and Ommer, B., “Divide and Conquer the Embedding Space for Metric Learning”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019.

2018

L. Cerrone, “Deep End-to-End Learning of a Diffusion Process for Seeded Image Segmentation”, Heidelberg University, 2018.

A. Sanakoyeu, Bautista, M., and Ommer, B., “Deep Unsupervised Learning of Visual Similarities”, Pattern Recognition, vol. 78, 2018.

PDF (8.35 MB)

C. Weilbach, “Dictionary Learning with Bayesian GANs for Few-Shot Classification”, Heidelberg University, 2018.

2017

N. Ufer and Ommer, B., “Deep Semantic Feature Matching”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

article (8.88 MB)

M. Bautista, Sanakoyeu, A., and Ommer, B., “Deep Unsupervised Similarity Learning using Partially Ordered Sets”, in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

deep_unsupervised_similarity_learning_cvpr_2017_paper.pdf (905.82 KB)

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.

S. Ramos, Gehrig, S., Pinggera, P., Franke, U., and Rother, C., “Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling”, in IEEE Intelligent Vehicles Symposium, Proceedings, 2017, pp. 1025–1032.

C. Haubold, Uhlmann, V., Unser, M., and Hamprecht, F. A., “Diverse M-best Solutions by Dynamic Programming”, GCPR. Proceedings, vol. LNCS 10496. Springer, pp. 255-267, 2017.

V. Uhlmann, Haubold, C., Hamprecht, F. A., and Unser, M., “Diverse Shortest Paths for Bioimage Analysis”, Bioinformatics, pp. 1-3, 2017.

E. Brachmann, Krull, A., Nowozin, S., Shotton, J., Michel, F., Gumhold, S., and Rother, C., “DSAC - Differentiable RANSAC for camera localization”, in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017, vol. 2017-Janua, pp. 2492–2500.

2016

K. Honauer, Johannsen, O., Kondermann, D., and Goldlücke, B., “A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields”, in Computer Vision - ACCV 2016 : 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part III, Cham, 2016.

P. Schmidt, “Deep Learning for Bioimage Analysis”, University of Heidelberg, 2016.

L. Balles, “Deep Learning for Diabetic Retinopathy Diagnostics”, University of Heidelberg, 2016.

J. Kleesiek, Urban, G., Hubert, A., Schwarz, D., Maier-Hein, K., Bendszus, M., and Biller, A., “Deep MRI brain extraction: A 3D convolutional neural network for skull stripping.”, NeuroImage, vol. 129, pp. 460-469, 2016.

Technical Report (1.14 MB)

P. Bell and Ommer, B., “Digital Connoisseur? How Computer Vision Supports Art History”, in Connoisseurship nel XXI secolo. Approcci, Limiti, Prospettive, A. Aggujaro & S. Albl (ed.), Rome: Artemide, 2016.

M. Zisler, Petra, S., Schnörr, C., and Schnörr, C., “Discrete Tomography by Continuous Multilabeling Subject to Projection Constraints”, in Proc. GCPR, 2016.

F. Aström and Schnörr, C., “Double-Opponent Vectorial Total Variation”, in Proc. ECCV, 2016.

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