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Author Title Type [ Year(Asc)]
2022
E. Fita, Damrich, S., and Hamprecht, F. A., “The Algebraic Path Problem for Graph Metrics”, 39th International Conference on Machine Learning, PMLR. Proceedings , vol. 162. pp. 19178-19204, 2022.
S. Damrich, “Discovering Structure without Labels”, Heidelberg University. 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. Sitenko, Boll, B., and Schnörr, C., “Assignment Flow For Order-Constrained OCT Segmentation”, Int J Computer Vision, vol. 129, 2021.
D. Gonzalez-Alvarado, Zeilmann, A., and Schnörr, C., “Assignment Flows and Nonlocal PDEs on Graphs”, GCPR, in press. 2021.
D. Sitenko, Boll, B., and Schnörr, C., “Assignment Flows and Nonlocal PDEs on Graphs”, GCPR, in press. 2021.
M. Haußmann, “Bayesian Neural Networks for Probabilistic Machine Learning”. Heidelberg University, 2021.
A. Blattmann, Milbich, T., Dorkenwald, M., and Ommer, B., “Behavior-Driven Synthesis of Human Dynamics”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 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.
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.PDF icon Technical Report (957.78 KB)
M. Kandemir, Agkül, A., Haußmann, M., and Ünal, G., “Evidential Turing Processes”. arXiv preprint, 2021.
E. Jenner, Fita, E., and Hamprecht, F. A., “Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice”, ICCV. Proceedings. pp. 4602-4611, 2021.PDF icon Technical Report (1.1 MB)
R. Rombach, Esser, P., and Ommer, B., “Geometry-Free View Synthesis: Transformers and no 3D Priors”, in Proceedings of the Intl. Conf. on Computer Vision (ICCV), 2021.
M. Jahn, Rombach, R., and Ommer, B., “High-Resolution Complex Scene Synthesis with Transformers”, in CVPR 2021, AI for Content Creation Workshop, 2021.
P. Esser, Rombach, R., Blattmann, A., and Ommer, B., “ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis”. 2021.
A. Sanakoyeu, Ma, P., Tschernezki, V., and Ommer, B., “Improving Deep Metric Learning by Divide and Conquer”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021.
L. M. Schütz, Louveaux, M., Vilches-Barro, A., Bouziri, S., Cerrone, L., Wolny, A., Kreshuk, A., Hamprecht, F. A., and Maizel, A., “Integration of Cell Growth and Asymmetric Division during Lateral Root Initiation in Arabidopsis thaliana”, Plant and Cell Physiology, vol. 62, no. 8, pp. 1269-1279, 2021.
A. Blattmann, Milbich, T., Dorkenwald, M., and Ommer, B., “iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis”, in Proceedings of the International Conference on Computer Vision (ICCV), 2021.
A. Andersson, Diego, F., Hamprecht, F. A., and Wählby, C., “ISTDECO: In Situ Transcriptomics Decoding by Deconvolution”, bioRxiv, 2021.
M. Afifi, Derpanis, K. G., Ommer, B., and Brown, M. S., “Learning Multi-Scale Photo Exposure Correction”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 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.
F. C. Walter, Damrich, S., and Hamprecht, F. A., “MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons”, ISBI. pp. 295-298, 2021.PDF icon Technical Report (1.83 MB)
D. Kotovenko, Wright, M., Heimbrecht, A., and Ommer, B., “Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 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.
C. Pape, “Scalable Instance Segmentation for Microscopy”. Heidelberg University, 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.
M. Amirul Islam, Kowal, M., Esser, P., Jia, S., Ommer, B., Derpanis, K. G., and Bruce, N., “Shape or Texture: Understanding Discriminative Features in CNNs”, International Conference on Learning Representations (ICLR). 2021.
M. Dorkenwald, Milbich, T., Blattmann, A., Rombach, R., Derpanis, K. G., and Ommer, B., “Stochastic Image-to-Video Synthesis usin cINNs”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2021.
P. Esser, Rombach, R., and Ommer, B., “Taming Transformers for High-Resolution Image Synthesis”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2021.
S. Lang and Ommer, B., “Transforming Information Into Knowledge: How Computational Methods Reshape Art History”, Digital Humanities Quaterly (DHQ), vol. 15, no. 3, 2021.
S. Lang and Ommer, B., “Transforming Information Into Knowledge: How Computational Methods Reshape Art History”, Digital Humanities Quaterly (DHQ), vol. 15, no. 3, 2021.

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