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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

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.

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

Technical Report (957.78 KB)

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.

F. C. Walter, Damrich, S., and Hamprecht, F. A., “MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons”, ISBI. pp. 295-298, 2021.

Technical Report (1.83 MB)

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.

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.

S. Damrich and Hamprecht, F. H., “UMAP does not reproduce high-dimensional similarities due to negative sampling”. arXiv preprint, 2021.

S. Damrich and Hamprecht, F. A., “On UMAP's True Loss Function”, NeurIPS. Proceedings, vol. 34. 2021.

Technical Report (1.87 MB)

A. Blattmann, Milbich, T., Dorkenwald, M., and Ommer, B., “Understanding Object Dynamics for Interactive Image-to-Video Synthesis”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2021.

B. Brattoli, Büchler, U., Dorkenwald, M., Reiser, P., Filli, L., Helmchen, F., Wahl, A. - S., and Ommer, B., “Unsupervised behaviour analysis and magnification (uBAM) using deep learning”, Nature Machine Intelligence, 2021.

2020

A. Wolny, Cerrone, L., Vijayan, A., Tofanelli, R., Vilches-Barro, A., Louveaux, M., Wenzel, C., Strauss, S., Wilson-Sanchez, D., Lymbouridou, R., Steigleder, S. S., Pape, C., Bailoni, A., Duran-Nebreda, S., Bassel, G. W., Lohmann, J. U., Tsiantis, M., Hamprecht, F. A., Schneitz, K., Maizel, A., and Kreshuk, A., “Accurate and Versatile 3D Segmentation of Plant Tissues at Cellular Resolution”, eLife, vol. 9, 2020.

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. K. Mustikovela, Jampani, V., De Mello, S., Liu, S., Iqbal, U., Rother, C., and Kautz, J., “Self-Supervised Viewpoint Learning From Image Collections”, in CONSAC, 2020.

PDF (8.77 MB)

M. Desana and Schnörr, C., “Sum-Product Graphical Models”, Machine Learning, vol. 109, pp. 135–173, 2020.

M. Dorkenwald, Büchler, U., and Ommer, B., “Unsupervised Magnification of Posture Deviations Across Subjects”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2020.

article.pdf (1.15 MB)

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.

J. Kleesiek, Morshuis, J. Nikolas, Isensee, F., Deike-Hofmann, K., Paech, D., Kickingereder, P., Köthe, U., Rother, C., Forsting, M., Wick, W., Bendszus, M., Schlemmer, H. Peter, and Radbruch, A., “Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium?: A Feasibility Study”, Investigative Radiology, vol. 54, pp. 653–660, 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.

E. Kirschbaum, Haußmann, M., Wolf, S., Sonntag, H., Schneider, J., Elzoheiry, S., Kann, O., Durstewitz, D., and Hamprecht, F. A., “LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos”, ICLR. Proceedings. 2019.

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.

Technical Report (871.59 KB)

F. E Sanmartin, Damrich, S., and Hamprecht, F. A., “Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning”, in Advances in Neural Information Processing Systems, 2019.

M. Desana and Schnörr, C., “Sum-Product Graphical Models”, Machine Learning, 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

T. Hodaň, Michel, F., Brachmann, E., Kehl, W., Buch, A. Glent, Kraft, D., Drost, B., Vidal, J., Ihrke, S., Zabulis, X., Sahin, C., Manhardt, F., Tombari, F., Kim, T. Kyun, Matas, J., and Rother, C., “BOP: Benchmark for 6D object pose estimation”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11214 LNCS, pp. 19–35.

F. Draxler, “The Energy Landscape of Deep Neural Networks”, Heidelberg University, 2018.

F. Draxler, Veschgini, K., Salmhofer, M., and Hamprecht, F. A., “Essentially No Barriers in Neural Network Energy Landscape”, ICML. Proceedings, vol. 80. p. 1308--1317, 2018.

Technical Report (685.93 KB)

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.

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.

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