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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.

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)

S. Wolf, Li, Y., Pape, C., Bailoni, A., Kreshuk, A., and Hamprecht, F. A., “The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation”, ECCV. Proceedings. pp. 208-224, 2020.

T. Milbich, Roth, K., Brattoli, B., and Ommer, B., “Sharing Matters for Generalization in Deep Metric Learning”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020.

B. Jähne, “Struktur und Chaos: Kleinskalige Austauschprozesse zwischen Atmosphäre und Meer”, Heidelberger Jahrbücher Online, Entwicklung – Wie aus Prozessen Strukturen werden, vol. 5. pp. 133–150, 2020.

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

Y. Censor, Petra, S., and Schnörr, C., “Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case”, J. Appl. Numer. Optimization (in press; arXiv:1911.05498), vol. 2, pp. 15-62, 2020.

2019

M. Haußmann, Hamprecht, F. A., and Kandemir, M., “Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation”, UAI. Proceedings. pp. 563-573, 2019.

Technical Report (1.04 MB)

M. Zisler, Zern, A., Petra, S., and Schnörr, C., “Self-Assignment Flows for Unsupervised Data Labeling on Graphs”, preprint: arXiv, 2019.

Y. Li, “Semantic Instance Segmentation with the Multiway Mutex Watershed”, Heidelberg University, 2019.

E. Fita, “Semi-supervised distance-based segmentation”, Heidelberg University, 2019.

P. Voigt, “Simulation and Measurement of the Water-sided Viscous Shear Stress without Waves”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2019.

M. Storath, Kiefer, L., and Weinmann, A., “Smoothing for signals with discontinuities using higher order Mumford-Shah models”, Numerische Mathematik, vol. 143(2), pp. 423-460, 2019.

Technical Report (1.09 MB)

M. Desana and Schnörr, C., “Sum-Product Graphical Models”, Machine Learning, 2019.

Y. Censor, Petra, S., and Schnörr, C., “Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case”, preprint: arXiv, 2019.

M. Großkinsky, “Synaptic Cleft Prediction on Electron Microsope Images”, Heidelberg University, 2019.

2018

L. Kostrykin, Schnörr, C., and Rohr, K., “Segmentation of Cell Nuclei Using Intensity-Based Model Fitting and Sequential Convex Programming”, in Proc. ISBI, 2018.

D. Kawetzki, “Semantic Segmentation of Urban Scenes Using Deep Learning”, Heidelberg University, 2018.

N. Rahaman, Arpit, D., Baratin, A., Draxler, F., Lin, M., Hamprecht, F. A., Bengio, Y., and Courville, A., “On the spectral bias of deep neural networks”, arXiv preprint arXiv:1806.08734, 2018.

A. Sanakoyeu, Kotovenko, D., Lang, S., and Ommer, B., “A Style-Aware Content Loss for Real-time HD Style Transfer”, in Proceedings of the European Conference on Computer Vision (ECCV) (Oral), 2018.

“A Supplementary Material CEREALS-Cost-Effective REgion-based Active Learning for Semantic Segmentation”, 2018.

2017

C. Haubold, “Scalable Inference for Multi-Target Tracking on Proliferating Cells”. University of Heidelberg, 2017.

J. Berger, Lenzen, F., Becker, F., Neufeld, A., and Schnörr, C., “{Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations”, J. Math. Imag. Vision, vol. 58, pp. 102–129, 2017.

P. Markowsky, Reith, S., Zuber, T. E., König, R., Rohr, K., and Schnörr, C., “Segmentation of cell structure using model-based set covering with iterative reweighting”, in Proc. ISBI, 2017.

P. Neigel, “Self-Similarity Based Detection of Temporal Motifs in Multivariate Signals”, Heidelberg University, 2017.

Ö. Sümer, Dencker, T., and Ommer, B., “Self-supervised Learning of Pose Embeddings from Spatiotemporal Relations in Videos”, in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017.

Paper (3.98 MB)

Supplementary Material (3.36 MB)

M. Hullin, Klein, R., Schultz, T., Yao, A., Li, W., Hosseini Jafari, O., and Rother, C., “Semantic-Aware Image Smoothing”, Vision, Modeling, and Visualization, 2017.

C. Pape, Beier, T., Li, P., Jain, V., Brock, D. D., and Kreshuk, A., “Solving Large Multicut Problems for Connectomics via Domain Decomposition”, Bioimage Computing Workshop. ICCV. pp. 1-10, 2017.

S. Peter, Kirschbaum, E., Both, M., Campbell, L. A., Harvey, B. K., Heins, C., Durstewitz, D., Diego, F., and Hamprecht, F. A., “Sparse convolutional coding for neuronal assembly detection”, NIPS, poster. 2017.

A. Rennebaum, “Spatio-Temporal Properties of the initial Wave Formation Phase at the Aeolotron”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2017.

2016

C. Haubold, Schiegg, M., Kreshuk, A., Berg, S., Köthe, U., and Hamprecht, F. A., “Segmenting and Tracking Multiple Dividing Targets Using ilastik”, in Focus on Bio-Image Informatics, vol. 219, Springer, 2016, pp. 199-229.

Technical Report (4.46 MB)

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