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2021

Sitenko, D, Boll, B and Schnörr, C (2021). Assignment Flow For Order-Constrained OCT Segmentation. Int J Computer Vision. 129

Sitenko, D, Boll, B and Schnörr, C (2021). Assignment Flows and Nonlocal PDEs on Graphs. GCPR, in press

Gonzalez-Alvarado, D, Zeilmann, A and Schnörr, C (2021). Assignment Flows and Nonlocal PDEs on Graphs. GCPR, in press

Haußmann, (2021). Bayesian Neural Networks for Probabilistic Machine Learning. Heidelberg University

Blattmann, A, Milbich, T, Dorkenwald, M and Ommer, B (2021). Behavior-Driven Synthesis of Human Dynamics. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://arxiv.org/abs/2103.04677

Milbich, T, Roth, K, Sinha, S, Schmidt, L, Ghassemi, M and Ommer, B (2021). Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning. https://arxiv.org/abs/2107.09562

Ruiz, A (2021). Deep K-Segments: A Generalization Of K-Means. Heidelberg University

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

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

Fita, E, Damrich, S and Hamprecht, F A (2021). Directed Probabilistic Watershed. NeurIPS. Proceedings. 34

Technical Report (957.78 KB)

Kandemir, M, Agkül, A, Haußmann, M and Ünal, G (2021). Evidential Turing Processes. arXiv preprint. https://arxiv.org/abs/2106.01216

Jenner, E, Fita, E and Hamprecht, F A (2021). Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice. ICCV. Proceedings. 4602-4611

Technical Report (1.1 MB)

Rombach, R, Esser, P and Ommer, B (2021). Geometry-Free View Synthesis: Transformers and no 3D Priors. Proceedings of the Intl. Conf. on Computer Vision (ICCV). https://arxiv.org/abs/2104.07652

Jahn, M, Rombach, R and Ommer, B (2021). High-Resolution Complex Scene Synthesis with Transformers. CVPR 2021, AI for Content Creation Workshop

Esser, P, Rombach, R, Blattmann, A and Ommer, B (2021). ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis. https://arxiv.org/abs/2108.08827

Sanakoyeu, A, Ma, P, Tschernezki, V and Ommer, B (2021). Improving Deep Metric Learning by Divide and Conquer. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). https://arxiv.org/abs/2109.04003

Schütz, L M, Louveaux, M, Vilches-Barro, A, Bouziri, S, Cerrone, L, Wolny, A, Kreshuk, A, Hamprecht, F A and Maizel, A (2021). Integration of Cell Growth and Asymmetric Division during Lateral Root Initiation in Arabidopsis thaliana. Plant and Cell Physiology. 62 1269-1279

Blattmann, A, Milbich, T, Dorkenwald, M and Ommer, B (2021). iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis. Proceedings of the International Conference on Computer Vision (ICCV). https://arxiv.org/abs/2107.02790

Andersson, A, Diego, F, Hamprecht, F A and Wählby, C (2021). Istdeco: In Situ Transcriptomics Decoding By Deconvolution. bioRxiv

Afifi, M, Derpanis, K G, Ommer, B and Brown, M S (2021). Learning Multi-Scale Photo Exposure Correction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://arxiv.org/abs/2003.11596

Haußmann, M, Gerwinn, S, Look, A, Rakitsch, B and Kandemir, M (2021). Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes. International Conference on Artificial Intelligence and Statistics . PMLR 130 478-486

Pape, C, 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 (2021). Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera. BioEssays. 43

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

Technical Report (1.83 MB)

Kotovenko, D, Wright, M, Heimbrecht, A and Ommer, B (2021). Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://compvis.github.io/brushstroke-parameterized-style-transfer/

Roth, K, Milbich, T, Ommer, B, Cohen, J Paul and Ghassemi, M (2021). S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning. Proceedings of International Conference on Machine Learning (ICML). https://arxiv.org/abs/2009.08348

Pape, C (2021). Scalable Instance Segmentation for Microscopy. Heidelberg University

Arlt, H, 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 (2021). Seipin forms a flexible cage at lipid droplet formation sites. bioRxiv

Islam, M Amirul, Kowal, M, Esser, P, Jia, S, Ommer, B, Derpanis, K G and Bruce, N (2021). Shape or Texture: Understanding Discriminative Features in CNNs. International Conference on Learning Representations (ICLR)

Dorkenwald, M, Milbich, T, Blattmann, A, Rombach, R, Derpanis, K G and Ommer, B (2021). Stochastic Image-to-Video Synthesis usin cINNs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Esser, P, Rombach, R and Ommer, B (2021). Taming Transformers for High-Resolution Image Synthesis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://arxiv.org/abs/2012.09841

Lang, S and Ommer, B (2021). Transforming Information Into Knowledge: How Computational Methods Reshape Art History. Digital Humanities Quaterly (DHQ). 15

Lang, S and Ommer, B (2021). Transforming Information Into Knowledge: How Computational Methods Reshape Art History. Digital Humanities Quaterly (DHQ). 15. http://digitalhumanities.org/dhq/vol/15/3/000560/000560.html

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

Damrich, S and Hamprecht, F A (2021). On UMAP's True Loss Function. NeurIPS. Proceedings. 34

Technical Report (1.87 MB)

Bellagente, M, Haußmann, M, Luchmann, M and Plehn, T (2021). Understanding Event-Generation Networks via Uncertainties. arXiv preprint. https://arxiv.org/abs/2104.04543v1

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