Publications

2018
Sanakoyeu, A, Kotovenko, D, Lang, S and Ommer, B (2018). A Style-Aware Content Loss for Real-time HD Style Transfer. Proceedings of the European Conference on Computer Vision (ECCV), https://compvis.github.io/adaptive-style-transfer/
Esser, P, Sutter, E and Ommer, B (2018). A Variational U-Net for Conditional Appearance and Shape Generation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://compvis.github.io/vunet/, Project Page
Lang, S and Ommer, B (2018). Attesting Similarity: Supporting the Organization and Study of Art Image Collections with Computer Vision. Digital Scholarship in the Humanities, Oxford University Press. 33 845-856, https://academic.oup.com/dsh/advance-article/doi/10.1093/llc/fqy006/4964861?gues...
H Alhaija, A, Mustikovela, S K, Mescheder, A, Geiger, C and Rother, C (2018). Augmented Reality Meets Computer Vision Efficient Data Generation for Urban Driving Scenes. IJCV. 1-12PDF icon Technical Report (3.83 MB)
Sayed, N, Brattoli, B and Ommer, B (2018). Cross and Learn: Cross-Modal Self-Supervision. Oral at German Conference on Pattern Recognition (GCPR). Stuttgart, Germany. https://arxiv.org/abs/1811.03879v1PDF icon Article (891.47 KB)PDF icon Oral slides (9.17 MB)
Cerrone, L (2018). Deep End-To-End Learning Of A Diffusion Process For Seeded Image Segmentation. Heidelberg University
Sanakoyeu, A, Bautista, M and Ommer, B (2018). Deep Unsupervised Learning of Visual Similarities. Pattern Recognition. 78. https://authors.elsevier.com/a/1WXUt77nKSb25 PDF icon PDF (8.35 MB), Arxiv Paper
Weilbach, C (2018). Dictionary Learning With Bayesian Gans For Few-Shot Classification. Heidelberg University
Wahl, A S, Erlebach, E, Brattoli, B, Büchler, U, Kaiser, J, Ineichen, V B, Mosberger, A C, Schneeberger, S, Imobersteg, S, Wieckhorst, M, Stirn, M, Schroeter, A, Ommer, B and Schwab, M E (2018). Early reduced behavioral activity induced by large strokes affects the efficiency of enriched environment in rats. Sage Journals. Journal of Cerebral Blood Flow & Metabolism. http://journals.sagepub.com/doi/abs/10.1177/0271678X18777661PDF icon 0271678x18777661.pdf (770.87 KB)
Draxler, F, Veschgini, K, Salmhofer, M and Hamprecht, F (2018). Essentially No Barriers in Neural Network Energy Landscape. ICML. Proceedings. 80 1308--1317PDF icon Technical Report (685.93 KB)
Storath, M and Weinmann, A (2018). Fast median filtering for phase or orientation data. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40 639–652PDF icon Technical Report (7.32 MB), Circle Median Filter
Rathke, F and Schnörr, C (2018). Fast Multivariate Log-Concave Density Estimation. preprint: ArXiv. https://arxiv.org/pdf/1805.07272.pdfPDF icon Technical Report (3.54 MB)
Fortun, D, Storath, M, Rickert, D, Weinmann, A and Unser, M (2018). Fast Piecewise-Affine Motion Estimation Without Segmentation. IEEE Transactions on Image Processing. 27 5612 - 5624
Hühnerbein, R, Savarino, F, Aström, F and Schnörr, C (2018). Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment. SIAM Journal on Imaging Sciences. 11 1317-1362PDF icon Technical Report (2.62 MB)
Büchler, U, Brattoli, B and Ommer, B (2018). Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning. Proceedings of the European Conference on Computer Vision (ECCV). (UB and BB contributed equally), Munich, GermanyPDF icon Article (5.34 MB)
Schimmel, F (2018). Learnability Of Approximated Graph Cut Segmentation. Heidelberg University
Weiler, M, Hamprecht, F A and Storath, M (2018). Learning Steerable Filters for Rotation Equivariant CNNs. CVPR
Ghori, O, Mackowiak, R, Bautista, M, Beuter, N, Drumond, L, Diego, F and Ommer, B (2018). Learning to Forecast Pedestrian Intention from Pose Dynamics. Intelligent Vehicles, IEEE, 2018
Erb, W, Weinmann, A, Ahlborg, M, Brandt, C, Bringout, G, Buzug, T M, Frikel, J, Kaethner, C, Knopp, T, März, T, Möddel, M, Storath, M and Weber, A (2018). Mathematical Analysis of the 1D Model and Reconstruction Schemes for Magnetic Particle Imaging. Inverse Problems. 34
Kiechle, M, Storath, M, Weinmann, A and Kleinsteuber, M (2018). Model-based learning of local image features for unsupervised texture segmentation. IEEE Transactions on Image Processing. 27 1994-2007
Beier, T (2018). Multicut Algorithms for Neurite Segmentation. Heidelberg University
Lang, S and Ommer, B (2018). Reconstructing Histories: Analyzing Exhibition Photographs with Computational Methods. Arts, Computational Aesthetics. 7, 64PDF icon arts-07-00064.pdf (4.6 MB), Link
Lang, S and Ommer, B (2018). Reflecting on How Artworks Are Processed and Analyzed by Computer Vision. European Conference on Computer Vision (ECCV). Springer
Kawetzki, D (2018). Semantic Segmentation Of Urban Scenes Using Deep Learning. Heidelberg University
Draxler, F (2018). The Energy Landscape Of Deep Neural Networks. Heidelberg University
Wolf, S, Pape, C, Bailoni, A, Rahaman, N, Kreshuk, A, Köthe, U and Hamprecht, F A (2018). The Mutex Watershed: Efficient, Parameter-Free Image Partitioning. ECCV. Proceedings, in press
Bredies, K, Holler, M, Storath, M and Weinmann, A (2018). Total Generalized Variation for Manifold-valued Data. SIAM Journal on Imaging Sciences. 11 1785 - 1848
Esser, P, Haux, J, Milbich, T and Ommer, B (2018). Towards Learning a Realistic Rendering of Human Behavior. European Conference on Computer Vision (HBUGEN), Project Page
Schilling, H, Diebold, M, Rother, C and Jähne, B (2018). Trust your Model: Light Field Depth Estimation with inline Occlusion Handling. CVPR. Proceedings, in pressPDF icon Technical Report (5.46 MB)
Blum, O, Brattoli, B and Ommer, B (2018). X-GAN: Improving Generative Adversarial Networks with ConveX Combinations. Oral at German Conference on Pattern Recognition (GCPR). Stuttgart, GermanyPDF icon Article (6.65 MB)PDF icon Supplementary material (7.96 MB)PDF icon Oral slides (14.96 MB)

2017
Vianello, A, Manfredi, G, Diebold, M and Jähne, B (2017). 3D reconstruction by a combined structure tensor and Hough transform light field approach. tm - Technisches Messen
Hehn, T (2017). A Probabilistic Approach To Learn Complex Differentiable Split Functions In Decision Trees Using Gradient Ascent. Heidelberg University
Kandemir, M, Hamprecht, F A, Wojek, C and Schmidt, U (2017). Active machine learning for training an event classification. Patent, Patent Number WO2017032775 A1
Ulman, V, Maška, M, Magnusson, K E G, Ronneberger, O, Haubold, C, Harder, N, Matula, P, Matula, P, Svoboda, D, Radojevic, M, Smal, I, Rohr, K, Jaldén, J, Blau, H M, Dzyubachyk, O, Lelieveldt, B, Xiao, P, Li, Y, Cho, S - Y, Dufour, A, Olivo-Marin, J C, Reyes-Aldasoro, C C, Solis-Lemus, J A, Bensch, R, Brox, T, Stegmaier, J, Mikut, R, Wolf, S, Hamprecht, F A, Esteves, T, Quelhas, P, Demirel, Ö, Malström, L, Jug, F, Tomančák, P, Meijering, E, Muñoz-Barrutia, A, Kozubek, M and Ortiz-de-Solorzano, C (2017). An Objective Comparison of Cell Tracking Algorithms. Nature Methods. 14 1141-1152PDF icon Technical Report (4.24 MB)
Brosowsky, M (2017). Cluster Resolving For Animal Tracking: Multi Hypotheses Tracking With Part Based Model For Object Hypotheses Generation And Pose Estimation. University of Heidelberg
Peter, S, Diego, F, Hamprecht, F A and Nadler, B (2017). Cost-efficient Gradient Boosting. NIPS, poster
Ufer, N and Ommer, B (2017). Deep Semantic Feature Matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)PDF icon article (8.88 MB)
Bautista, M, Sanakoyeu, A and Ommer, B (2017). Deep Unsupervised Similarity Learning using Partially Ordered Sets. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)PDF icon deep_unsupervised_similarity_learning_cvpr_2017_paper.pdf (905.82 KB)
Haubold, C, Uhlmann, V, Unser, M and Hamprecht, F A (2017). Diverse M-best Solutions by Dynamic Programming. GCPR. Proceedings. Springer. LNCS 10496 255-267
Uhlmann, V, Haubold, C, Hamprecht, F A and Unser, M (2017). Diverse Shortest Paths for Bioimage Analysis. Bioinformatics. 1-3

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