All Publications

2018

Hodaň, T, 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 (2018). BOP: Benchmark for 6D object pose estimation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11214 LNCS 19–35. http://arxiv.org/abs/1808.08319
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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11208 LNCS 571–587. http://arxiv.org/abs/1904.12654
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
Abu Alhaija, H, Mustikovela, S Karthik, Mescheder, L, Geiger, A and Rother, C (2018). Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes. International Journal of Computer Vision. 126 961–972. http://arxiv.org/abs/1708.01566
Abu Alhaija, H, Mustikovela, S Karthik, Mescheder, L, Geiger, A and Rother, C (2018). Augmented Reality Meets Computer Vision. International Journal of Computer Vision. In press 1–13
Shekhovtsov, A, Swoboda, P and Savchynskyy, B (2018). Maximum Persistency via Iterative Relaxed Inference in Graphical Models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40 1668–1682. http://www.icg.tugraz.at/
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)
Weiler, M, Hamprecht, F A and Storath, M (2018). Learning Steerable Filters for Rotation Equivariant CNNs. CVPR
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
(2018). A Supplementary Material Cereals-Cost-Effective Region-Based Active Learning For Semantic Segmentation
Schimmel, F (2018). Learnability Of Approximated Graph Cut Segmentation. Heidelberg University
Kawetzki, D (2018). Semantic Segmentation Of Urban Scenes Using Deep Learning. Heidelberg University
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
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, 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) (short Oral). https://compvis.github.io/vunet/
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
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)
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) (Oral)
Rahaman, N, Arpit, D, Baratin, A, Draxler, F, Lin, M, Hamprecht, F A, Bengio, Y and Courville, A (2018). On the spectral bias of deep neural networks. arXiv preprint arXiv:1806.08734
Zern, A, Zisler, M, Aström, F, Petra, S and Schnörr, C (2018). Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment. GCPR. Proceedings. 698-713PDF icon Technical Report (5.23 MB)
Hosseini Jafari, O, Mustikovela, S K, Pertsch, K, Brachmann, E and Rother, C (2018). iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects. ACCV. Proceedings, in pressPDF icon Technical Report (3.28 MB)
Abu Alhaija, H, Mustikovela, S K, Geiger, A and Rother, C (2018). Geometric Image Synthesis. ACCV. Proceedings, in pressPDF icon Technical Report (1.83 MB)
Hehn, T and Hamprecht, F A (2018). End-to-end Learning of Deterministic Decision Trees. German Conference on Pattern Recognition. Proceedings. Springer. LNCS 11269 612-627PDF icon Technical Report (1.4 MB)
Bell, P and Ommer, B (2018). Computer Vision und Kunstgeschichte — Dialog zweier Bildwissenschaften. Computing Art Reader: Einführung in die digitale Kunstgeschichte, P. Kuroczyński et al. (ed.)PDF icon 413-17-83318-2-10-20181210.pdf (2.98 MB)
Lang, S and Ommer, B (2018). Reflecting on How Artworks Are Processed and Analyzed by Computer Vision. European Conference on Computer Vision (ECCV - VISART). Springer
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)
Esser, P, Haux, J, Milbich, T and Ommer, B (2018). Towards Learning a Realistic Rendering of Human Behavior. European Conference on Computer Vision (ECCV - HBUGEN)
Blum, O, Brattoli, B and Ommer, B (2018). X-GAN: Improving Generative Adversarial Networks with ConveX Combinations. German Conference on Pattern Recognition (GCPR) (Oral). Stuttgart, GermanyPDF icon Article (6.65 MB)PDF icon Supplementary material (7.96 MB)PDF icon Oral slides (14.96 MB)
Sayed, N, Brattoli, B and Ommer, B (2018). Cross and Learn: Cross-Modal Self-Supervision. German Conference on Pattern Recognition (GCPR) (Oral). Stuttgart, Germany. https://arxiv.org/abs/1811.03879v1PDF icon Article (891.47 KB)PDF icon Oral slides (9.17 MB)
Draxler, F, Veschgini, K, Salmhofer, M and Hamprecht, F A (2018). Essentially No Barriers in Neural Network Energy Landscape. ICML. Proceedings. 80 1308--1317PDF icon Technical Report (685.93 KB)
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)
Schilling, H, Diebold, M, Rother, C and Jähne, B (2018). Trust your Model: Light Field Depth Estimation with inline Occlusion Handling. CVPR. ProceedingsPDF icon Technical Report (5.46 MB)
Abu Alhaija, H, 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)
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)
Draxler, F (2018). The Energy Landscape Of Deep Neural Networks. Heidelberg University
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)PDF icon buechler_eccv18_poster.pdf (1.65 MB)
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. Springer. 571-587
Beier, T (2018). Multicut Algorithms for Neurite Segmentation. Heidelberg University

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