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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.

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.

S. Xiao, “Tracking Dividing Cells Using Spatio-Temporal Embeddings”, Heidelberg University, 2019.

A. Zern, Zisler, M., Petra, S., and Schnörr, C., “Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment”, preprint: arXiv, 2019.

M. Zisler, Zern, A., Petra, S., and Schnörr, C., “Unsupervised Labeling by Geometric and Spatially Regularized Self-Assignment”, in Proc. SSVM, 2019.

D. Lorenz, Bereska, L., Milbich, T., and Ommer, B., “Unsupervised Part-Based Disentangling of Object Shape and Appearance”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Oral + Best paper finalist: top 45 / 5160 submissions), 2019.

P. Esser, Haux, J., and Ommer, B., “Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis”, in Proceedings of the Intl. Conf. on Computer Vision (ICCV), 2019.

D. Kotovenko, Sanakoyeu, A., Lang, S., Ma, P., and Ommer, B., “Using a Transformation Content Block For Image Style Transfer”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019.

F. Savarino and Schnörr, C., “A Variational Perspective on the Assignment Flow”, in Proc. SSVM, 2019.

N. Ufer, Lui, K. To, Schwarz, K., Warkentin, P., and Ommer, B., “Weakly Supervised Learning of Dense SemanticCorrespondences and Segmentation”, in German Conference on Pattern Recognition (GCPR), 2019.

article (6.1 MB)

N. Pandey, “Weakly Supervised Semantic Segmentation”, Heidelberg University, 2019.

2018

M. Bopp, “Air-Flow and Stress Partitioning over Wind Waves in a Linear Wind-Wave Facility”, vol. Dissertation. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg, Heidelberg, 2018.

S. Lang and Ommer, B., “Attesting Similarity: Supporting the Organization and Study of Art Image Collections with Computer Vision”, Digital Scholarship in the Humanities, Oxford University Press, vol. 33, no. 4, pp. 845-856, 2018.

H. Abu Alhaija, Mustikovela, S. Karthik, Mescheder, L., Geiger, A., and Rother, C., “Augmented Reality Meets Computer Vision”, International Journal of Computer Vision, vol. In press, pp. 1–13, 2018.

H. Abu Alhaija, Mustikovela, S. K., Mescheder, A., Geiger, C., and Rother, C., “Augmented Reality Meets Computer Vision Efficient Data Generation for Urban Driving Scenes”, IJCV, pp. 1-12, 2018.

Technical Report (3.83 MB)

H. Abu Alhaija, Mustikovela, S. Karthik, Mescheder, L., Geiger, A., and Rother, C., “Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes”, International Journal of Computer Vision, vol. 126, pp. 961–972, 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.

P. Bell and Ommer, B., “Computer Vision und Kunstgeschichte — Dialog zweier Bildwissenschaften”, in Computing Art Reader: Einführung in die digitale Kunstgeschichte, P. Kuroczyński et al. (ed.), 2018.

413-17-83318-2-10-20181210.pdf (2.98 MB)

A. Arnab, Zheng, S., Jayasumana, S., Romera-paredes, B., Kirillov, A., Savchynskyy, B., Rother, C., Kahl, F., and Torr, P., “Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation”, Cvpr, vol. XX, pp. 1–15, 2018.

N. Sayed, Brattoli, B., and Ommer, B., “Cross and Learn: Cross-Modal Self-Supervision”, in German Conference on Pattern Recognition (GCPR) (Oral), Stuttgart, Germany, 2018.

Article (891.47 KB)

Oral slides (9.17 MB)

L. Cerrone, “Deep End-to-End Learning of a Diffusion Process for Seeded Image Segmentation”, Heidelberg University, 2018.

A. Sanakoyeu, Bautista, M., and Ommer, B., “Deep Unsupervised Learning of Visual Similarities”, Pattern Recognition, vol. 78, 2018.

PDF (8.35 MB)

C. Weilbach, “Dictionary Learning with Bayesian GANs for Few-Shot Classification”, Heidelberg University, 2018.

A. - S. Wahl, 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., “Early reduced behavioral activity induced by large strokes affects the efficiency of enriched environment in rats”, Sage Journals, vol. Journal of Cerebral Blood Flow & Metabolism, 2018.

0271678x18777661.pdf (770.87 KB)

T. Hehn and Hamprecht, F. A., “End-to-end Learning of Deterministic Decision Trees”, German Conference on Pattern Recognition. Proceedings, vol. LNCS 11269. Springer, pp. 612-627, 2018.

Technical Report (1.4 MB)

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)

S. Haller, Swoboda, P., and Savchynskyy, B., “Exact MAP-Inference by Confining Combinatorial Search With LP Relaxation”, in Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, 2018.

2018-02-02_aaai_dense_combilp.pdf (325.08 KB)

M. Storath and Weinmann, A., “Fast median filtering for phase or orientation data”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 3, pp. 639–652, 2018.

Technical Report (7.32 MB)

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