Publications

Search

Show only items where

Author

Type

Term

Year

Keyword

Export 1965 results:

Author Title [ Type

] Year

Conference Proceedings

S. Wolf, Hamprecht, F. A., and Funke, J., “Inpainting Networks Learn to Separate Cells in Microscopy Images”, BMCV. 2020.

Technical Report (357.23 KB)

O. Hosseini Jafari, Mustikovela, S. K., Pertsch, K., Brachmann, E., and Rother, C., “iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects”, ACCV. Proceedings, in press. 2018.

Technical Report (3.28 MB)

C. Schnörr, Ed., “Künstliche Intelligenz: Special Issue on Medical Computer Vision”, vol. 3. 2000.

M. Schiegg, Diego, F., and Hamprecht, F. A., “Learning Diverse Models: The Coulomb Structured Support Vector Machine”, ECCV. Proceedings, vol. LNCS 9907. Springer, pp. 585-599, 2016.

Technical Report (2.54 MB)

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

M. Weiler, Hamprecht, F. A., and Storath, M., “Learning Steerable Filters for Rotation Equivariant CNNs”, CVPR. Proceedings. pp. 849-858, 2018.

Technical Report (1.35 MB)

M. Bautista, Fuchs, P., and Ommer, B., “Learning Where to Drive by Watching Others”, Proceedings of the German Conference Pattern Recognition, vol. 1. Springer-Verlag, Basel, 2017.

E. Kirschbaum, Haußmann, M., Wolf, S., Sonntag, H., Schneider, J., Elzoheiry, S., Kann, O., Durstewitz, D., and Hamprecht, F. A., “LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos”, ICLR. Proceedings. 2019.

M. Diebold, Blum, O., Gutsche, M., Wanner, S., Garbe, C. S., Baker, H., and Jähne, B., “Light-field camera design for high-accuracy depth estimation”, Videometrics, Range Imaging, and Applications XIII. 2015.

F. Rathke, Desana, M., and Schnörr, C., “Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans”, MICCAI. Proceedings. pp. 177-184, 2017.

Technical Report (4.79 MB)

R. Rombach, Esser, P., and Ommer, B., “Making Sense of CNNs: Interpreting Deep Representations & Their Invariances with INNs”, IEEE European Conference on Computer Vision (ECCV). 2020.

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

Technical Report (1.83 MB)

S. Wolf, Pape, C., Bailoni, A., Rahaman, N., Kreshuk, A., Köthe, U., and Hamprecht, F. A., “The Mutex Watershed: Efficient, Parameter-Free Image Partitioning”, ECCV. Proceedings. Springer, pp. 571-587, 2018.

R. Rombach, Esser, P., and Ommer, B., “Network-to-Network Translation with Conditional Invertible Neural Networks”, Neural Information Processing Systems (NeurIPS) (Oral). 2020.

N. Ufer, Lang, S., and Ommer, B., “Object Retrieval and Localization in Large Art Collections Using Deep Multi-style Feature Fusion and Iterative Voting”, IEEE European Conference on Computer Vision (ECCV), VISART Workshop . 2020.

Paper (1.03 MB)

F. A. Hamprecht, Schnörr, C., and Jähne, B., Eds., “Pattern Recognition – 29th DAGM Symposium”, LCNS, vol. 4713. Springer, 2007.

C. Schnörr and Jähne, B., “Pattern Recognition, 29th DAGM Symposium, Heidelberg, Germany, September 12-14”, vol. 4713. Springer, 2007.

E. Bodnariuc, Schiffner, M. F., Petra, S., and Schnörr, C., “Plane Wave Acoustic Superposition for Fast Ultrasound Imaging”, International Ultrasonics Symposium. 2016.

S. Haller, Prakash, M., Hutschenreiter, L., Pietzsch, T., Rother, C., Jug, F., Swoboda, P., and Savchynskyy, B., “A Primal-Dual Solver for Large-Scale Tracking-by-Assignment”, AISTATS 2020. 2020.

PDF (1.04 MB)

A. Bailoni, Pape, C., Wolf, S., Kreshuk, A., and Hamprecht, F. A., “Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks”, GCPR, vol. 12544. Springer, pp. 331-344, 2020.

S. Lang and Ommer, B., “Reflecting on How Artworks Are Processed and Analyzed by Computer Vision”, European Conference on Computer Vision (ECCV - VISART). Springer, 2018.

D. Kotovenko, Wright, M., Heimbrecht, A., and Ommer, B., “Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2021.

K. Roth, Milbich, T., Sinha, S., Gupta, P., Ommer, B., and Cohen, J. Paul, “Revisiting Training Strategies and Generalization Performance in Deep Metric Learning”, International Conference on Machine Learning (ICML). 2020.

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.

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)

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.

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.

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.

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.

F. Diego and Hamprecht, F. A., “Structured Regression Gradient Boosting”, CVPR. Proceedings. pp. 1459-1467, 2016.

Technical Report (3.97 MB)

P. Esser, Rombach, R., and Ommer, B., “Taming Transformers for High-Resolution Image Synthesis”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2021.

H. Schilling, Diebold, M., Rother, C., and Jähne, B., “Trust your Model: Light Field Depth Estimation with inline Occlusion Handling”, CVPR. Proceedings. 2018.

Technical Report (5.46 MB)

S. Damrich and Hamprecht, F. A., “On UMAP's True Loss Function”, NeurIPS. Proceedings, vol. 34. 2021.

Technical Report (1.87 MB)

A. Blattmann, Milbich, T., Dorkenwald, M., and Ommer, B., “Understanding Object Dynamics for Interactive Image-to-Video Synthesis”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2021.

A. Zern, Zisler, M., Aström, F., Petra, S., and Schnörr, C., “Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment”, GCPR. Proceedings. pp. 698-713, 2018.

Technical Report (5.23 MB)

Pages