Publications

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Author Title [ Type(Desc)] Year
Conference Proceedings
A. Vianello, Manfredi, G., Diebold, M., and Jähne, B., 3D Reconstruction by a Combined Structure Tensor and Hough Transform Light-Field Approach, Forum Bildverarbeitung. 2016.
B. Jähne, Air-Water Gas Transfer --- Selected Papers from the Third International Symposium on Air-Water Gas Transfer. AEON, 1995.
E. Fita, Damrich, S., and Hamprecht, F. A., The Algebraic Path Problem for Graph Metrics, 39th International Conference on Machine Learning, PMLR. Proceedings , vol. 162. pp. 19178-19204, 2022.
D. Sitenko, Boll, B., and Schnörr, C., Assignment Flows and Nonlocal PDEs on Graphs, GCPR, in press. 2021.
D. Gonzalez-Alvarado, Zeilmann, A., and Schnörr, C., Assignment Flows and Nonlocal PDEs on Graphs, GCPR, in press. 2021.
M. Kandemir, Asymmetric transfer learning with deep Gaussian processes, ICML. Proceedings. pp. 730-738, 2015.PDF icon Technical Report (570.95 KB)
M. Haußmann, Gerwinn, S., and Kandemir, M., Bayesian Evidential Deep Learning with PAC Regularization , 3rd Symposium on Advances in Approximate Bayesian Inference . 2020.
A. Blattmann, Milbich, T., Dorkenwald, M., and Ommer, B., Behavior-Driven Synthesis of Human Dynamics, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2021.
A. Monroy and Ommer, B., Beyond Bounding-Boxes: Learning Object Shape by Model-driven Grouping, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 7574. Springer, p. 582--595, 2012.PDF icon Technical Report (1.58 MB)
M. Kandemir and Hamprecht, F. A., Cell event detection in phase-contrast microscopy sequences from few annotations, MICCAI. Proceedings, vol. LNCS 9351. Springer, pp. 316-323, 2015.PDF icon Technical Report (564.69 KB)
S. Peter, Diego, F., Hamprecht, F. A., and Nadler, B., Cost-efficient Gradient Boosting, NIPS, poster. 2017.
S. Lang and Ommer, B., Das Objekt jenseits der Digitalisierung, Das digitale Objekt, vol. 7. 2020.PDF icon lang_ommer_digitalhumanities_2020_.pdf (599.56 KB)
M. Haußmann, Hamprecht, F. A., and Kandemir, M., Deep Active Learning with Adaptive Acquisition, IJCAI. Proceedings. pp. 2470-2476, 2019.PDF icon Technical Report (137.6 KB)
M. Kandemir and Hamprecht, F. A., The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors, NIPS. Proceedings, vol. 44. pp. 145-159, 2015.PDF icon Supplementary Material (223.39 KB)PDF icon Technical Report (2.58 MB)
E. Fita, Damrich, S., and Hamprecht, F. A., Directed Probabilistic Watershed, NeurIPS. Proceedings, vol. 34. 2021.PDF icon Technical Report (957.78 KB)
E. Kirschbaum, Bailoni, A., and Hamprecht, F. A., DISCo: Deep Learning, Instance Segmentation, and Correlations for Cell Segmentation in Calcium Imaging, MICCAI. Proceedings. pp. 151-162, 2020.
P. Esser, Rombach, R., and Ommer, B., A Disentangling Invertible Interpretation Network for Explaining Latent Representations, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2020.PDF icon Article (13.07 MB)
T. Milbich, Roth, K., Bharadhwaj, H., Sinha, S., Bengio, Y., Ommer, B., and Cohen, J. Paul, DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning, IEEE European Conference on Computer Vision (ECCV). 2020.
C. Haubold, Uhlmann, V., Unser, M., and Hamprecht, F. A., Diverse M-best Solutions by Dynamic Programming, GCPR. Proceedings, vol. LNCS 10496. Springer, pp. 255-267, 2017.
A. Sanakoyeu, Tschernezki, V., Büchler, U., and Ommer, B., Divide and Conquer the Embedding Space for Metric Learning, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019.
T. Beier, Andres, B., Köthe, U., and Hamprecht, F. A., An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem, ECCV. Proceedings, vol. LNCS 9906. Springer, pp. 715-730, 2016.PDF icon Technical Report (4.89 MB)
L. Cerrone, Zeilmann, A., and Hamprecht, F. A., End-to-End Learned Random Walker for Seeded Image Segmentation, CVPR. Proceedings. pp. 12559-12568, 2019.
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.PDF icon Technical Report (1.4 MB)
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.PDF icon Technical Report (685.93 KB)
E. Jenner, Fita, E., and Hamprecht, F. A., Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice, ICCV. Proceedings. pp. 4602-4611, 2021.PDF icon Technical Report (1.1 MB)
H. Schilling, Diebold, M., Gutsche, M., Aziz-Ahmad, H., and Jähne, B., A fractal calibration pattern for improved camera calibration, Forum Bildverarbeitung. 2016.
M. von Borstel, Kandemir, M., Schmidt, P., Rao, M., Rajamani, K., and Hamprecht, F. A., Gaussian process density counting from weak supervision, ECCV. Proceedings, vol. LNCS 9905. Springer, pp. 365-380 , 2016.PDF icon Technical Report (1.71 MB)
C. Haubold, Ales, J., Wolf, S., and Hamprecht, F. A., A Generalized Successive Shortest Paths Solver for Tracking Dividing Targets, ECCV. Proceedings, vol. LNCS 9911. Springer, pp. 566-582, 2016.PDF icon Technical Report (1.18 MB)
H. Abu Alhaija, Mustikovela, S. K., Geiger, A., and Rother, C., Geometric Image Synthesis, ACCV. Proceedings, in press. 2018.PDF icon Technical Report (1.83 MB)
P. Esser, Rombach, R., Blattmann, A., and Ommer, B., ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis. 2021.

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