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Conference Paper

P. Esser, Rombach, R., and Ommer, B., “A Note on Data Biases in Generative Models”, in NeurIPS 2020 Workshop on Machine Learning for Creativity and Design, 2020.

C. Sigg, Fischer, B., Ommer, B., Roth, V., and Buhmann, J. M., “Nonnegative CCA for Audiovisual Source Separation”, in International Workshop on Machine Learning for Signal Processing, 2007, p. 253--258.

Technical Report (1.27 MB)

R. Rombach, Esser, P., and Ommer, B., “Network Fusion for Content Creation with Conditional INNs”, in CVPRW 2020 (AI for Content Creation), 2020.

B. Ommer and Malik, J., “Multi-scale Object Detection by Clustering Lines”, in Proceedings of the IEEE International Conference on Computer Vision, 2009, p. 484--491.

Technical Report (3.18 MB)

A. Monroy, Bell, P., and Ommer, B., “A Morphometric Approach to Reception Analysis of Premodern Art”, in Scientific Computing & Cultural Heritage, 2013.

Technical Report (17.75 MB)

B. Brattoli, Roth, K., and Ommer, B., “MIC: Mining Interclass Characteristics for Improved Metric Learning”, in Proceedings of the Intl. Conf. on Computer Vision (ICCV), 2019.

A. Eigenstetter, Yarlagadda, P., and Ommer, B., “Max-Margin Regularization for Reducing Accidentalness in Chamfer Matching”, in Proceedins of the Aian Conference on Computer Vision, 2012, p. 152--163.

Technical Report (7.31 MB)

B. Brattoli, Büchler, U., Wahl, A. - S., Schwab, M. E., and Ommer, B., “LSTM Self-Supervision for Detailed Behavior Analysis”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

Article (8.75 MB)

B. Antic, Milbich, T., and Ommer, B., “Less is More: Video Trimming for Action Recognition”, in Proceedings of the IEEE International Conference on Computer Vision, Workshop on Understanding Human Activities: Context and Interaction, 2013, p. 515--521.

Technical Report (984.89 KB)

B. Ommer, Sauter, M., and M., B. J., “Learning Top-Down Grouping of Compositional Hierarchies for Recognition”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Perceptual Organization in Computer Vision, 2006, p. 194--194.

Technical Report (358.98 KB)

O. Ghori, Mackowiak, R., Bautista, M., Beuter, N., Drumond, L., Diego, F., and Ommer, B., “Learning to Forecast Pedestrian Intention from Pose Dynamics”, in Intelligent Vehicles, IEEE, 2018, 2018.

B. Ommer and Buhmann, J. M., “Learning the Compositional Nature of Visual Objects”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2007, p. 1--8.

Technical Report (2.78 MB)

M. Afifi, Derpanis, K. G., Ommer, B., and Brown, M. S., “Learning Multi-Scale Photo Exposure Correction”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

B. Antic and Ommer, B., “Learning Latent Constituents for Recognition of Group Activities in Video”, in Proceedings of the European Conference on Computer Vision (ECCV) (Oral), 2014, p. 33--47.

Technical Report (4.54 MB)

P. Yarlagadda, Eigenstetter, A., and Ommer, B., “Learning Discriminative Chamfer Regularization”, in BMVC, 2012, p. 1--11.

B. Ommer and Buhmann, J. M., “Learning Compositional Categorization Models”, in Proceedings of the European Conference on Computer Vision, 2006, vol. 3953, p. 316--329.

Technical Report (1.35 MB)

A. Blattmann, Milbich, T., Dorkenwald, M., and Ommer, B., “iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis”, in Proceedings of the International Conference on Computer Vision (ICCV), 2021.

U. Büchler, Brattoli, B., and Ommer, B., “Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning”, in Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 2018.

Article (5.34 MB)

buechler_eccv18_poster.pdf (1.65 MB)

M. Jahn, Rombach, R., and Ommer, B., “High-Resolution Complex Scene Synthesis with Transformers”, in CVPR 2021, AI for Content Creation Workshop, 2021.

R. Rombach, Esser, P., and Ommer, B., “Geometry-Free View Synthesis: Transformers and no 3D Priors”, in Proceedings of the Intl. Conf. on Computer Vision (ICCV), 2021.

P. Yarlagadda and Ommer, B., “From Meaningful Contours to Discriminative Object Shape”, in Proceedings of the European Conference on Computer Vision, 2012, vol. 7572, p. 766--779.

Technical Report (4.58 MB)

V. Roth and Ommer, B., “Exploiting Low-level Image Segmentation for Object Recognition”, in Pattern Recognition, Symposium of the DAGM, 2006, vol. 4174, p. 11--20.

Technical Report (473.84 KB)

M. Kandemir, Rubio, J. C., Schmidt, U., Wojek, C., Welbl, J., Ommer, B., and Hamprecht, F. A., “Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures”, in Medical Image Computing and Computer-Assisted Intervention, 2014, p. 154--161.

Technical Report (2 MB)

M. Kandemir, Rubio, J. C., Schmidt, U., Welbl, J., Ommer, B., and Hamprecht, F. A., “Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures”, in MICCAI. Proceedings, 2014, pp. 154-161.

Paper (2 MB)

J. Wagner and Ommer, B., “Efficiently Clustering Earth Mover's Distance”, in Proceedins of the Aian Conference on Computer Vision, 2010, p. 477--488.

Technical Report (841.98 KB)

J. Schlecht, Carque, B., and Ommer, B., “Detecting Gestures in Medieval Images”, in Proceedings of the International Conference on Image Processing, 2011, p. 1309--1312.

Technical Report (1.61 MB)

M. Bautista, Sanakoyeu, A., and Ommer, B., “Deep Unsupervised Similarity Learning using Partially Ordered Sets”, in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

deep_unsupervised_similarity_learning_cvpr_2017_paper.pdf (905.82 KB)

N. Ufer and Ommer, B., “Deep Semantic Feature Matching”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

article (8.88 MB)

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)

J. Schlecht and Ommer, B., “Contour-based Object Detection”, in BMVC, 2011, p. 1--9.

Technical Report (2.62 MB)

D. Kotovenko, Sanakoyeu, A., Lang, S., and Ommer, B., “Content and Style Disentanglement for Artistic Style Transfer”, in Proceedings of the Intl. Conf. on Computer Vision (ICCV), 2019.

S. V. E. Keränen, DePace, A., Hendriks, C. L. Luengo, Fowlkes, C., Arbelaez, P., Ommer, B., Brox, T., Henriquez, C., Wunderlich, Z., Eckenrode, K., Fischer, B., Hammonds, A., and Celniker, S. E., “Computational Analysis of Quantitative Changes in Gene Expression and Embryo Morphology between Species”, in Evolution-The Molecular Landscape, 2009.

B. Ommer and Buhmann, J. M., “A Compositionality Architecture for Perceptual Feature Grouping”, in Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, 2003, vol. 2683, p. 275--290.

Technical Report (2.89 MB)

B. Ommer and Buhmann, J. M., “Compositional Object Recognition, Segmentation, and Tracking in Video”, in Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, 2007, vol. 4679, p. 318--333.

Technical Report (2.78 MB)

M. Bautista, Sanakoyeu, A., Sutter, E., and Ommer, B., “CliqueCNN: Deep Unsupervised Exemplar Learning”, in Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS), Barcelona, 2016.

Article (5.79 MB)

Pages