Publications

Search

Show only items where

Author

Type

Term

Year

Keyword

Export 114 results:

Filters: Author is Björn Ommer [Clear All Filters]

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

H

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

I

P. Esser, Rombach, R., Blattmann, A., and Ommer, B., “ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis”. 2021.

A. Sanakoyeu, Ma, P., Tschernezki, V., and Ommer, B., “Improving Deep Metric Learning by Divide and Conquer”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 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)

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.

L

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)

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

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)

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. Ommer and Buhmann, J. M., “Learning the Compositional Nature of Visual Object Categories for Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, p. 501--516, 2010.

Technical Report (2.78 MB)

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)

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, 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)

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.

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. 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)

M

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.

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, 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. Monroy, Bell, P., and Ommer, B., “Morphological Analysis for Investigating Artistic Images”, Image and Vision Computing, vol. 32, p. 414--423, 2014.

Technical Report (2.86 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. 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)

N

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

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

P. Bell, Schlecht, J., and Ommer, B., “Nonverbal Communication in Medieval Illustrations Revisited by Computer Vision and Art History”, Visual Resources Journal, Special Issue on Digital Art History, vol. 29, p. 26--37, 2013.

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.

O

B. Ommer and Buhmann, J. M., “Object Categorization by Compositional Graphical Models”, in Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, 2005, vol. 3757, p. 235--250.

Technical Report (2.07 MB)

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)

M. Takami, Bell, P., and Ommer, B., “Offline Learning of Prototypical Negatives for Efficient Online Exemplar SVM”, in Winter Conference on Applications of Computer Vision, 2014, p. 377--384.

A. - S. Wahl, Büchler, U., Brändli, A., Brattoli, B., Musall, S., Kasper, H., Ineichen, B. V., Helmchen, F., Ommer, B., and Schwab, M. E., “Optogenetically stimulating the intact corticospinal tract post-stroke restores motor control through regionalized functional circuit formation”, Nature Communications, p. (ASW & UB contributed equally; BO and MES contributed equally), 2017.

P

T. Milbich, Roth, K., and Ommer, B., “PADS: Policy-Adapted Sampling for Visual Similarity Learning”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020, vol. 1, no. 1.

C. S. Garbe and Ommer, B., “Parameter Estimation in Image Processing and Computer Vision”, in Model Based Parameter Estimation: Theory and Applications, Springer, 2013, p. 311--334.

Technical Report (928 KB)

A. Monroy, Kröger, T., Arnold, M., and Ommer, B., “Parametric Object Detection for Iconographic Analysis”, in Scientific Computing & Cultural Heritage, 2011.

B. Antic and Ommer, B., “Per-Sample Kernel Adaptation for Visual Recognition and Grouping”, in Proceedings of the IEEE International Conference on Computer Vision, 2015.

Technical Report (1.58 MB)

Pages