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2019

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.

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.

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)

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.

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)

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)

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

PDF (8.35 MB)

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)

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)

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.

S. Lang and Ommer, B., “Reconstructing Histories: Analyzing Exhibition Photographs with Computational Methods”, Arts, Computational Aesthetics, vol. 7, 64, no. 64, 2018.

arts-07-00064.pdf (4.6 MB)

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.

A. Sanakoyeu, Kotovenko, D., Lang, S., and Ommer, B., “A Style-Aware Content Loss for Real-time HD Style Transfer”, in Proceedings of the European Conference on Computer Vision (ECCV) (Oral), 2018.

P. Esser, Haux, J., Milbich, T., and Ommer, B., “Towards Learning a Realistic Rendering of Human Behavior”, in European Conference on Computer Vision (ECCV - HBUGEN), 2018.

P. Esser, Sutter, E., and Ommer, B., “A Variational U-Net for Conditional Appearance and Shape Generation”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (short Oral), 2018.

O. Blum, Brattoli, B., and Ommer, B., “X-GAN: Improving Generative Adversarial Networks with ConveX Combinations”, in German Conference on Pattern Recognition (GCPR) (Oral), Stuttgart, Germany, 2018.

Article (6.65 MB)

Supplementary material (7.96 MB)

Oral slides (14.96 MB)

2017

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)

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)

E. Levinkov, Uhrig, J., Tang, S., Omran, M., Insafutdinov, E., Kirillov, A., Rother, C., Brox, T., Schiele, B., and Andres, B., “Joint graph decomposition & node labeling: Problem, algorithms, applications”, in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017, vol. 2017-Janua, pp. 1904–1912.

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. 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)

V. Ulman, Maška, M., Magnusson, K. E. G., Ronneberger, O., Haubold, C., Harder, N., Matula, P., Matula, P., Svoboda, D., Radojevic, M., Smal, I., Rohr, K., Jaldén, J., Blau, H. M., Dzyubachyk, O., Lelieveldt, B., Xiao, P., Li, Y., Cho, S. - Y., Dufour, A., Olivo-Marin, J. C., Reyes-Aldasoro, C. C., Solis-Lemus, J. A., Bensch, R., Brox, T., Stegmaier, J., Mikut, R., Wolf, S., Hamprecht, F. A., Esteves, T., Quelhas, P., Demirel, Ö., Malström, L., Jug, F., Tomančák, P., Meijering, E., Muñoz-Barrutia, A., Kozubek, M., and Ortiz-de-Solorzano, C., “An Objective Comparison of Cell Tracking Algorithms”, Nature Methods, vol. 14, no. 12, pp. 1141-1152, 2017.

Technical Report (4.24 MB)

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.

Ö. Sümer, Dencker, T., and Ommer, B., “Self-supervised Learning of Pose Embeddings from Spatiotemporal Relations in Videos”, in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017.

Paper (3.98 MB)

Supplementary Material (3.36 MB)

T. Milbich, Bautista, M., Sutter, E., and Ommer, B., “Unsupervised Video Understanding by Reconciliation of Posture Similarities”, in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017.

2016

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)

P. Bell and Ommer, B., “Digital Connoisseur? How Computer Vision Supports Art History”, in Connoisseurship nel XXI secolo. Approcci, Limiti, Prospettive, A. Aggujaro & S. Albl (ed.), Rome: Artemide, 2016.

E. Meijering, Carpenter, A. E., Peng, H., Hamprecht, F. A., and Olivo-Marin, J., “Imagining the future of bioimage analysis”, Nature Biotechnology, vol. 34, no. 12, pp. 1250-1255, 2016.

Technical Report (924.57 KB)

2015

P. Yarlagadda and Ommer, B., “Beyond the Sum of Parts: Voting with Groups of Dependent Entities”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, p. 1134--1147, 2015.

J. C. Rubio, Eigenstetter, A., and Ommer, B., “Generative Regularization with Latent Topics for Discriminative Object Recognition”, Pattern Recognition, vol. 48, p. 3871--3880, 2015.

Technical Report (5.49 MB)

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)

J. C. Rubio and Ommer, B., “Regularizing Max-Margin Exemplars by Reconstruction and Generative Models”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, p. 4213--4221.

Technical Report (2.8 MB)

Publications

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