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T. Milbich, Roth, K., Sinha, S., Schmidt, L., Ghassemi, M., and Ommer, B., Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning. 2021.
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.PDF icon Article (5.79 MB)
P. Snoeij, van Halsema, D., Oost, W. A., Calkoen, C. J., Vogelzang, J., and Jähne, B., Comparison of microwave backscatter measurements and small-scale surface wave measurements made from the Dutch ocean research tower 'Noordwijk', in Proceedings IGARSS '91, 1991, vol. 3, p. 1289--1292.
D. van Halsema, Calkoen, C. J., Oost, W. A., Snoeij, P., Vogelzang, J., and Jähne, B., Comparisons of backscattering calculations with measurements made in a large wind/wave flume, in Proc. IGARSS'92, 1992, vol. 2, p. 1451--1453.
D. van Halsema, Calkoen, C. J., Oost, W. A., Snoeij, P., and Jähne, B., Comparisons of X-band Radar Backscatter Measurements with Area extended wave slop measurements made in a large Wind/Wave Tank, in Proc. IGARSS'89, 1989, vol. 5, p. 2997--3001.
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.PDF icon Technical Report (2.78 MB)
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.PDF icon Technical Report (2.89 MB)
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.
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.PDF icon 413-17-83318-2-10-20181210.pdf (2.98 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.
J. Schlecht and Ommer, B., Contour-based Object Detection, in BMVC, 2011, p. 1--9.PDF icon Technical Report (2.62 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.PDF icon Article (891.47 KB)PDF icon Oral slides (9.17 MB)
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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)
T. Dencker, Klinkisch, P., Maul, S. M., and Ommer, B., Deep learning of cuneiform sign detection with weak supervision using transliteration alignment, PLoS ONE, vol. 15, no. 12, 2020.
N. Ufer and Ommer, B., Deep Semantic Feature Matching, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.PDF icon article (8.88 MB)
A. Sanakoyeu, Bautista, M., and Ommer, B., Deep Unsupervised Learning of Visual Similarities, Pattern Recognition, vol. 78, 2018.PDF icon PDF (8.35 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.PDF icon deep_unsupervised_similarity_learning_cvpr_2017_paper.pdf (905.82 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.PDF icon Technical Report (1.61 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.
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.
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.
P. Snoeij, Calkoen, C. J., Oost, W. A., van Halsema, D., Vogelzang, J., and Jähne, B., Dual-polarized scatterometer measurements of generated Wind and Gravity Wave in a Very Large Wind/Wave Tank, in Proc. IGARSS'90, 1990, p. 2157--2160.
D. van Halsema, Calkoen, C. J., Oost, W. A., Snoeij, P., Vogelzang, J., and Jähne, B., Dual-polarized scatterometer measurements of wind and mechanically generated waves in a very large wind/wave flume, the VIERS-1 project, in Proc.\ 5th Int. Coll. Physical Measurements and Signatures in Remote Sensing, Courchevel, France, 1991, vol. ESA SP-319, p. 247--252.

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