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Conference Paper
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.PDF icon 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.PDF icon Article (8.75 MB)
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.PDF icon 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., “A Morphometric Approach to Reception Analysis of Premodern Art”, in Scientific Computing & Cultural Heritage, 2013.PDF icon 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.PDF icon Technical Report (3.18 MB)
R. Rombach, Esser, P., and Ommer, B., “Network Fusion for Content Creation with Conditional INNs”, in CVPRW 2020 (AI for Content Creation), 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.PDF icon Technical Report (1.27 MB)
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.
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.PDF icon Technical Report (2.07 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.
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.
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.PDF icon Technical Report (1.58 MB)
A. Eigenstetter, Takami, M., and Ommer, B., “Randomized Max-Margin Compositions for Visual Recognition”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, p. 3590--3597.PDF icon Technical Report (8.01 MB)
P. Yarlagadda, Monroy, A., Carque, B., and Ommer, B., “Recognition and Analysis of Objects in Medieval Images”, in Proceedins of the Aian Conference on Computer Vision, Workshop on e-Heritage, 2010, p. 296--305.PDF icon Technical Report (2.76 MB)
A. Monroy, Carque, B., and Ommer, B., “Reconstructing the Drawing Process of Reproductions from Medieval Images”, in Proceedings of the International Conference on Image Processing, 2011, p. 2974--2977.PDF icon Technical Report (2.43 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.PDF icon Technical Report (2.8 MB)
B. Antic and Ommer, B., “Robust Multiple-Instance Learning with Superbags”, in Proceedings of the Aian Conference on Computer Vision (ACCV) (Oral), 2012, p. 242--255.PDF icon Technical Report (319.58 KB)
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.PDF icon Paper (3.98 MB)PDF icon Supplementary Material (3.36 MB)
A. Monroy, Bell, P., and Ommer, B., “Shaping Art with Art: Morphological Analysis for Investigating Artistic Reproductions”, in Proceedings of the European Conference on Computer Vision, Workshop on VISART, 2012, vol. 7583, p. 571--580.PDF icon Technical Report (7 MB)
B. Antic, Büchler, U., Wahl, A. - S., Schwab, M. E., and Ommer, B., “Spatiotemporal Parsing of Motor Kinematics for Assessing Stroke Recovery”, in Medical Image Computing and Computer-Assisted Intervention, 2015.PDF icon Article (2.24 MB)
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. Yarlagadda, Monroy, A., Carque, B., and Ommer, B., “Top-down Analysis of Low-level Object Relatedness Leading to Semantic Understanding of Medieval Image Collections”, in Conference on Computer Vision and Image Analysis of Art II, 2011, vol. 7869, p. 61--69.PDF icon Technical Report (11.06 MB)
P. Yarlagadda, Monroy, A., Carque, B., and Ommer, B., “Towards a Computer-based Understanding of Medieval Images”, in Scientific Computing & Cultural Heritage, 2009, p. 89--97.
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.
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.
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.
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.
B. Antic and Ommer, B., “Video Parsing for Abnormality Detection”, in Proceedings of the IEEE International Conference on Computer Vision, 2011, p. 2415--2422.PDF icon Technical Report (990.21 KB)
A. Eigenstetter and Ommer, B., “Visual Recognition using Embedded Feature Selection for Curvature Self-Similarity”, in Proceedings of the Conference on Advances in Neural Information Processing Systems, 2012, p. 377--385.PDF icon Technical Report (3.27 MB)
P. Yarlagadda, Monroy, A., and Ommer, B., “Voting by Grouping Dependent Parts”, in Proceedings of the European Conference on Computer Vision, 2010, vol. 6315, p. 197--210.PDF icon Technical Report (2.99 MB)
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.PDF icon article (6.1 MB)
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.PDF icon Article (6.65 MB)PDF icon Supplementary material (7.96 MB)PDF icon Oral slides (14.96 MB)

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