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Conference Proceedings

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.

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.

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

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.

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.

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.

Article (13.07 MB)

S. Lang and Ommer, B., “Das Objekt jenseits der Digitalisierung”, Das digitale Objekt, vol. 7. 2020.

lang_ommer_digitalhumanities_2020_.pdf (599.56 KB)

A. Monroy and Ommer, B., “Beyond Bounding-Boxes: Learning Object Shape by Model-driven Grouping”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 7574. Springer, p. 582--595, 2012.

Technical Report (1.58 MB)

A. Blattmann, Milbich, T., Dorkenwald, M., and Ommer, B., “Behavior-Driven Synthesis of Human Dynamics”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2021.

Conference Paper

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)

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)

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.

Technical Report (2.99 MB)

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.

Technical Report (3.27 MB)

B. Antic and Ommer, B., “Video Parsing for Abnormality Detection”, in Proceedings of the IEEE International Conference on Computer Vision, 2011, p. 2415--2422.

Technical Report (990.21 KB)

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.

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

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. 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.

Technical Report (11.06 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.

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.

Article (2.24 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.

Technical Report (7 MB)

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)

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.

Technical Report (319.58 KB)

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)

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.

Technical Report (2.43 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.

Technical Report (2.76 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.

Technical Report (8.01 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)

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

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.

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.

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)

Pages