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2021

Blattmann, A, Milbich, T, Dorkenwald, M and Ommer, B (2021). Behavior-Driven Synthesis of Human Dynamics. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://arxiv.org/abs/2103.04677

Milbich, T, Roth, K, Sinha, S, Schmidt, L, Ghassemi, M and Ommer, B (2021). Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning. https://arxiv.org/abs/2107.09562

Rombach, R, Esser, P and Ommer, B (2021). Geometry-Free View Synthesis: Transformers and no 3D Priors. Proceedings of the Intl. Conf. on Computer Vision (ICCV). https://arxiv.org/abs/2104.07652

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

Esser, P, Rombach, R, Blattmann, A and Ommer, B (2021). ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis. https://arxiv.org/abs/2108.08827

Sanakoyeu, A, Ma, P, Tschernezki, V and Ommer, B (2021). Improving Deep Metric Learning by Divide and Conquer. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). https://arxiv.org/abs/2109.04003

Blattmann, A, Milbich, T, Dorkenwald, M and Ommer, B (2021). iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis. Proceedings of the International Conference on Computer Vision (ICCV). https://arxiv.org/abs/2107.02790

Afifi, M, Derpanis, K G, Ommer, B and Brown, M S (2021). Learning Multi-Scale Photo Exposure Correction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://arxiv.org/abs/2003.11596

Kotovenko, D, Wright, M, Heimbrecht, A and Ommer, B (2021). Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://compvis.github.io/brushstroke-parameterized-style-transfer/

Roth, K, Milbich, T, Ommer, B, Cohen, J Paul and Ghassemi, M (2021). S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning. Proceedings of International Conference on Machine Learning (ICML). https://arxiv.org/abs/2009.08348

Islam, M Amirul, Kowal, M, Esser, P, Jia, S, Ommer, B, Derpanis, K G and Bruce, N (2021). Shape or Texture: Understanding Discriminative Features in CNNs. International Conference on Learning Representations (ICLR)

Dorkenwald, M, Milbich, T, Blattmann, A, Rombach, R, Derpanis, K G and Ommer, B (2021). Stochastic Image-to-Video Synthesis usin cINNs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Esser, P, Rombach, R and Ommer, B (2021). Taming Transformers for High-Resolution Image Synthesis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://arxiv.org/abs/2012.09841

Lang, S and Ommer, B (2021). Transforming Information Into Knowledge: How Computational Methods Reshape Art History. Digital Humanities Quaterly (DHQ). 15. http://digitalhumanities.org/dhq/vol/15/3/000560/000560.html

Lang, S and Ommer, B (2021). Transforming Information Into Knowledge: How Computational Methods Reshape Art History. Digital Humanities Quaterly (DHQ). 15

Blattmann, A, Milbich, T, Dorkenwald, M and Ommer, B (2021). Understanding Object Dynamics for Interactive Image-to-Video Synthesis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://arxiv.org/abs/2106.11303v1

Brattoli, B, Büchler, U, Dorkenwald, M, Reiser, P, Filli, L, Helmchen, F, Wahl, A - S and Ommer, B (2021). Unsupervised behaviour analysis and magnification (uBAM) using deep learning. Nature Machine Intelligence. https://rdcu.be/ch6pL

2020

Lang, S and Ommer, B (2020). Das Objekt jenseits der Digitalisierung. Das digitale Objekt. 7. http://www.deutsches-museum.de/fileadmin/Content/010_DM/060_Verlag/studies-7.pdf

lang_ommer_digitalhumanities_2020_.pdf (599.56 KB)

Dencker, T, Klinkisch, P, Maul, S M and Ommer, B (2020). Deep learning of cuneiform sign detection with weak supervision using transliteration alignment. PLoS ONE. 15. https://hci.iwr.uni-heidelberg.de/compvis/projects/cuneiform

Esser, P, Rombach, R and Ommer, B (2020). A Disentangling Invertible Interpretation Network for Explaining Latent Representations. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://compvis.github.io/iin/

Article (13.07 MB)

Milbich, T, Roth, K, Bharadhwaj, H, Sinha, S, Bengio, Y, Ommer, B and Cohen, J Paul (2020). DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning. IEEE European Conference on Computer Vision (ECCV). https://arxiv.org/abs/2004.13458

Rombach, R, Esser, P and Ommer, B (2020). Making Sense of CNNs: Interpreting Deep Representations & Their Invariances with INNs. IEEE European Conference on Computer Vision (ECCV). https://compvis.github.io/invariances/

Rombach, R, Esser, P and Ommer, B (2020). Network Fusion for Content Creation with Conditional INNs. CVPRW 2020 (AI for Content Creation). https://compvis.github.io/network-fusion/

Rombach, R, Esser, P and Ommer, B (2020). Network-to-Network Translation with Conditional Invertible Neural Networks. Neural Information Processing Systems (NeurIPS) (Oral). https://compvis.github.io/net2net/

Esser, P, Rombach, R and Ommer, B (2020). A Note on Data Biases in Generative Models. NeurIPS 2020 Workshop on Machine Learning for Creativity and Design. https://arxiv.org/abs/2012.02516

Ufer, N, Lang, S and Ommer, B (2020). 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

Paper (1.03 MB)

Milbich, T, Roth, K and Ommer, B (2020). PADS: Policy-Adapted Sampling for Visual Similarity Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1. https://arxiv.org/abs/2003.11113

Roth, K, Milbich, T, Sinha, S, Gupta, P, Ommer, B and Cohen, J Paul (2020). Revisiting Training Strategies and Generalization Performance in Deep Metric Learning. International Conference on Machine Learning (ICML). https://arxiv.org/pdf/2002.08473.pdf

Milbich, T, Roth, K, Brattoli, B and Ommer, B (2020). Sharing Matters for Generalization in Deep Metric Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). https://arxiv.org/abs/2004.05582

Dorkenwald, M, Büchler, U and Ommer, B (2020). Unsupervised Magnification of Posture Deviations Across Subjects. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

article.pdf (1.15 MB)

Braun, S, Esser, P and Ommer, B (2020). Unsupervised Part Discovery by Unsupervised Disentanglement. Proceedings of the German Conference on Pattern Recognition (GCPR) (Oral). Tübingen. https://compvis.github.io/unsupervised-part-segmentation/

Milbich, T, Ghori, O and Ommer, B (2020). Unsupervised Representation Learning by Discovering Reliable Image Relations. Pattern Recognition. 102. http://arxiv.org/abs/1911.07808

2019

Kotovenko, D, Sanakoyeu, A, Lang, S and Ommer, B (2019). Content and Style Disentanglement for Artistic Style Transfer. Proceedings of the Intl. Conf. on Computer Vision (ICCV)

Sanakoyeu, A, Tschernezki, V, Büchler, U and Ommer, B (2019). Divide and Conquer the Embedding Space for Metric Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://github.com/CompVis/metric-learning-divide-and-conquer

Brattoli, B, Roth, K and Ommer, B (2019). MIC: Mining Interclass Characteristics for Improved Metric Learning. Proceedings of the Intl. Conf. on Computer Vision (ICCV)

Pages