Publications

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2019
Lorenz, D, Bereska, L, Milbich, T and Ommer, B (2019). Unsupervised Part-Based Disentangling of Object Shape and Appearance. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Oral + Best paper finalist: top 45 / 5160 submissions)
Esser, P, Haux, J and Ommer, B (2019). Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis. Proceedings of the Intl. Conf. on Computer Vision (ICCV). https://compvis.github.io/robust-disentangling/
Kotovenko, D, Sanakoyeu, A, Lang, S, Ma, P and Ommer, B (2019). Using a Transformation Content Block For Image Style Transfer. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Ufer, N, Lui, K To, Schwarz, K, Warkentin, P and Ommer, B (2019). Weakly Supervised Learning of Dense SemanticCorrespondences and Segmentation. German Conference on Pattern Recognition (GCPR)PDF icon article (6.1 MB)
2018
Lang, S and Ommer, B (2018). Attesting Similarity: Supporting the Organization and Study of Art Image Collections with Computer Vision. Digital Scholarship in the Humanities, Oxford University Press. 33 845-856
Bell, P and Ommer, B (2018). Computer Vision und Kunstgeschichte — Dialog zweier Bildwissenschaften. Computing Art Reader: Einführung in die digitale Kunstgeschichte, P. Kuroczyński et al. (ed.)PDF icon 413-17-83318-2-10-20181210.pdf (2.98 MB)
Sayed, N, Brattoli, B and Ommer, B (2018). Cross and Learn: Cross-Modal Self-Supervision. German Conference on Pattern Recognition (GCPR) (Oral). Stuttgart, Germany. https://arxiv.org/abs/1811.03879v1PDF icon Article (891.47 KB)PDF icon Oral slides (9.17 MB)
Sanakoyeu, A, Bautista, M and Ommer, B (2018). Deep Unsupervised Learning of Visual Similarities. Pattern Recognition. 78. https://authors.elsevier.com/a/1WXUt77nKSb25 PDF icon PDF (8.35 MB)
Wahl, A - S, 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 (2018). Early reduced behavioral activity induced by large strokes affects the efficiency of enriched environment in rats. Sage Journals. Journal of Cerebral Blood Flow & Metabolism. http://journals.sagepub.com/doi/abs/10.1177/0271678X18777661PDF icon 0271678x18777661.pdf (770.87 KB)
Büchler, U, Brattoli, B and Ommer, B (2018). Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning. Proceedings of the European Conference on Computer Vision (ECCV). (UB and BB contributed equally), Munich, GermanyPDF icon Article (5.34 MB)PDF icon buechler_eccv18_poster.pdf (1.65 MB)
Ghori, O, Mackowiak, R, Bautista, M, Beuter, N, Drumond, L, Diego, F and Ommer, B (2018). Learning to Forecast Pedestrian Intention from Pose Dynamics. Intelligent Vehicles, IEEE, 2018
Lang, S and Ommer, B (2018). Reconstructing Histories: Analyzing Exhibition Photographs with Computational Methods. Arts, Computational Aesthetics. 7, 64PDF icon arts-07-00064.pdf (4.6 MB)
Lang, S and Ommer, B (2018). Reflecting on How Artworks Are Processed and Analyzed by Computer Vision. European Conference on Computer Vision (ECCV - VISART). Springer
Sanakoyeu, A, Kotovenko, D, Lang, S and Ommer, B (2018). A Style-Aware Content Loss for Real-time HD Style Transfer. Proceedings of the European Conference on Computer Vision (ECCV) (Oral)
Esser, P, Haux, J, Milbich, T and Ommer, B (2018). Towards Learning a Realistic Rendering of Human Behavior. European Conference on Computer Vision (ECCV - HBUGEN)
Esser, P, Sutter, E and Ommer, B (2018). A Variational U-Net for Conditional Appearance and Shape Generation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (short Oral). https://compvis.github.io/vunet/
Blum, O, Brattoli, B and Ommer, B (2018). X-GAN: Improving Generative Adversarial Networks with ConveX Combinations. German Conference on Pattern Recognition (GCPR) (Oral). Stuttgart, GermanyPDF icon Article (6.65 MB)PDF icon Supplementary material (7.96 MB)PDF icon Oral slides (14.96 MB)
2017
Ufer, N and Ommer, B (2017). Deep Semantic Feature Matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)PDF icon article (8.88 MB)
Bautista, M, Sanakoyeu, A and Ommer, B (2017). Deep Unsupervised Similarity Learning using Partially Ordered Sets. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)PDF icon deep_unsupervised_similarity_learning_cvpr_2017_paper.pdf (905.82 KB)
Bautista, M, Fuchs, P and Ommer, B (2017). Learning Where to Drive by Watching Others. Proceedings of the German Conference Pattern Recognition. Springer-Verlag, Basel. 1
Brattoli, B, Büchler, U, Wahl, A - S, Schwab, M E and Ommer, B (2017). LSTM Self-Supervision for Detailed Behavior Analysis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (BB and UB contributed equally)PDF icon Article (8.75 MB)
Wahl, A - S, Büchler, U, Brändli, A, Brattoli, B, Musall, S, Kasper, H, Ineichen, B V, Helmchen, F, Ommer, B and Schwab, M E (2017). Optogenetically stimulating the intact corticospinal tract post-stroke restores motor control through regionalized functional circuit formation. Nature Communications. (ASW & UB contributed equally; BO and MES contributed equally). https://www.nature.com/articles/s41467-017-01090-6
Sümer, Ö, Dencker, T and Ommer, B (2017). Self-supervised Learning of Pose Embeddings from Spatiotemporal Relations in Videos. Proceedings of the IEEE International Conference on Computer Vision (ICCV)PDF icon Paper (3.98 MB)PDF icon Supplementary Material (3.36 MB)
Milbich, T, Bautista, M, Sutter, E and Ommer, B (2017). Unsupervised Video Understanding by Reconciliation of Posture Similarities. Proceedings of the IEEE International Conference on Computer Vision (ICCV). https://hciweb.iwr.uni-heidelberg.de/compvis/research/tmilbich_iccv17
2016
Bautista, M, Sanakoyeu, A, Sutter, E and Ommer, B (2016). CliqueCNN: Deep Unsupervised Exemplar Learning. Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS). MIT Press, Barcelona. https://arxiv.org/abs/1608.08792PDF icon Article (5.79 MB)
Bell, P and Ommer, B (2016). Digital Connoisseur? How Computer Vision Supports Art History. Connoisseurship nel XXI secolo. Approcci, Limiti, Prospettive, A. Aggujaro & S. Albl (ed.). Artemide, Rome
2015
Yarlagadda, P and Ommer, B (2015). Beyond the Sum of Parts: Voting with Groups of Dependent Entities. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE. 37 1134--1147. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6926849
Rubio, J C, Eigenstetter, A and Ommer, B (2015). Generative Regularization with Latent Topics for Discriminative Object Recognition. Pattern Recognition. Elsevier. 48 3871--3880PDF icon Technical Report (5.49 MB)
Antic, B and Ommer, B (2015). Per-Sample Kernel Adaptation for Visual Recognition and Grouping. Proceedings of the IEEE International Conference on Computer Vision. IEEEPDF icon Technical Report (1.58 MB)
Rubio, J C and Ommer, B (2015). Regularizing Max-Margin Exemplars by Reconstruction and Generative Models. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE. 4213--4221PDF icon Technical Report (2.8 MB)
Antic, B, Büchler, U, Wahl, A - S, Schwab, M E and Ommer, B (2015). Spatiotemporal Parsing of Motor Kinematics for Assessing Stroke Recovery. Medical Image Computing and Computer-Assisted Intervention. SpringerPDF icon Article (2.24 MB)
Antic, B and Ommer, B (2015). Spatio-temporal Video Parsing for Abnormality Detection. arXiv. abs/1502.06235. http://arxiv.org/abs/1502.06235PDF icon Technical Report (4.61 MB)
Bell, P and Ommer, B (2015). Training Argus. Kunstchronik. Monatsschrift für Kunstwissenschaft, Museumswesen und Denkmalpflege. Zentralinstitut für Kunstgeschichte. 68 414--420

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