Publications

Note, we add all peer reviewed articles to this list, and sometimes also arXiv papers (but not all arXiv papers).

Books

2024

  • D. Zavadski, D. Kalšan, C. Rother. “PrimeDepth: Efficient Monocular Depth Estimation with a Stable Diffusion Preimage”. accepted at ACCV 2024, arxiv:2409.09144 [arxiv], [pdf], [code], [project]
  • P. Sorrenson, F. Draxler, A. Rousselot, S. Hummerich, U. Köthe (2024). “Learning Distributions on Manifolds with Free-Form Flows”. accepted at NeurIPS 2024, arXiv:2312.09852. [arxiv], [pdf]
  • M. Schmitt, V. Pratz, U. Köthe, P. Bürkner, S. Radev (2024). “Consistency Models for Scalable and Fast Simulation-Based Inference”, accepted at NeurIPS 2024, arXiv:2312.05440. [arxiv], [pdf]
  • D. Zavadski, J.-F. Feiden, C. Rother. “ControlNet-XS: Rethinking the Control of Text-to-Image Diffusion Models as Feedback-Control Systems”. ECCV 2024 Oral, [arxiv], [pdf], [code], [project]
  • T. Hodan, M. Sundermeyer, Y. Labbé, V. N. Nguyen, G. Wang, E. Brachmann, B. Drost, V. Lepetit, C. Rother, J. Matas. “BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects”. Workshop on Computer Vision for Mixed Reality at CVPR 2024. [arxiv], [pdf]
  • F. Draxler, S. Wahl, C. Schnörr, U. Köthe (2024). “On the Universality of Volume-Preserving and Coupling-Based Normalizing Flows”. ICML 2024, arXiv:2402.06578. [link], [arxiv], [pdf]
  • M. Schmitt, D. Ivanova, D. Habermann, P. Bürkner, U. Köthe, S. Radev (2024). “Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference”, ICML 2024, arXiv:2310.04395. [link], [arxiv], [pdf]
  • F. Draxler, P. Sorrenson, A. Rousselot, L. Zimmerman, U. Köthe (2024). “Free-form flows: Make Any Architecture a Normalizing Flow”. AISTATS 2024, arXiv:2310.16624. [link], [arxiv], [pdf]
  • P. Sorrenson, F. Draxler, A. Rousselot, S. Hummerich, L. Zimmerman, U. Köthe (2024). “Lifting Architectural Constraints of Injective Flows”. ICLR 2024, arXiv:2306.01843. [link], [arxiv], [pdf]
  • D.H. Lehmann, B. Gomes, N. Vetter, O. Braun, A. Amr, T. Hilbel, J, Müller, U. Köthe, C. Reich, E. Kayvanpour, F. Sedaghat-Hamedani, M. Meder, J. Haas, E. Ashley, W. Rottbauer, D. Felbel, R. Bekeredjian, H. Mahrholdt, A. Keller, P. Ong, A. Seitz, H. Hund, N. Geis, F. André, S. Engelhardt, H.A. Katus, N. Frey, V. Heuveline, B. Meder (2024). “Prediction of diagnosis and diastolic filling pressure by AI-enhanced cardiac MRI: a modeling study of hospital data”, The Lancet Digital Health, 6(6):e407-e417. [link]
  • L. Elsemüller, H. Olischläger, M. Schmitt, P. Bürkner, U. Köthe, S. Radev (2024). “Sensitivity-aware amortized Bayesian inference”, Transactions on Machine Learning Research, arXiv:2310.11122. [link], [arxiv], [pdf]
  • Siddharth Tourani, Carsten Rother, Muhammad Haris Khan, Bogdan Savchynskyy “Unsupervised Deep Graph Matching Based on Cycle Consistency”. AAAI 2024 [extended version]
  • M. Schüssler, L. Hormann, R. Dachselt, A. Blake, C. Rother (2024). “Gazing Heads: Investigating Gaze Perception in Video-Mediated Communication”. ACM Transactions on Computer-Human Interaction. [link], [pdf]
  • P. Lorenz, M. Fernandez, J. Müller, U. Köthe (2024). “Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors”, ICML 2024 Next Generation of AI Safety Workshop, arXiv:2406.15104. [link], [arxiv], [pdf]
  • P. Sorrenson, D. Behrend-Uriarte, C. Schnörr, U. Köthe (2024). “Learning Distances from Data with Normalizing Flows and Score Matching”. arXiv:2407.09297. [arxiv], [pdf]

2023

  • U. Köthe, C. Rother (2023), eds.: “Pattern Recognition”, Proceedings of 45th DAGM German Conference, DAGM GCPR 2023. [link]
  • F. Draxler, L. Kühmichel, A. Rousselot, J. Müller, C. Schnörr, U. Köthe (2023). “On the Convergence Rate of Gaussianization with Random Rotations”, ICML 2023, arXiv:2306.13520. [link], [arxiv], [pdf]
  • R. Schmier, U. Köthe, C.N. Straehle (2023). “Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data”, Transactions on Machine Learning Research, arXiv:2208.14024. [link], [arxiv], [pdf]
  • M. Sundermeyer, T. Hodaň, Y. Labbé, G. Wang, E. Brachmann, B. Drost, C. Rother, J. Matas (2023). “BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects”. [pdf]
  • D.E. Kang, R. Klessen, V. Ksoll, L. Ardizzone, U. Köthe, S. Glover (2023). “Noise-Net: Determining physical properties of HII regions reflecting observational uncertainties”, Monthly Notices of the Royal Astronomical Society (vol. 520, no. 4, pp. 4981-5001), arXiv:2301.03014. [link], [arxiv], [pdf]
  • T. Bister, M. Erdmann, U. Köthe, J. Schulte (2023). “Inference of astrophysical parameters with a conditional invertible neural network”, Journal of Physics: Conference Series (Vol. 2438, No. 1, p. 012094). [link], [pdf]
  • Tomas Dlask, Bogdan Savchynskyy (2023). “Relative-Interior Solution for (Incomplete) Linear Assignment Problem with Applications to Quadratic Assignment Problem” [arxiv]
  • S. Radev, M. Schmitt, V. Pratz, U. Picchini, U. Köthe, P.-C. Bürkner (2023). “JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models”, UAI 2023, arXiv:2302.09125. [link], [arxiv], [pdf]
  • S. Radev, M. Schmitt, L. Schumacher, L. Elsemüller, V. Pratz, Y. Schälte, U. Köthe, P.-C. Bürkner (2023). “BayesFlow: Amortized Bayesian Workflows With Neural Networks”, Journal of Open Source Software, arXiv:2306.16015. [link], [arxiv], [pdf]
  • L. Schumacher, P. Bürkner, A. Voss, U. Köthe, S. Radev (2023). “Neural superstatistics for Bayesian estimation of dynamic cognitive models”, Scientific Reports, arXiv:2211.13165. [link], [arxiv], [pdf]
  • M. Schmitt, P. Bürkner, U. Köthe, S. Radev (2022). “Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks”, DAGM German Conference on Pattern Recognition 2023, arXiv:2112.08866. [link], [arxiv], [pdf]
  • J. Müller, S. Radev, R. Schmier, F. Draxler, C. Rother, U. Köthe (2023). “Finding Competence Regions in Domain Generalization”, Transactions on Machine Learning Research, arXiv:2303.09989. [link], [arxiv], [pdf]
  • K. Dreher, L. Ayala, M. Schellenberg, M. Hübner, J.-H. Nölke, T. Adler, S. Seidlitz, J. Sellner, A. Studier-Fischer, J. Gröhl, F. Nickel, U. Köthe, A. Seitel, L. Maier-Hein (2023). “Unsupervised Domain Transfer with Conditional Invertible Neural Networks”, MICCAI 2023, arXiv:2303.10191. [link], [arxiv], [pdf]
  • J. Wider, J. Kruse, N. Weitzel, J. Bühler, U. Köthe, K. Rehfeld (2023). “Towards Learned Emulation of Interannual Water Isotopologue Variations in General Circulation Models”, Environmental Data Science, arXiv:2301.13462. [link], [arxiv], [pdf]
  • J. Haldemann, V. Ksoll, D. Walter, Y. Alibert, R. Klessen, W. Benz, U. Köthe, L. Ardizzone, C. Rother (2023). “Exoplanet Characterization using Conditional Invertible Neural Networks”, Astronomy & Astrophysics, arXiv:2202.00027. [link], [arxiv], [pdf]
  • A. Bischops, S. Radev, U. Köthe, S. Chen, P. Geldsetzer, M. Sarker, T.T. Su, F.A. Mohamed, N. Darwish, N.A. Ahmad, Sidi Ahmed Ould Baba, T. Bärnighausen, S. Barteit (2023). “Data Resource Profile: The Global School-based Student Health Survey—behavioural risk and protective factors among adolescents”, International Journal of Epidemiology, 52(2):e102–e109. [link], [pdf]
  • J. Müller, L. Kühmichel, M. Rohbeck, S. Radev, U. Köthe (2023). “Towards Context-Aware Domain Generalization: Representing Environments with Permutation-Invariant Networks”. arXiv:2312.10107. [arxiv], [pdf]
  • J. Müller, L. Ardizzone, U. Köthe (2023). “ProDAS: Probabilistic Dataset of Abstract Shapes”, Technical Report U Heidelberg. [link], [pdf]
  • TJ Adler, JH Nölke, A Reinke, MD Tizabi, S Gruber, D Trofimova, L Ardizzone, P Jaeger, F Buettner, U Köthe, L Maier-Hein (2023). “Application-driven Validation of Posteriors in Inverse Problems”, arXiv:2309.09764. [arxiv], [pdf]
  • U. Köthe (2023). “A review of change of variable formulas for generative modeling”. arXiv:2308.02652. [arxiv], [pdf]

2022

  • F. Draxler, C. Schnörr, U. Köthe (2022). “Whitening Convergence Rate of Coupling-Based Normalizing Flows”, NeurIPS 2022 (oral presentation), arXiv:2210.14032. [arxiv], [pdf]
  • S. Haller, L. Feineis, L. Hutschenreiter, F. Bernard, C. Rother, D. Kainmüller, P. Swoboda, B. Savchynskyy (2022). “A Comparative Study of Graph Matching Algorithms in Computer Vision”, ECCV 2022. [arxiv] [pdf] [website]
  • T. Leistner, R. Mackowiak, L. Ardizzone, U. Köthe, C. Rother (2022). “Towards Multimodal Depth Estimation from Light Fields”, CVPR 2022, arXiv:2203.16542. [link], [arxiv], [pdf]
  • U. Köthe (2022). “Mensch und Automat – Die Rolle von Zufall und Determinismus”, Forum Marsilius-Kolleg 22(2). [link], [pdf]
  • J. Kruse, B. Ellerhoff, U. Köthe, K. Rehfeld (2022). “Conditional normalizing flow for predicting the occurrence of rare extreme events on long time scales”, EGU General Assembly 2022. [link], [pdf]
  • M. Tölle, U. Köthe, F. André, B. Meder, S. Engelhardt (2022). “Content-Aware Differential Privacy with Conditional Invertible Neural Networks”, International Workshop on Distributed, Collaborative, and Federated Learning, Springer LNCS 13573, arXiv:2207.14625. [link], [arxiv], [pdf]
  • J. Fragemann, L. Ardizzone, J. Egger, J. Kleesiek (2022). “Review of Disentanglement Approaches for Medical Applications – Towards Solving the Gordian Knot of Generative Models in Healthcare”, MICCAI WS Medical Applications with Disentanglements (MAD), arXiv:2203.11132, 2022. [arxiv], [pdf]
  • D.E. Kang, E. Pellegrini, L. Ardizzone, R. Klessen, U. Köthe, S. Glover, V. Ksoll (2022). “Emission-line diagnostics of HII regions using conditional Invertible Neural Networks”, Monthly Notices of the Royal Astronomical Society, 512(1):617–647. [link], [arxiv], [pdf]
  • T. Bister, M. Erdmann, U. Köthe, J. Schulte (2022). “Inference of cosmic-ray source properties by conditional invertible neural networks”, The European Physical Journal C 82 (2),171. [link], [arxiv], [pdf]

2021

  • A. Sauer, K. Chitta, J. Müller, A. Geiger (2021). “Projected GANs converge faster”, NeurIPS 2021, arXiv:2111.01007, [arxiv], [pdf]
  • E. Brachmann, M. Humenberger, C. Rother, T. Sattle (2021). “On the Limits of Pseudo Ground Truth in Visual Camera Re-localisation”, ICCV 2021 [pdf]
  • L.Hutschenreiter, S. Haller, L. Feineis, C. Rother, D. Kainmueller, B. Savchynskyy (2021). “Fusion Moves for Graph Matching”, ICCV 2021 (oral presentation) [pdf], [project page]
  • R. Mackowiak, L. Ardizzone, U. Köthe, C. Rother (2021). “Generative Classifiers as a Basis for Trustworthy Image Classification”, CVPR 2021 (oral presentation), arXiv:2007.15036 [arxiv], [pdf]
  • E. Brachmann, C. Rother (2021). “Visual Camera Re-Localization from RGB and RGB-D Images Using DSAC”, IEEE Transactions on Pattern Analysis and Machine Intelligence 2021, arXiv:2002.12324. [arxiv], [pdf]
  • J. Kruse, G. Detommaso, U. Köthe, R. Scheichl (2021). “HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference”, AAAI 2021, arXiv:1905.10687. [arxiv], [pdf]
  • S. Radev, M. D’Alessandro, U. Mertens, A. Voss, U. Köthe, P. Bürkner (2021). “Amortized Bayesian model comparison with evidential deep learning”, IEEE Trans. Neural Networks and Learning Systems, arXiv:2004.10629. [link], [arxiv], [pdf]
  • S. Radev, F. Graw, S. Chen, N. Mutters, V. Eichel, T. Bärnighausen, U. Köthe (2021). “OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany”, PLOS Computational Biology, arXiv:2010.00300. [link], [arxiv], [pdf]
  • J. Müller, R. Schmier, L. Ardizzone, C. Rother, U. Köthe (2021). “Learning Robust Models Using The Principle of Independent Causal Mechanisms”, GCPR 2021 [arxiv], [pdf]
  • H. Lee, R. Herzog, J. Rexilius, C. Rother (2021). “Spatiotemporal Outdoor Lighting Aggregation on Image Sequences”, GCPR 2021
  • P. Noever-Castelos, L. Ardizzone, C. Balzani (2021). “Model updating of wind turbine blade cross sections with invertible neural networks”, Wind Energy, 2021 [link], [pdf]
  • J. Kleesiek, B. Kersjes, K. Ueltzhoeffer, J. M. Murray, C. Rother, U. Köthe, H. Schlemmer (2021). “Discovering Digital Tumor Signatures – Using Latent Code Representations to Manipulate and Classify Liver Lesions”, Cancers 2021. [link], [pdf]
  • A. Neishabouri, N. Wahl, A. Mairani, U. Köthe, M. Bangert (2021). “Long short-term memory networks for proton dose calculation in highly heterogeneous tissues”, Medical Physics 48(4):1893-1908, arXiv:2006.06085 [link], [arxiv], [pdf]
  • V.F. Ksoll, D. Gouliermis, E. Sabbi, J.E. Ryon, M. Robberto, M. Gennaro, R. Klessen, U. Köthe, … & A.E. Dolphin (2021). “Measuring Young Stars in Space and Time. I. The Photometric Catalog and Extinction Properties of N44”, The Astronomical Journal, 161(6), 256, arXiv:2012.00521. [link], [arxiv], [pdf]
  • V.F. Ksoll, D. Gouliermis, E. Sabbi, J.E. Ryon, M. Robberto, M. Gennaro, R. Klessen, U. Köthe, … & A.E. Dolphin (2021). “Measuring Young Stars in Space and Time. II. The Pre-main-sequence Stellar Content of N44”. The Astronomical Journal, 161(6), 257, arXiv:2012.00524. [link], [arxiv], [pdf]
  • B. Dillon, T. Plehn, C. Sauer, P. Sorrenson (2021). “Better latent spaces for better autoencoders”, SciPost Phys. 11, 061. [link], [pdf]
  • S. Bieringer, A. Butter, T. Heimel, S. Höche, U. Köthe, T. Plehn, S. Radev (2021). “Measuring QCD Splittings with Invertible Networks”, SciPost Phys. 10, 126. [link], [pdf]
  • J.H. Nölke, T. Adler, J. Gröhl, L. Ardizzone, C. Rother, U. Köthe, L. Maier-Hein (2021). “Invertible Neural Networks for Uncertainty Quantification in Photoacoustic Imaging”, BVM 2021, arXiv:2011.05110. [arxiv], [pdf]

2020

  • D. Trofimova, T. Adler, L. Kausch, L. Ardizzone, K. Maier-Hein, U. Köthe, C. Rother, and L. Maier-Hein (2020). “Representing Ambiguity in Registration Problems with Conditional Invertible Neural Networks”, NeurIPs Medical Imaging 2020 [arxiv], [pdf]
  • L. Ardizzone, R. Mackowiak, C. Rother, U. Köthe (2020). “Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification”, NeurIPS 2020 (oral presentation), arXiv:2001.06448 [arxiv], [pdf]
  • S. Radev, U. Mertens, A. Voss, L. Ardizzone, U. Köthe (2020). “BayesFlow: Learning complex stochastic models with invertible neural networks”, IEEE Trans. Neural Networks and Learning Systems, doi:10.1109/TNNLS.2020.3042395, arXiv:2003.06281. [link], [arxiv], [pdf]
  • V.F. Ksoll, L. Ardizzone, R. Klessen, U. Köthe, E. Sabbi, M. Robberto, D. Gouliermis, C. Rother, P. Zeidler, M. Gennaro (2020) “Stellar parameter determination from photometry using invertible neural networks”, Monthly Notices of the Royal Astronomical Society, Volume 499, Issue 4, Pages 5447–5485. [link], [arxiv], [pdf]
  • H. Abu Alhaija, S.K. Mustikovela, J. Thies, V. Jampani, M. Nießner, A. Geiger, C. Rother (2020) “Intrinsic Autoencoders for Joint Deferred Neural Rendering and Intrinsic Image Decomposition”, 3DV 2020. [arxiv]
  • C. Kamann, C. Rother (2020) “Benchmarking the Robustness of Semantic Segmentation Models with Respect to Common Corruptions”, IJCV 2020. [link]
  • T. Hodaň, M. Sundermeyer, B. Drost, Y. Labbé, E. Brachmann, F. Michel, C. Rother, J. Matas (2020). “BOP Challenge 2020 on 6D Object Localization”, ECCVW 2020. [arxiv]
  • F. Draxler, J. Schwarz, C. Schnörr, U. Köthe (2020). “Characterizing the Role of a Single Affine Coupling Layer in Affine Normalizing Flows”, GCPR 2020 (best paper honorable mention). [pdf] [video]
  • J. Schwarz, F. Draxler, U. Köthe, C. Schnörr (2020). “Riemannian SOS-Polynomial Normalizing Flows”, GCPR 2020. [pdf]
  • L. Ardizzone, J. Kruse, C. Lüth, N. Bracher, C. Rother, U. Köthe (2020). “Conditional Invertible Neural Networks for Diverse Image-to-Image Translation”, GCPR 2020, arXiv:2105.02104. [link], [arxiv], [pdf]
  • C. Kamann, C. Rother (2020). “Increasing the Robustness of Semantic Segmentation Models with Painting-by-Numbers”, ECCV 2020. [link]
  • H. Schilling, M. Gutsche, A. Brock, D. Späth, C. Rother, K. Krispin (2020). “Mind the Gap – A Benchmark for Dense Depth Prediction beyond Lidar”, 2nd Workshop on Safe Artificial Intelligence for Automated Driving, in conjunction with CVPR 2020.
  • P. Sorrenson, C. Rother, U. Köthe (2020). “Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)”, Intl. Conf. Learning Representations (ICLR) [arxiv], [pdf]
  • M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, A. Rousselot, R. Winterhalder, L. Ardizzone, U. Köthe (2020). “Invertible networks or partons to detector and back again”, SciPost Phys. 9, 074 [link], [pdf]
  • S.K. Mustikovela, V. Jampani, S. De Mello, S. Liu, U. Iqbal, C. Rother, J. Kautz (2020). “Self-Supervised Viewpoint Learning from Image Collections”, CVPR 2020. [pdf]
  • A. Bhowmik, S. Gumhold, C. Rother, E. Brachmann, “Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task”, CVPR 2020 (oral). [pdf]
  • F. Kluger, E. Brachmann, H. Ackermann, C .Rother, M.Y. Yang, B. Rosenhahn, “CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus”, CVPR 2020. [pdf] [project page]
  • C. Kamann, C. Rother, “Benchmarking the Robustness of Semantic Segmentation Models”, CVPR 2020. [pdf]
  • S. Haller, M. Prakash, L. Hutschenreiter, T. Pietzsch, C. Rother, F. Jug, P. Swoboda, B. Savchynskyy, “A Primal-Dual Solver for Large-Scale Tracking-by-Assignment”, AISTATS 2020. [pdf] [project website]
  • S. Tourani, A. Shekhovtsov, C. Rother, B. Savchynskyy, “Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization”, AISTATS 2020. [pdf]
  • A. Krull, P. Hirsch, C. Rother, A. Schiffrin, C. Krull (2020). “Artificial-intelligence-driven scanning probe microscopy”, Communications Physics volume 3, 54. [link]
  • S. Wolf, A. Bailoni, C. Pape, N. Rahaman, A. Kreshuk, U. Köthe, F.A. Hamprecht (2020), “The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning”. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020. [link], [pdf]

2019

  • T. Adler, L. Ayala, L. Ardizzone, H. Kenngott, A. Vemuri, B. Muller-Stich, C. Rother, U. Köthe, L. Maier-Hein, “Out of Distribution Detection for Intra-Operative Functional Imaging”, MICCAI UNSURE Workshop 2019. [pdf]
  • J. Kleesiek, J.N. Morshuis, F. Isensee, K. Deike-Hofmann, D. Paech, P. Kickingereder, U. Köthe, C. Rother, M. Forsting, W. Wick, M. Bendszus, H.-P. Schlemmer, A. Radbruch, “Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium?”, Investigative Radiology October 2019. [journal]
  • T. Leistner, H. Schilling, R. Mackowiak, S. Gumhold, C. Rother, “Learning to Think Outside the Box: Wide-Baseline Light-Field Depth Estimation from Low-Baseline Training Data”, 3DV 2019 (oral). [pdf][project page]
  • C. Kamann, C. Rother, “Benchmarking the Robustness of Semantic Segmentation Models”, Arxiv 2019. [pdf]
  • E. Brachmann, C. Rother, “Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses”, ICCV 2019. [pdf] [project page]
  • E. Brachmann, C. Rother, “Expert Sample Consensus Applied to Camera Re-Localization”, ICCV 2019. [pdf] [project page]
  • L. Ardizzone, C. Lüth, J. Kruse, C. Rother, U. Köthe, “Guided Image Generation with Conditional Invertible Neural Networks”, GCPR 2020, arXiv:1907.02392, [arxiv] [pdf] [supplement]
  • J. Kruse, L. Ardizzone, C. Rother, U. Köthe, “Benchmarking Invertible Architectures on Inverse Problems”, First Workshop on Invertible Neural Networks and Normalizing Flows, ICML 2019. [arxiv] [pdf]
  • T.-G. Nguyen, L. Ardizzone, U. Koethe (2019). “Training Invertible Neural Networks as Autoencoders”, GCPR 2019, Springer LNCS 11824, arXiv:2303.11239. [link], [arxiv], [pdf]
  • W. Li, O. Hosseini Jafari, C. Rother, “Localizing Common Objects Using Common Component Activation Map”, Explainable AI Workshop, CVPR 2019 [pdf]
  • T. J. Adler, L. Ardizzone, A. Vemuri, L. Ayala, J. Gröhl, T. Kirchner, S. Wirkert, J. Kruse, C. Rother, U. Köthe, L. Maier-Hein, “Uncertainty-Aware Performance Assessment of Optical Imaging Modalities with Invertible Neural Networks”, IPCAI 2019 [arxiv]
  • Alexander Kirillov, Kaiming He, Ross Girshick, Carsten Rother, Piotr Dollar, “Panoptic Segmentation”, CVPR 2019, [arxiv]
  • L. Ardizzone, J. Kruse, S. Wirkert, D. Rahner, E.W. Pellegrini, R.S. Klessen, L. Maier-Hein, C. Rother, U. Köthe, “Analyzing Inverse Problems with Invertible Neural Networks”, ICLR 2019 [arxiv] [OpenReview] [pdf]
  • S. Berg, D. Kutra, …, U. Köthe, F.A. Hamprecht, A. Kreshuk (2019): “ilastik: interactive machine learning for (bio)image analysis”, Nature Methods, vol. 16, pages 1226–1232 [link]

2018

  • H. Abu Alhaija, S.K. Mustikovela, A. Geiger, C. Rother, “Geometric Image Synthesis”, ACCV 2018 [pdf] [video]
  • O. Hosseini Jafari*, S.K. Mustikovela*, K. Pertsch, E. Brachmann, C. Rother, “iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects”, ACCV 2018 [pdf] (*equal contribution)
  • W. Li*, O. Hosseini Jafari*, C. Rother, “Deep Object Co-Segmentation”, ACCV 2018 [pdf] (*equal contribution)
  • R. Mackowiak, P. Lenz, O. Ghori, F. Diego, O. Lange, C. Rother, “CEREALS – Cost-Effective REgion-based Active Learning for Semantic Segmentation”, BMVC 2018. [pdf] [supp]
  • S. Tourani, A. Shekhovtsov, C. Rother, B.Savchynskyy, “MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models”, ECCV 2018. [pdf]
  • T. Hodan, F. Michel, E. Brachmann, W. Kehl, A. Glent Buch, D. Kraft, B. Drost, J. Vidal, S. Ihrke, X. Zabulis, C. Sahin, F. Manhardt, F. Tombari, T. Kim, J. Matas, C. Rother, “BOP: Benchmark for 6D Object Pose Estimation”, ECCV 2018. [pdf]
  • H. Abu Alhaija, S.K. Mustikovela, L. Mescheder, A. Geiger, C. Rother, “Augmented Reality Meets Computer Vision Efficient Data Generation for Urban Driving Scenes”, IJCV 2018. [link] [pdf]
  • E. Brachmann, C. Rother, “Learning Less is More – 6D Camera Localization via 3D Surface Regression”, CVPR 2018. [pdf] [project page]
  • H. Schilling, M. Diebold, C. Rother, B. Jähne, “Trust your Model: Light Field Depth Estimation with inline Occlusion Handling”, CVPR 2018. [pdf]
  • S. Haller, P. Swoboda, B. Savchynskyy, “Exact MAP-Inference by Confining Combinatorial Search with LP Relaxation”, AAAI 2018. [pdf]

2017

  • A. Arnab, S. Zheng, S. Jayasumana, B. Romera-Paredes, M. Larsson, A. Kirillov, B. Savchynskyy, C. Rother, F. Kahl, P.H.S. Torr, “Conditional Random Fields meet Deep Neural Networks for Semantic Segmentation”, IEEE Signal Processing Magazine, Special Issue in Deep Learning for Visual Understanding, White Paper, 2017. [pdf]
  • H. Abu Alhaija , S. K. Mustikovela, L. Mescheder, A. Geiger, C. Rother, “Augmented Reality Meets Deep Learning for Car Instance Segmentation in Urban Scenes”, BMVC 2017. [pdf][extended Arxiv pdf]
  • W. Li, O. Hosseini Jafari, C. Rother, ”Semantic-Aware Image Smoothing”, VMV 2017. [pdf]
  • J. Kruse, C. Rother, U. Schmidt, “Learning to Push the Limits of Efficient FFT-based Image Deconvolution”, ICCV 2017. [pdf][supp]
  • A. Behl*, O. Hosseini Jafari*, S. K. Mustikovela*, H. Abu Alhaija, C. Rother, A. Geiger, “Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?”, ICCV 2017. [pdf][supp] (*equal contribution)
  • S. Ramos, S. Gehrig, P. Pinggera, U. Franke, C. Rother. “Detecting Unexpected Obstacles for Self-Driving Cars: Fusing Deep Learning and Geometric Modeling.” , Intelligent Vehicles Symposium (IV) (oral). [pdf]
  • E. Brachmann, A. Krull, S. Nowozin, J. Shotton, F. Michel, S. Gumhold, C. Rother, “DSAC – Differentiable RANSAC for Camera Localization”, CVPR 2017 (oral). [pdf][project page]
  • P. Swoboda, J. Kuske, B. Savchynskyy, “A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems”, CVPR 2017. [pdf]
  • P. Swoboda, C. Rother, H. Abu Alhaija, D. Kainmueller, B. Savchynskyy, “A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching”, CVPR 2017. [pdf]
  • A. Kirillov, E. Levinkov, B. Andres, B. Savchynskyy, C. Rother, “InstanceCut: from Edges to Instances with MultiCut”, CVPR 2017. [pdf]
  • F. Michel, A. Kirillov, E. Brachmann, A. Krull, S. Gumhold, B. Savchynskyy, C. Rother, “Global Hypothesis Generation for 6D Object Pose Estimation”, CVPR 2017. [pdf][project page]
  • E. Levinkov, J. Uhrig, S. Tang, M. Omran, E. Insafutdinov, A. Kirillov, C. Rother, T. Brox, B. Schiele, B. Andres, “Joint Graph Decomposition & Node Labeling: Problem, Algorithms, Applications”, CVPR 2017. [pdf]
  • A. Krull, E. Brachmann, S. Nowozin, F. Michel, J. Shotton, C. Rother, “PoseAgent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning”, CVPR 2017. [pdf][project page]
  • D. Massiceti, A. Krull, E. Brachmann, C. Rother, P.H.S. Torr, “Random Forests versus Neural Networks − What’s Best for Camera Localization?”, ICRA 2017. [pdf]
  • D. Schlesinger, F. Jug, G. Myers, C. Rother, D. Kainmüller, “Crowd Sourcing Image Segmentation with iaSTAPLE”, ISBI 2017. [pdf]
  • O. Hosseini Jafari, O. Groth, A. Kirillov, M. Y. Yang, C. Rother, “Analyzing Modular CNN Architectures for Joint Depth Prediction and Semantic Segmentation”, ICRA 2017. [pdf]

2016

  • S. K. Mustikovela, M. Y. Yang, C. Rother, “Can Ground Truth Label Propagation from Video help Semantic Segmentation?”, Video Segmentation Workshop, ECCV 2016. [pdf]
  • P. Pinggera, S. Ramos, S. Gehrig, U. Franke, C. Rother, R. Mester, “Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles”, IROS 2016. [pdf]
  • A. Kirillov, A. Shekhovtsov, C. Rother, B. Savchynskyy, “Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization”, NIPS 2016. [pdf]
  • A. Kirillov, D. Schlesinger, S. Zheng, B. Savchynskyy, P.H.S. Torr, C. Rother, “Joint Training of Generic CNN-CRF Models with Stochastic Optimization”, ACCV 2016. [pdf]
  • J.H. Kappes, P. Swoboda, B. Savchynskyy, T. Hazan, C. Schnörr, “Multicuts and Perturb & MAP for Probabilistic Graph Clustering”, in J. Math. Imag. Vision 2016. [pdf] [bib]
  • P. Swoboda, A. Shekhovtsov, J.H. Kappes, C. Schnörr, B. Savchynskyy, “Partial Optimality by Pruning for MAP-Inference with General Graphical Models”, in IEEE Trans. Patt. Anal. Mach. Intell., vol. 38, July 2016, pp. 1370-1382. [preprint] [bib]
  • A. Sellent, C. Rother, S. Roth,”Stereo Video Deblurring”, ECCV 2016. [pdf][supp]
  • D. L. Richmond, D. Kainmueller, M. Y. Yang, E. W. Myers, C. Rother, “Mapping Auto-context Decision Forests to Deep ConvNets for Semantic Segmentation”, BMVC 2016. [pdf] [supplement] [Extended Arxiv pdf]
  • L. A. Royer, D. L. Richmond, C. Rother, B. Andres, D. Kainmueller, “Convexity Shape Constraints for Image Segmentation”, CVPR 2016. [pdf]
  • J. Mund, F. Michel, F. Dieke-Meier, H. Fricke, L. Meyer, C. Rother, “Introducing LiDAR Point Cloud-based Object Classification for Safer Apron Operations”, ESAVS 2016. [pdf]
  • E. Brachmann, F. Michel, A. Krull, M. Y. Yang, S. Gumhold, C. Rother, “Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image”, CVPR 2016. [pdf][supplement][project page]
  • F. Matulic, W. Büschel, M. Y. Yang, S. Ihrke, A. Ramraika, C. Rother, R. Dachselt, “Smart Ubiquitous Projection: Discovering Surfaces for the Projection of Adaptive Content”, Proceedings of the 34th Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, 2016. [pdf]
  • O. Hosseini Jafari, M. Y. Yang, “Real-Time RGB-D based Template Matching Pedestrian Detection”, ICRA 2016. [pdf]

2015

  • A. Kirillov, D. Schlesinger, D. Vetrov, C. Rother, B. Savchynskyy, “M-Best-Diverse Labelings for Submodular Energies and Beyond”, NIPS 2015. [pdf with supplementary material][bib]
  • A. Krull, E. Brachmann, F. Michel, M. Y. Yang, S. Gumhold, C. Rother, “Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images”, ICCV 2015. [pdf][project page]
  • A. Kirillov, B. Savchynskyy, D. Schlesinger, D. Vetrov, C. Rother, “Inferring M-Best Diverse Solutions in a Single One”, ICCV 2015. [pdf with supplementary material][bib][video spotlight]
  • R. Nair, A. Fitzgibbon, D. Kondermann, C. Rother. “Reflection Modelling for Passive Stereo”, ICCV 2015. [pdf]
  • H. Abu Alhaija, A. Sellent, D. Kondermann, C. Rother, “GraphFlow – 6D Large Displacement Scene Flow via Graph Matching”, German Conference on Pattern Recognition (GCPR, a.k.a. DAGM), 2015. [project][pdf]
  • M. Cheng, V. Prisacariu, S. Zheng, P. Torr, C Rother, “DenseCut: Densely Connected CRFs for Realtime GrabCut”, Computer Graphics Forum (CGF), 2015 (oral & journal). [Project][pdf][bib][code]
  • S. Zheng, V. Prisacariu, M Averkiou, M. Cheng, N. Mitra, J. Shotton, P. Torr, C. Rother. “Object Proposal Estimation in Depth Images using Compact 3D Shape Manifolds”, German Conference on Pattern Recognition (GCPR, a.k.a. DAGM), 2015. (oral). [pdf]
  • J. Mund, A. Zouhar, L. Meyer, H. Fricke, C. Rother, “Performance Evaluation of LiDAR Point Clouds towards Automated FOD Detection on Airport Aprons”, ATACCS 2015. [pdf]
  • F. Michel, A. Krull, E. Brachmann, M. Y. Yang, S. Gumhold, C. Rother, “Pose Estimation of Kinematic Chain Instances via Object Coordinate Regression”, BMVC 2015. [pdf][Supplementary_Material][Extended_Abstract][Dataset][project page]
  • D. Richmond, D. Kainmueller, B. Glocker, C. Rother, G. Myers, “Uncertainty-driven Forest Predictors for Vertebra Localization and Segmentation”, MICCAI 2015. [pdf]
  • A. Zouhar, C. Rother, S. Fuchs, “Semantic 3-D Labeling of ear implants using a global parametric transition prior”, MICCAI 2015. [pdf][Ear data set]
  • U. Schmidt, J. Jancsary, S. Nowozin, S. Roth, C. Rother, “Cascades of regression tree fields for image restoration”, IEEE Transactions on Pattern Analysis and Machine Intelligence 2015. [pdf]
  • W. Huang, X. Gong, M. Ying Yang, “Joint object segmentation and depth upsampling”, Signal Processing Letters, 22(2):192–196, 2015. [link]
  • Schelten, S. Nowozin, J. Jancsary, C. Rother, and S. Roth, “Interleaved regression tree field cascades for blind image deconvolution”, in IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Beach, HI, Jan. 2015, pp. 494-501. [preprint]
  • J.H. Kappes, B. Andres, F.A. Hamprecht, C. Schnörr, S. Nowozin, D. Batra, S. Kim, T. Kroeger, B.X. Kausler, J. Lellmann, N. Komodakis, B. Savchynskyy, C. Rother, “A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems”, IJCV 2015. [bib][preprint] [supplementary material 1] [supplementary material 2]
  • A. Shekhovtsov, P. Swoboda, B. Savchynskyy, “Maximum Persistency via Iterative Relaxed Inference with Graphical Models”, CVPR 2015. [bib][pdf with supplementary material]
  • J. Kappes, P. Swoboda, B. Savchynskyy, T. Hazan, C. Schnörr, “Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts”, SSVM 2015 – oral presentation. [bib][pdf]

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

  • C. Rother, “Analyse initialer Positionsschätzungen bei der Bildfolgenauswertung”, DAGM 1999.