Category Archives: news
Carsten Rother among 9 most influential CV scholars in Europe
Carsten Rother was selected by aminer to be among the 9 most influential Computer Vision researchers in Europe. See: AI 2000 Computer Vision Most Influential Scholars. https://www.aminer.org/ai2000/cv
Continue readingFour Papers accepted to CVPR 2020
S.K. Mustikovela, V. Jampani, S. De Mello, S. Liu, U. Iqbal, C. Rother, J. Kautz “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 […]
Continue readingECCV Tutorial (Half-day) on Normalizing Flow
Ullrich Köthe, Markus Brubaeker (York University/Toronto) and Carsten Rother offer a half-day tutorial at ECCV 2020 in Glasgow on “Normalizing Flows in Computer Vision”
Continue readingPaper accepted to 3DV 2019
Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift [pdf] [project page]
Continue readingTwo papers accepted to ICCV 2019
Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses [pre-print] [project page] Expert Sample Consensus Applied to Camera Re-Localization [pre-print] [project page] See you in Seoul!
Continue readingAssessing Medical Optical Devices with INNs – joint work with Lena Maier Hein’s team
Paper accepted to IPCAI 2019. Find arxiv paper here https://arxiv.org/abs/1903.03441 Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks. Tim J. Adler, Lynton Ardizzone, Anant Vemuri, Leonardo Ayala, Janek Gröhl, Thomas Kirchner, Sebastian Wirkert, Jakob Kruse, Carsten Rother, Ullrich Köthe, Lena Maier-Hein. IPCAI 2019.
Continue readingCVPR 19 Paper on Panoptic Segmentation accepted
Panoptic Segmentation, Alexander Kirillov, Kaiming He, Ross Girshick, Carsten Rother, Piotr Dollar, CVPR 2019 arxiv version https://arxiv.org/abs/1801.00868
Continue readingCheck out our ICLR Paper and Blog on Analyzing Inverse Problems with Invertible Neural Networks
Here is a gentle introduction to our Invertible Neural Network architecture to tackle ambiguous inverse problems. [Update: Accepted at ICLR 2019!]
Continue reading