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

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Four 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 […]

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ECCV 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”

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Paper accepted to 3DV 2019

Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift [pdf] [project page]

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Two 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!

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Assessing 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.

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CVPR 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

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Check 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!]

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Our excellent cluster “Structures” – where ML is used to find structures in data and the physical world – got funded by DFG

STRUCTURES: A unifying approach to emergent phenomena in the physical world, mathematics, and complex data.

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