2 code implementations • 29 Nov 2022 • David Bojanić, Kristijan Bartol, Josep Forest, Stefan Gumhold, Tomislav Petković, Tomislav Pribanić
Learning universal representations across different applications domain is an open research problem.
1 code implementation • 7 Jul 2022 • Tobias Hänel, Nishant Kumar, Dmitrij Schlesinger, Mengze Li, Erdem Ünal, Abouzar Eslami, Stefan Gumhold
The performance of deep neural networks for image recognition tasks such as predicting a smiling face is known to degrade with under-represented classes of sensitive attributes.
1 code implementation • 10 Jun 2021 • Nishant Kumar, Pia Hanfeld, Michael Hecht, Michael Bussmann, Stefan Gumhold, Nico Hoffmann
Normalizing flows are prominent deep generative models that provide tractable probability distributions and efficient density estimation.
1 code implementation • 6 Dec 2020 • Nishant Kumar, Stefan Gumhold
However, it is challenging to analyze the reliability of these CNNs for the image fusion tasks since no groundtruth is available.
1 code implementation • 26 Jan 2020 • Nishant Kumar, Nico Hoffmann, Matthias Kirsch, Stefan Gumhold
The medical image fusion combines two or more modalities into a single view while medical image translation synthesizes new images and assists in data augmentation.
1 code implementation • CVPR 2020 • Aritra Bhowmik, Stefan Gumhold, Carsten Rother, Eric Brachmann
We address a core problem of computer vision: Detection and description of 2D feature points for image matching.
no code implementations • 19 Sep 2019 • Titus Leistner, Hendrik Schilling, Radek Mackowiak, Stefan Gumhold, Carsten Rother
In order to work with wide-baseline light fields, we introduce the idea of EPI-Shift: To virtually shift the light field stack which enables to retain a small receptive field, independent of the disparity range.
1 code implementation • 11 Aug 2019 • Nishant Kumar, Nico Hoffmann, Martin Oelschlägel, Edmund Koch, Matthias Kirsch, Stefan Gumhold
Multimodal medical image fusion helps in combining contrasting features from two or more input imaging modalities to represent fused information in a single image.
2 code implementations • 16 Jun 2019 • Ulrik Günther, Tobias Pietzsch, Aryaman Gupta, Kyle I. S. Harrington, Pavel Tomancak, Stefan Gumhold, Ivo F. Sbalzarini
Life science today involves computational analysis of a large amount and variety of data, such as volumetric data acquired by state-of-the-art microscopes, or mesh data resulting from analysis of such data or simulations.
Graphics
no code implementations • CVPR 2017 • Frank Michel, Alexander Kirillov, Eric Brachmann, Alexander Krull, Stefan Gumhold, Bogdan Savchynskyy, Carsten Rother
Most modern approaches solve this task in three steps: i) Compute local features; ii) Generate a pool of pose-hypotheses; iii) Select and refine a pose from the pool.
1 code implementation • 28 Nov 2016 • Alexander Matthes, Axel Huebl, René Widera, Sebastian Grottel, Stefan Gumhold, Michael Bussmann
The computation power of supercomputers grows faster than the bandwidth of their storage and network.
Distributed, Parallel, and Cluster Computing
4 code implementations • CVPR 2017 • Eric Brachmann, Alexander Krull, Sebastian Nowozin, Jamie Shotton, Frank Michel, Stefan Gumhold, Carsten Rother
The most promising approach is inspired by reinforcement learning, namely to replace the deterministic hypothesis selection by a probabilistic selection for which we can derive the expected loss w. r. t.
no code implementations • CVPR 2016 • Eric Brachmann, Frank Michel, Alexander Krull, Michael Ying Yang, Stefan Gumhold, Carsten Rother
In recent years, the task of estimating the 6D pose of object instances and complete scenes, i. e. camera localization, from a single input image has received considerable attention.
no code implementations • ICCV 2015 • Alexander Krull, Eric Brachmann, Frank Michel, Michael Ying Yang, Stefan Gumhold, Carsten Rother
This is done by describing the posterior density of a particular object pose with a convolutional neural network (CNN) that compares an observed and rendered image.