no code implementations • CVPR 2022 • Titus Leistner, Radek Mackowiak, Lynton Ardizzone, Ullrich Köthe, Carsten Rother
We argue that this is due current methods only considering a single "true" depth, even when multiple objects at different depths contributed to the color of a single pixel.
2 code implementations • CVPR 2021 • Radek Mackowiak, Lynton Ardizzone, Ullrich Köthe, Carsten Rother
Generative classifiers (GCs) are a promising class of models that are said to naturally accomplish these qualities.
3 code implementations • NeurIPS 2020 • Lynton Ardizzone, Radek Mackowiak, Carsten Rother, Ullrich Köthe
In this work, firstly, we develop the theory and methodology of IB-INNs, a class of conditional normalizing flows where INNs are trained using the IB objective: Introducing a small amount of {\em controlled} information loss allows for an asymptotically exact formulation of the IB, while keeping the INN's generative capabilities intact.
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.
no code implementations • 23 Oct 2018 • Radek Mackowiak, Philip Lenz, Omair Ghori, Ferran Diego, Oliver Lange, Carsten Rother
State of the art methods for semantic image segmentation are trained in a supervised fashion using a large corpus of fully labeled training images.