1 code implementation • 1 Apr 2022 • Steve Dias Da Cruz, Bertram Taetz, Thomas Stifter, Didier Stricker
Learning on synthetic data and transferring the resulting properties to their real counterparts is an important challenge for reducing costs and increasing safety in machine learning.
1 code implementation • 1 Apr 2022 • Steve Dias Da Cruz, Bertram Taetz, Thomas Stifter, Didier Stricker
While input images close to known samples will converge to the same or similar attractor, input samples containing unknown features are unstable and converge to different training samples by potentially removing or changing characteristic features.
no code implementations • 7 May 2021 • Steve Dias Da Cruz, Bertram Taetz, Oliver Wasenmüller, Thomas Stifter, Didier Stricker
Common domain shift problem formulations consider the integration of multiple source domains, or the target domain during training.
no code implementations • 6 Nov 2020 • Steve Dias Da Cruz, Bertram Taetz, Thomas Stifter, Didier Stricker
Our method exploits the availability of identical sceneries under different illumination and environmental conditions for which we formulate a partially impossible reconstruction target: the input image will not convey enough information to reconstruct the target in its entirety.
no code implementations • ICCV 2015 • Christian Bailer, Bertram Taetz, Didier Stricker
In this article we present a dense correspondence field approach that is much less outlier-prone and thus much better suited for optical flow estimation than approximate nearest neighbor fields.
no code implementations • ICCV 2015 • Christian Bailer, Bertram Taetz, Didier Stricker
In this paper we present a dense correspondence field approach that is much less outlier prone and thus much better suited for optical flow estimation than approximate nearest neighbor fields.