no code implementations • 17 Apr 2023 • Bingchen Zhao, Jiahao Wang, Wufei Ma, Artur Jesslen, Siwei Yang, Shaozuo Yu, Oliver Zendel, Christian Theobalt, Alan Yuille, Adam Kortylewski
Enhancing the robustness of vision algorithms in real-world scenarios is challenging.
1 code implementation • Asian Conference on Computer Vision (ACCV) Workshops 2022 • Daniel Steininger, Andreas Kriegler, Wolfgang Pointner, Verena Widhalm, Julia Simon, Oliver Zendel
The results are quite promising for future applications and provide essential insights regarding the selection of aggregation strategies as well as current potentials and limitations of similar approaches in this research domain.
no code implementations • CVPR 2022 • Oliver Zendel, Matthias Schörghuber, Bernhard Rainer, Markus Murschitz, Csaba Beleznai
The dataset consists of more than 5000 unique driving scenes from all over the world with a focus on visually challenging scenes, such as diverse weather conditions, lighting situations, and camera characteristics.
no code implementations • CVPR 2019 • Oliver Zendel, Markus Murschitz, Marcel Zeilinger, Daniel Steininger, Sara Abbasi, Csaba Beleznai
In this paper, we intro-duce the first public dataset for semantic scene understand-ing for trains and trams: RailSem19.
no code implementations • ECCV 2018 • Oliver Zendel, Katrin Honauer, Markus Murschitz, Daniel Steininger, Gustavo Fernandez Dominguez
We have conducted a thorough risk analysis to identify situations and aspects that can reduce the output performance for these tasks.
no code implementations • CVPR 2017 • Oliver Zendel, Katrin Honauer, Markus Murschitz, Martin Humenberger, Gustavo Fernandez Dominguez
However, major questions concerning quality and usefulness of test data for CV evaluation are still unanswered.
no code implementations • ICCV 2015 • Oliver Zendel, Markus Murschitz, Martin Humenberger, Wolfgang Herzner
This checklist can be used to evaluate existing test datasets by quantifying the amount of covered hazards.