no code implementations • 3 Sep 2023 • Maximilian Nitsche, S. Karthik Mukkavilli, Niklas Kühl, Thomas Brunschwiler
To achieve robust and accurate evaluations of building damage detection and classification, we evaluated different deep learning models with residual, squeeze and excitation, and dual path network backbones, as well as ensemble techniques.
no code implementations • 13 Jul 2022 • Daniel Bogdoll, Meng Zhang, Maximilian Nitsche, J. Marius Zöllner
As a safety-critical problem, however, anomaly detection is a huge hurdle towards a large-scale deployment of autonomous vehicles in the real world.
no code implementations • 10 May 2022 • Max Schemmer, Patrick Hemmer, Maximilian Nitsche, Niklas Kühl, Michael Vössing
However, we find no effect of explanations on users' performance compared to sole AI predictions.
1 code implementation • 3 May 2022 • Daniel Bogdoll, Enrico Eisen, Maximilian Nitsche, Christin Scheib, J. Marius Zöllner
Tremendous progress in deep learning over the last years has led towards a future with autonomous vehicles on our roads.