no code implementations • 6 Mar 2023 • Maxim Tatarchenko, Kilian Rambach
Compared to existing methods, the design of our approach is extremely simple: it boils down to computing a point cloud histogram and passing it through a multi-layer perceptron.
no code implementations • 17 Feb 2022 • Adriana-Eliza Cozma, Lisa Morgan, Martin Stolz, David Stoeckel, Kilian Rambach
Automated vehicles need to detect and classify objects and traffic participants accurately.
no code implementations • 27 Sep 2021 • Kanil Patel, William Beluch, Kilian Rambach, Michael Pfeiffer, Bin Yang
The focus of this article is to learn deep radar spectra classifiers which offer robust real-time uncertainty estimates using label smoothing during training.
1 code implementation • ICCV 2021 • Elias Eulig, Piyapat Saranrittichai, Chaithanya Kumar Mummadi, Kilian Rambach, William Beluch, Xiahan Shi, Volker Fischer
We also argue that it is necessary for DNNs to exploit GO to overcome shortcut learning.
no code implementations • 28 Jun 2021 • Chaithanya Kumar Mummadi, Robin Hutmacher, Kilian Rambach, Evgeny Levinkov, Thomas Brox, Jan Hendrik Metzen
This paper focuses on the fully test-time adaptation setting, where only unlabeled data from the target distribution is required.
no code implementations • 1 Jun 2021 • Kanil Patel, William Beluch, Kilian Rambach, Adriana-Eliza Cozma, Michael Pfeiffer, Bin Yang
Deep learning (DL) has recently attracted increasing interest to improve object type classification for automotive radar. In addition to high accuracy, it is crucial for decision making in autonomous vehicles to evaluate the reliability of the predictions; however, decisions of DL networks are non-transparent.
no code implementations • NeurIPS 2021 • Chaithanya Kumar Mummadi, Robin Hutmacher, Kilian Rambach, Evgeny Levinkov, Thomas Brox, Jan Hendrik Metzen
This paper focuses on the fully test-time adaptation setting, where only unlabeled data from the target distribution is required.