1 code implementation • 20 Mar 2024 • Yumeng Li, William Beluch, Margret Keuper, Dan Zhang, Anna Khoreva
Despite tremendous progress in the field of text-to-video (T2V) synthesis, open-sourced T2V diffusion models struggle to generate longer videos with dynamically varying and evolving content.
no code implementations • 19 Aug 2023 • Dan Zhang, Kaspar Sakmann, William Beluch, Robin Hutmacher, Yumeng Li
Within the context of autonomous driving, encountering unknown objects becomes inevitable during deployment in the open world.
no code implementations • 10 Oct 2022 • Sophie Henning, William Beluch, Alexander Fraser, Annemarie Friedrich
With this survey, the first overview on class imbalance in deep-learning based NLP, we provide guidance for NLP researchers and practitioners dealing with imbalanced data.
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 • 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.
1 code implementation • ICLR 2021 • Kanil Patel, William Beluch, Bin Yang, Michael Pfeiffer, Dan Zhang
The goal of this paper is to resolve the identified issues of HB in order to provide calibrated confidence estimates using only a small holdout calibration dataset for bin optimization while preserving multi-class ranking accuracy.
no code implementations • 16 Dec 2019 • Kanil Patel, William Beluch, Dan Zhang, Michael Pfeiffer, Bin Yang
Uncertainty estimates help to identify ambiguous, novel, or anomalous inputs, but the reliable quantification of uncertainty has proven to be challenging for modern deep networks.