no code implementations • 30 Sep 2023 • Marius Schubert, Tobias Riedlinger, Karsten Kahl, Matthias Rottmann
Here, we propose a composite active learning framework including a label review module for deep object detection.
1 code implementation • 13 Jun 2023 • Tobias Riedlinger, Marius Schubert, Sarina Penquitt, Jan-Marcel Kezmann, Pascal Colling, Karsten Kahl, Lutz Roese-Koerner, Michael Arnold, Urs Zimmermann, Matthias Rottmann
In order to address these two issues, we propose LidarMetaDetect (LMD), a light-weight post-processing scheme for prediction quality estimation.
no code implementations • 13 Mar 2023 • Marius Schubert, Tobias Riedlinger, Karsten Kahl, Daniel Kröll, Sebastian Schoenen, Siniša Šegvić, Matthias Rottmann
In this work, we for the first time introduce a benchmark for label error detection methods on object detection datasets as well as a label error detection method and a number of baselines.
1 code implementation • 13 Mar 2023 • Kira Maag, Tobias Riedlinger
In recent years, deep neural networks have defined the state-of-the-art in semantic segmentation where their predictions are constrained to a predefined set of semantic classes.
no code implementations • 21 Dec 2022 • Tobias Riedlinger, Marius Schubert, Karsten Kahl, Hanno Gottschalk, Matthias Rottmann
Active learning as a paradigm in deep learning is especially important in applications involving intricate perception tasks such as object detection where labels are difficult and expensive to acquire.
no code implementations • 30 May 2022 • Julian Burghoff, Robin Chan, Hanno Gottschalk, Annika Muetze, Tobias Riedlinger, Matthias Rottmann, Marius Schubert
Training deep neural networks is already resource demanding and so is also their uncertainty quantification.
1 code implementation • 9 Jul 2021 • Tobias Riedlinger, Matthias Rottmann, Marius Schubert, Hanno Gottschalk
The vast majority of uncertainty quantification methods for deep object detectors such as variational inference are based on the network output.