Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Schubert, Marius"'
Obtaining annotations for complex computer vision tasks such as object detection is an expensive and time-intense endeavor involving a large number of human workers or expert opinions. Reducing the amount of annotations required while maintaining alg
Externí odkaz:
http://arxiv.org/abs/2310.00372
Autor:
Riedlinger, Tobias, Schubert, Marius, Penquitt, Sarina, Kezmann, Jan-Marcel, Colling, Pascal, Kahl, Karsten, Roese-Koerner, Lutz, Arnold, Michael, Zimmermann, Urs, Rottmann, Matthias
Object detection on Lidar point cloud data is a promising technology for autonomous driving and robotics which has seen a significant rise in performance and accuracy during recent years. Particularly uncertainty estimation is a crucial component for
Externí odkaz:
http://arxiv.org/abs/2306.07835
Autor:
Schubert, Marius, Riedlinger, Tobias, Kahl, Karsten, Kröll, Daniel, Schoenen, Sebastian, Šegvić, Siniša, Rottmann, Matthias
Labeling datasets for supervised object detection is a dull and time-consuming task. Errors can be easily introduced during annotation and overlooked during review, yielding inaccurate benchmarks and performance degradation of deep neural networks tr
Externí odkaz:
http://arxiv.org/abs/2303.06999
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. Development of active learning methods in such f
Externí odkaz:
http://arxiv.org/abs/2212.10836
Autor:
Burghoff, Julian, Chan, Robin, Gottschalk, Hanno, Muetze, Annika, Riedlinger, Tobias, Rottmann, Matthias, Schubert, Marius
Bringing deep neural networks (DNNs) into safety critical applications such as automated driving, medical imaging and finance, requires a thorough treatment of the model's uncertainties. Training deep neural networks is already resource demanding and
Externí odkaz:
http://arxiv.org/abs/2205.14917
The vast majority of uncertainty quantification methods for deep object detectors such as variational inference are based on the network output. Here, we study gradient-based epistemic uncertainty metrics for deep object detectors to obtain reliable
Externí odkaz:
http://arxiv.org/abs/2107.04517
In object detection with deep neural networks, the box-wise objectness score tends to be overconfident, sometimes even indicating high confidence in presence of inaccurate predictions. Hence, the reliability of the prediction and therefore reliable u
Externí odkaz:
http://arxiv.org/abs/2010.01695
Autor:
Rottmann, Matthias, Schubert, Marius
In the semantic segmentation of street scenes the reliability of the prediction and therefore uncertainty measures are of highest interest. We present a method that generates for each input image a hierarchy of nested crops around the image center an
Externí odkaz:
http://arxiv.org/abs/1904.04516
We consider arbitrage free valuation of European options in Black-Scholes and Merton markets, where the general structure of the market is known, however the specific parameters are not known. In order to reflect this subjective uncertainty of a mark
Externí odkaz:
http://arxiv.org/abs/1602.04848