Zobrazeno 1 - 10
of 1 431
pro vyhledávání: '"A. Riedlinger"'
Autor:
Beck, Samuel, Doerr, Nina, Kurzhals, Kuno, Riedlinger, Alexander, Schmierer, Fabian, Sedlmair, Michael, Koch, Steffen
Sports visualization has developed into an active research field over the last decades. Many approaches focus on analyzing movement data recorded from unstructured situations, such as soccer. For the analysis of choreographed activities like formatio
Externí odkaz:
http://arxiv.org/abs/2404.04100
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
Autor:
Maag, Kira, Riedlinger, Tobias
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. They are to be deployed in applications such as automated driving, altho
Externí odkaz:
http://arxiv.org/abs/2303.06920
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:
Mittelmann, Steffen, Riedlinger, Jan, Buchner, Benedikt, Schwarz-Selinger, Thomas, Mayer, Matej, Pretzler, Georg
Publikováno v:
Journal of Applied Physics; 9/14/2024, Vol. 136 Issue 10, p1-7, 7p
Publikováno v:
Electricity, Vol 5, Iss 2, Pp 174-210 (2024)
In the context of the energy transition, the share of new loads such as charging infrastructure for electromobility and electric heat pumps as well as feed-ins such as photovoltaic systems will steadily increase. This results in an increased degree o
Externí odkaz:
https://doaj.org/article/5d24d51896fc426c8d19d30e47217a51
Publikováno v:
AIP Advances, Vol 14, Iss 8, Pp 085304-085304-7 (2024)
Unconfined high-energy-density laser plasmas are known to emit broadband ion spectra in terms of species and their kinetic energy. The diagnostic of choice is often the Thomson parabola method, which is well-studied for the particle energies in the M
Externí odkaz:
https://doaj.org/article/131239c71e214351b7182c6ff7b1cd71
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