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1
Gradient-Based Quantification of Epistemic Uncertainty for ..:
, In:
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
,
Riedlinger, Tobias
;
Rottmann, Matthias
;
Schubert, Marius
. - p. 3910-3920 , 2023
Link:
https://doi.org/10.1109/WACV56688.2023.00391
RT T1 <cTitle>2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)</cTitle>: T1 <Title>Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10030773&Exemplar=1&LAN=DE A1 Riedlinger, Tobias A1 Rottmann, Matthias A1 Schubert, Marius A1 Gottschalk, Hanno YR 2023 SN 2642-9381 K1 Uncertainty K1 Three-dimensional displays K1 Monte Carlo methods K1 Object detection K1 Detectors K1 Computer architecture K1 Probabilistic logic K1 Algorithms: Image recognition and understanding (object detection K1 categorization K1 segmentation) K1 Machine learning architectures K1 formulations K1 and algorithms (including transfer K1 low-shot K1 semi- K1 self- K1 and un-supervised learning) SP 3910 OP 3920 LK http://dx.doi.org/https://doi.org/10.1109/WACV56688.2023.00391 DO https://doi.org/10.1109/WACV56688.2023.00391 SF ELIB - SuUB Bremen
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