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1 Ergebnisse
1
SHIFT: A Synthetic Driving Dataset for Continuous Multi-Tas..:
, In:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
,
Sun, Tao
;
Segu, Mattia
;
Postels, Janis
... - p. 21339-21350 , 2022
Link:
https://doi.org/10.1109/CVPR52688.2022.02068
RT T1 <cTitle>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)</cTitle>: T1 <Title>SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-9880320&Exemplar=1&LAN=DE A1 Sun, Tao A1 Segu, Mattia A1 Postels, Janis A1 Wang, Yuxuan A1 Van Gool, Luc A1 Schiele, Bernt A1 Tombari, Federico A1 Yu, Fisher YR 2022 SN 2575-7075 K1 Adaptation models K1 Uncertainty K1 Rain K1 Annotations K1 System performance K1 Data collection K1 Multitasking K1 Datasets and evaluation; Motion and tracking; Navigation and autonomous driving; Transfer/low-shot/long-tail learning SP 21339 OP 21350 LK http://dx.doi.org/https://doi.org/10.1109/CVPR52688.2022.02068 DO https://doi.org/10.1109/CVPR52688.2022.02068 SF ELIB - SuUB Bremen
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