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David Brüggemann
David Brüggemann
Swiss Data Science Center
在 datascience.ch 的电子邮件经过验证
标题
引用次数
引用次数
年份
Reparameterizing convolutions for incremental multi-task learning without task interference
M Kanakis, D Bruggemann, S Saha, S Georgoulis, A Obukhov, ...
Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020
712020
Exploring relational context for multi-task dense prediction
D Brüggemann, M Kanakis, A Obukhov, S Georgoulis, L Van Gool
Proceedings of the IEEE/CVF international conference on computer vision …, 2021
612021
Automated search for resource-efficient branched multi-task networks
D Bruggemann, M Kanakis, S Georgoulis, L Van Gool
British Machine Vision Conference (BMVC), 2020
542020
Refign: Align and refine for adaptation of semantic segmentation to adverse conditions
D Brüggemann, C Sakaridis, P Truong, L Van Gool
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023
382023
L2E: Lasers to Events for 6-DoF Extrinsic Calibration of Lidars and Event Cameras
K Ta, D Bruggemann, T Brödermann, C Sakaridis, L Van Gool
2023 IEEE International Conference on Robotics and Automation (ICRA), 11425 …, 2023
7*2023
Composite learning for robust and effective dense predictions
M Kanakis, TE Huang, D Brüggemann, F Yu, L Van Gool
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023
42023
Contrastive model adaptation for cross-condition robustness in semantic segmentation
D Brüggemann, C Sakaridis, T Brödermann, L Van Gool
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023
32023
Condition-invariant semantic segmentation
C Sakaridis, D Bruggemann, F Yu, L Van Gool
arXiv preprint arXiv:2305.17349, 2023
22023
MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty
T Brödermann, D Bruggemann, C Sakaridis, K Ta, O Liagouris, J Corkill, ...
arXiv preprint arXiv:2401.12761, 2024
2024
Variable Tasks and Domains in Dense Prediction
D Brüggemann
ETH Zurich, 2023
2023
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