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David Brüggemann
David Brüggemann
Computer Vision Lab, ETH Zürich
Bestätigte E-Mail-Adresse bei vision.ee.ethz.ch
Titel
Zitiert von
Zitiert von
Jahr
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
502020
Automated search for resource-efficient branched multi-task networks
D Bruggemann, M Kanakis, S Georgoulis, L Van Gool
British Machine Vision Conference (BMVC), 2020
362020
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
332021
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
132023
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
4*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
22023
Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation
D Bruggemann, C Sakaridis, T Brödermann, L Van Gool
arXiv preprint arXiv:2303.05194, 2023
12023
Condition-Invariant Semantic Segmentation
C Sakaridis, D Bruggemann, F Yu, L Van Gool
arXiv preprint arXiv:2305.17349, 2023
2023
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