no code implementations • 23 Jan 2024 • Tim Brödermann, David Bruggemann, Christos Sakaridis, Kevin Ta, Odysseas Liagouris, Jason Corkill, Luc van Gool
Achieving level-5 driving automation in autonomous vehicles necessitates a robust semantic visual perception system capable of parsing data from different sensors across diverse conditions.
1 code implementation • 27 May 2023 • Christos Sakaridis, David Bruggemann, Fisher Yu, Luc van Gool
Motivated by these findings, we propose to leverage stylization in performing feature-level adaptation by aligning the internal network features extracted by the encoder of the network from the original and the stylized view of each input image with a novel feature invariance loss.
1 code implementation • ICCV 2023 • David Bruggemann, Christos Sakaridis, Tim Brödermann, Luc van Gool
We investigate normal-to-adverse condition model adaptation for semantic segmentation, whereby image-level correspondences are available in the target domain.
Ranked #1 on Source-Free Domain Adaptation on Cityscapes to ACDC
no code implementations • 13 Oct 2022 • Menelaos Kanakis, Thomas E. Huang, David Bruggemann, Fisher Yu, Luc van Gool
In this paper, we find that jointly training a dense prediction (target) task with a self-supervised (auxiliary) task can consistently improve the performance of the target task, while eliminating the need for labeling auxiliary tasks.
Ranked #102 on Semantic Segmentation on NYU Depth v2
1 code implementation • 14 Jul 2022 • David Bruggemann, Christos Sakaridis, Prune Truong, Luc van Gool
Due to the scarcity of dense pixel-level semantic annotations for images recorded in adverse visual conditions, there has been a keen interest in unsupervised domain adaptation (UDA) for the semantic segmentation of such images.
Ranked #1 on Semantic Segmentation on Dark Zurich
1 code implementation • 3 Jul 2022 • Kevin Ta, David Bruggemann, Tim Brödermann, Christos Sakaridis, Luc van Gool
As neuromorphic technology is maturing, its application to robotics and autonomous vehicle systems has become an area of active research.
1 code implementation • ICCV 2021 • David Bruggemann, Menelaos Kanakis, Anton Obukhov, Stamatios Georgoulis, Luc van Gool
Our goal is to find the most efficient way to refine each task prediction by capturing cross-task contexts dependent on tasks' relations.
Ranked #78 on Semantic Segmentation on NYU Depth v2
2 code implementations • 24 Aug 2020 • David Bruggemann, Menelaos Kanakis, Stamatios Georgoulis, Luc van Gool
The multi-modal nature of many vision problems calls for neural network architectures that can perform multiple tasks concurrently.
1 code implementation • ECCV 2020 • Menelaos Kanakis, David Bruggemann, Suman Saha, Stamatios Georgoulis, Anton Obukhov, Luc van Gool
First, enabling the model to be inherently incremental, continuously incorporating information from new tasks without forgetting the previously learned ones (incremental learning).