no code implementations • 4 Dec 2023 • Christoph Hümmer, Manuel Schwonberg, Liangwei Zhou, Hu Cao, Alois Knoll, Hanno Gottschalk
We thus propose a new vision-language approach for domain generalized segmentation, which improves the domain generalization SOTA by 7. 6% mIoU when training on the synthetic GTA5 dataset.
Ranked #1 on Semantic Segmentation on Cityscapes test (using extra training data)
1 code implementation • 4 Dec 2023 • Joshua Niemeijer, Manuel Schwonberg, Jan-Aike Termöhlen, Nico M. Schmidt, Tim Fingscheidt
In a second step, we train a generalizing model by adapting towards this pseudo-target domain.
no code implementations • 24 Apr 2023 • Manuel Schwonberg, Fadoua El Bouazati, Nico M. Schmidt, Hanno Gottschalk
Unsupervised Domain Adaptation (UDA) and domain generalization (DG) are two research areas that aim to tackle the lack of generalization of Deep Neural Networks (DNNs) towards unseen domains.
no code implementations • 24 Apr 2023 • Manuel Schwonberg, Joshua Niemeijer, Jan-Aike Termöhlen, Jörg P. Schäfer, Nico M. Schmidt, Hanno Gottschalk, Tim Fingscheidt
DNNs play a significant role in environment perception for the challenging application of automated driving and are employed for tasks such as detection, semantic segmentation, and sensor fusion.