3 citations · 4 across the 6 of their papers we have counts for
6 papers · 1 filter
Learning to Reason: Targeted Knowledge Discovery and Fuzzy Logic Update for Robust Image Recognition
Gurucharan Srinivas, Joshua Niemeijer, Frank Köster
Integrating domain knowledge into deep neural networks is a promising way to improve generalization. Existing methods either encode prior knowledge in the loss function or apply po…
GOLD-BEV: GrOund and aeriaL Data for Dense Semantic BEV Mapping of Dynamic Scenes
Joshua Niemeijer, Alaa Eddine Ben Zekri, Reza Bahmanyar +3
Understanding road scenes in a geometrically consistent, scene-centric representation is crucial for planning and mapping. We present GOLD-BEV, a framework that learns dense bird's…
Test-Time Modification: Inverse Domain Transformation for Robust Perception
Arpit Jadon, Joshua Niemeijer, Yuki M. Asano
Generative foundation models contain broad visual knowledge and can produce diverse image variations, making them particularly promising for advancing domain generalization tasks.…
Generalization by Adaptation: Diffusion-Based Domain Extension for Domain-Generalized Semantic Segmentation
Joshua Niemeijer, Manuel Schwonberg, Jan-Aike Termöhlen +2
When models, e.g., for semantic segmentation, are applied to images that are vastly different from training data, the performance will drop significantly. Domain adaptation methods…
Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving
Manuel Schwonberg, Joshua Niemeijer, Jan-Aike Termöhlen +4
Deep neural networks (DNNs) have proven their capabilities in many areas in the past years, such as robotics, or automated driving, enabling technological breakthroughs. DNNs play…
Best Practices in Active Learning for Semantic Segmentation
Sudhanshu Mittal, Joshua Niemeijer, Jörg P. Schäfer +1
Active learning is particularly of interest for semantic segmentation, where annotations are costly. Previous academic studies focused on datasets that are already very diverse and…