8 citations · 23 across the 27 of their papers we have counts for
6 papers · 1 filter
Diffuse2Seg: Diffusion Models Can Segment Anything Without Supervision
Christoph Hümmer, Joachim Sicking, Fabian Hüger +1
Open-world entity segmentation aims to predict masks for arbitrary objects across domains and at multiple granularities, from parts to whole objects. In this setting, SAM sets a st…
RoG-DAgger: Rollout-Guided Post-Training for End-to-End Driving
Liangyu Zhong, Joachim Sicking, Fabian Hueger +1
Recent end-to-end driving systems demonstrate strong performance on closed-loop benchmarks, yet are still predominantly trained on fixed expert-collected data using open-loop imita…
Extended Field of View Analysis for VideoGAN-based Trajectory Generation
Annajoyce Mariani, Kira Maag, Hanno Gottschalk
Realistic and diverse trajectory generation is central to enabling higher levels of vehicle automation. While rule-based and classical learning-based methods may struggle to captur…
Generative Design of a Gas Turbine Combustor Using Invertible Neural Networks
Patrick Krüger, Hanno Gottschalk, Werner Krebs +1
The need to burn 100% H2 in high efficient gas turbines featuring low NOx combustion in premix mode require the complete redesign of the combustion system to ensure stable operatio…
How well do generative models solve inverse problems? A benchmark study
Patrick Krüger, Patrick Materne, Werner Krebs +1
Generative learning generates high dimensional data based on low dimensional conditions, also called prompts. Therefore, generative learning algorithms are eligible for solving (Ba…
Generative Design of Ship Propellers using Conditional Flow Matching
Patrick Kruger, Rafael Diaz, Simon Hauschulz +2
In this paper, we explore the use of generative artificial intelligence (GenAI) for ship propeller design. While traditional forward machine learning models predict the performance…