2 papers
cs.CV2026
Preserve the Hard, Regenerate the Rest: Uncertainty-Guided Synthetic Training Data Augmentation with Diffusion Models
Nikolai Röhrich, Julian Gleißner, Ahmed H. A. Ibrahim +2
Semantic segmentation models struggle with data sparsity and rare or visually diverse regions, e.g., dense regions or small objects in aerial or autonomous mobility data. While syn…
cs.RO2026
SUNSET - A Sensor-fUsioN based semantic SegmEnTation exemplar for ROS-based self-adaptation
Andreas Wiedholz, Rafael Paintner, Alwin Hoffmann +2
The fact that robots are getting deployed more often in dynamic environments, together with the increasing complexity of their software systems, raises the need for self-adaptive a…