Julian Gleißner

Preserve the Hard, Regenerate the Rest: Uncertainty-Guided Synthetic Training Data Augmentation with Diffusion Models

Nikolai Röhrich, Julian Gleißner, Ahmed H. A. Ibrahim, Silvan Mertes, Tobias Huber

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 synthetic augmentation is an appealing solution, directly generating new labeled data risks misalignmen…Read more