3 papers
cs.CV2026
Multi-Objective Optimization for Synthetic-to-Real Style Transfer
Estelle Chigot, Thomas Oberlin, Manon Huguenin +1
Semantic segmentation networks require large amounts of pixel-level annotated data, which are costly to obtain for real-world images. Computer graphics engines can generate synthet…
cs.LG2025
Synthetic Data for Robust Runway Detection
Estelle Chigot, Dennis G. Wilson, Meriem Ghrib +2
Deep vision models are now mature enough to be integrated in industrial and possibly critical applications such as autonomous navigation. Yet, data collection and labeling to train…
cs.CV2025
Style Transfer with Diffusion Models for Synthetic-to-Real Domain Adaptation
Estelle Chigot, Dennis G. Wilson, Meriem Ghrib +1
Semantic segmentation models trained on synthetic data often perform poorly on real-world images due to domain gaps, particularly in adverse conditions where labeled data is scarce…