Learning Generalizable Semantic Segmentation from Simulation to Real-World Domains with Multi-Resolution Feature Perturbation
A novel Multi-Resolution Feature Perturbation (MRFP) technique is proposed to enhance the generalizability of deep semantic segmentation models trained on synthetic data to unseen real-world domains.