AI RESEARCH
SAGE: Style-Adaptive Generalization for Privacy-Constrained Semantic Segmentation Across Domains
arXiv CS.CV
•
ArXi:2512.02369v2 Announce Type: replace Domain generalization for semantic segmentation aims to mitigate the degradation in model performance caused by domain shifts. However, in many real-world scenarios, we are unable to access the model parameters and architectural details due to privacy concerns and security constraints. Traditional fine-tuning or adaptation is hindered, leading to the demand for input-level strategies that can enhance generalization without modifying model weights.