R&D: Balancing Reliability and Diversity in Synthetic Data Augmentation for Semantic Segmentation

ArXi:2603.18427v1 Announce Type: cross Collecting and annotating datasets for pixel-level semantic segmentation tasks are highly labor-intensive. Data augmentation provides a viable solution by enhancing model generalization without additional real-world data collection. Traditional augmentation techniques, such as translation, scaling, and color transformations, create geometric variations but fail to generate new structures.