Benchmarking Semantic Segmentation Models via Appearance and Geometry Attribute Editing

ArXi:2603.01535v2 Announce Type: replace Semantic segmentation takes pivotal roles in various applications such as autonomous driving and medical image analysis. When deploying segmentation models in practice, it is critical to test their behaviors in varied and complex scenes in advance. In this paper, we construct an automatic data generation pipeline Gen4Seg to stress-test semantic segmentation models by generating various challenging samples with different attribute changes.