UniDG: Unified Defect Generation via Defect-Context Editing
arXiv:2604.08915v2 Announce Type: replace-cross Abstract: Generating realistic visual defects is crucial for alleviating the scarcity of abnormal samples in anomaly detection, yet existing methods often rely on category-specific few-shot adaptation and struggle to generalize across objects, defect types, and domains. This limitation stems in part from the lack of paired defect editing data and the fine-grained nature of defect appearance, where subtle variations in scale, texture, and morpholog…