2 papers
cs.CV2026
Large-Scale Universal Defect Generation: Foundation Models and Datasets
Yuanting Fan, Jun Liu, Bin-Bin Gao +5
Existing defect/anomaly generation methods often rely on few-shot learning, which overfits to specific defect categories due to the lack of large-scale paired defect editing data.…
cs.CV2024
SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuning
Zhewei Dai, Shilei Zeng, Haotian Liu +3
We introduce SeaS, a unified industrial generative model for automatically creating diverse anomalies, authentic normal products, and precise anomaly masks. While extensive researc…