7 citations · 7 across the 5 of their papers we have counts for
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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…
cs.CV2024
AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios
Ziming Huang, Xurui Li, Haotian Liu +3
Recently, multi-class anomaly classification has garnered increasing attention. Previous methods directly cluster anomalies but often struggle due to the lack of anomaly-prior know…
cs.CV2024★ 7 cited
MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images
Xurui Li, Ziming Huang, Feng Xue +1
This paper studies zero-shot anomaly classification (AC) and segmentation (AS) in industrial vision. We reveal that the abundant normal and abnormal cues implicit in unlabeled test…