Wan-Xiang Zhang

SAGAD: A Graph Foundation Model for Generalist Few-Shot Anomaly Detection via Fine-Grained Disentanglement.

This work proposes structural-anomaly perception and attribute-anomaly perception GAD (SAGAD), a novel GAD-oriented GFM tailored for fine-grained anomaly perception and achieves highly competitive performance compared to state-of-the-art methods under the GAD-oriented GFMs paradigm in few-shot scenarios.