no code implementations • 20 Mar 2024 • Xincheng Yao, Ruoqi Li, Zefeng Qian, Lu Wang, Chongyang Zhang
In this paper, we propose a novel Hierarchical Gaussian mixture normalizing flow modeling method for accomplishing unified Anomaly Detection, which we call HGAD.
1 code implementation • ICCV 2023 • Xincheng Yao, Ruoqi Li, Zefeng Qian, Yan Luo, Chongyang Zhang
Humans recognize anomalies through two aspects: larger patch-wise representation discrepancies and weaker patch-to-normal-patch correlations.
1 code implementation • CVPR 2023 • Xincheng Yao, Ruoqi Li, Jing Zhang, Jun Sun, Chongyang Zhang
In this way, our model can form a more explicit and discriminative decision boundary to distinguish known and also unseen anomalies from normal samples more effectively.
Ranked #3 on Supervised Anomaly Detection on MVTec AD (using extra training data)