4 papers
What Remains Normal? Clean Images Miss Useful Near-Defect Normal Patches for Anomaly Detection
Joongwon Chae, Runming Wang, Peiwu Qin
Memory-based anomaly detectors store nominal training patches and score test patches against this memory. A patch selected for coverage therefore becomes a nor- mal reference witho…
What Memory Composition Does Not Tell Us About Anomaly Detection
Joongwon Chae, Runming Wang, Peiwu Qin
Memory-based anomaly detectors store nominal training patches and score test patches against this memory. A patch selected for coverage therefore becomes a nor- mal reference witho…
ProCon: Projection-Consistency Memory for Training-Free Anomaly Detection
Joongwon Chae, Lihui Luo, Yang Liu +4
Memory-based anomaly detection is attractive because it localizes defects from normal images without training a decoder or synthesizing pseudo anomalies. However, most memory metho…
StructCore: Structure-Aware Image-Level Scoring for Training-Free Unsupervised Anomaly Detection
Joongwon Chae, Lihui Luo, Yang Liu +8
Max pooling is the de facto standard for converting anomaly score maps into image-level decisions in memory-bank-based unsupervised anomaly detection (UAD). However, because it rel…