3 papers
cs.CV2026
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…
cs.CV2026
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…
cs.CV2026
GCR: Geometry-Consistent Routing for Task-Agnostic Continual Anomaly Detection
Joongwon Chae, Lihui Luo, Yang Liu +8
Feature-based anomaly detection is widely adopted in industrial inspection due to the strong representational power of large pre-trained vision encoders. While most existing method…