4 papers
Adaptive Dual-Teacher Distillation with Subnetwork Rectification for Bridging Semantic Gaps in Black-Box Domain Adaptation
Zhe Zhang, Jing Li, Wanli Xue +4
Assuming that neither source data nor source model parameters are accessible, black-box domain adaptation (BBDA) represents a highly practical yet challenging setting, where transf…
RAID: Retrieval-Augmented Anomaly Detection
Mingxiu Cai, Zhe Zhang, Gaochang Wu +2
Unsupervised Anomaly Detection (UAD) aims to identify abnormal regions by establishing correspondences between test images and normal templates. Existing methods primarily rely on…
Unified Unsupervised Anomaly Detection via Matching Cost Filtering
Zhe Zhang, Mingxiu Cai, Gaochang Wu +5
Unsupervised anomaly detection (UAD) aims to identify image- and pixel-level anomalies using only normal training data, with wide applications such as industrial inspection and med…
CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering
Zhe Zhang, Mingxiu Cai, Hanxiao Wang +3
Unsupervised anomaly detection (UAD) seeks to localize the anomaly mask of an input image with respect to normal samples. Either by reconstructing normal counterparts (reconstructi…