← 研踪 PaperTrailCVF · CVPR 2026:cvf:440cafeeb4a23164f3a8a550df4dd233 · 记录 #1
Multi-Prototype Compactness and Boundary-Aware Synthesis for Unsupervised Anomaly Detection
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 00:18
摘要
Unsupervised Anomaly Detection (UAD) is crucial for industrial quality control. Many existing embedding-based methods adopt a single-prototype assumption and learn, for example, a compact hypersphere to enclose all normal features. However, this strategy breaks down under intra-class variance caused by changes in illumination, pose, or texture. To cover all diverse normal samples, a single prototype may induce an overly loose decision boundary, making subtle anomalies hard to detect. To overcome this limitation, we propose PGBL (Prototype-Guided Boundary Learning), a framework that synergizes structured representation learning with targeted anomaly synthesis. First, the Multi-Prototype Compact Constraint (MPCC) module models the normal feature distribution as a mixture of multiple semantic prototypes, enabling tighter local representations for each normal sub-pattern. Second, instead of blind anomaly synthesis, the Boundary-Aware Anomaly Synthesis (BAAS) module generates pseudo-anomalies at the topological boundaries between MPCC clusters. Finally, a Discriminative Boundary Refiner (DBR) learns to shape the final decision surface by separating normal clusters from the synthesized anomalies. Extensive experiments on MVTec-AD, VisA and Real-IAD show that PGBL consistently outperforms prior methods in both anomaly detection and localization.
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记录 #1 · 2026 · 最新观测版本
Multi-Prototype Compactness and Boundary-Aware Synthesis for Unsupervised Anomaly Detection
Unsupervised Anomaly Detection (UAD) is crucial for industrial quality control. Many existing embedding-based methods adopt a single-prototype assumption and learn, for example, a compact hypersphere to enclose all normal features. However, this strategy breaks down under intra-class variance caused by changes in illumination, pose, or texture. To cover all diverse normal samples, a single prototype may induce an overly loose decision boundary, making subtle anomalies hard to detect. To overcome this limitation, we propose PGBL (Prototype-Guided Boundary Learning), a framework that synergizes structured representation learning with targeted anomaly synthesis. First, the Multi-Prototype Compact Constraint (MPCC) module models the normal feature distribution as a mixture of multiple semantic prototypes, enabling tighter local representations for each normal sub-pattern. Second, instead of blind anomaly synthesis, the Boundary-Aware Anomaly Synthesis (BAAS) module generates pseudo-anomalies at the topological boundaries between MPCC clusters. Finally, a Discriminative Boundary Refiner (DBR) learns to shape the final decision surface by separating normal clusters from the synthesized anomalies. Extensive experiments on MVTec-AD, VisA and Real-IAD show that PGBL consistently outperforms prior methods in both anomaly detection and localization.
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