2 papers
cs.CV2026
Global Logic and Local Search: Dual-Stream Multimodal In-Context Learning for Verifiable Industrial Anomaly Detection
Runzhi Deng, Yundi Hu, Yiming Zhong +5
Large Multimodal Models (LMMs) show strong few-shot generalization, but industrial anomaly detection remains difficult because defects are small, input resolution is limited, and t…
cs.CV2026
ABounD: Adversarial Boundary-Driven Few-Shot Learning for Multi-Class Anomaly Detection
Runzhi Deng, Yundi Hu, Xinshuang Zhang +5
Few-shot multi-class industrial anomaly detection identifies diverse defects across multiple categories using a single unified model and limited normal samples. Although vision-lan…