ABounD: Adversarial Boundary-Driven Few-Shot Learning for Multi-Class Anomaly Detection

ArXi:2511.22436v2 Announce Type: replace Few-shot multi-class industrial anomaly detection identifies diverse defects across multiple categories using a single unified model and limited normal samples. Although vision-language models offer strong generalization, modeling multiple distinct category manifolds concurrently without actual anomalous data causes feature space collapse and cross-class interference. Consequently, existing methods often fail to balance scalability and precision, leading to either isolated single-class re.