Beyond Normal References: Discriminative Few-Shot Anomaly Detection 事件

Beyond Normal References: Discriminative Few-Shot Anomaly Detection arXiv:2605.23231v2 Announce Type: replace Abstract: This paper considers a practical few-shot anomaly detection (FSAD) setting, termed discriminative FSAD, where a limited number of both normal and anomalous examples are available as references during inference. Existing FSAD methods rely on normal-only references through normality matching, ignoring the discriminative clues in anomalous references, while directly fitting both