Beyond Normal References: Discriminative Few-Shot Anomaly Detection
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 references can overfit to the seen anomalies. We introduce IDEAL, an intrinsic deviation learning framework that leverages both reference types to learn intrinsic deviation patterns characterizing generalizable abnormality as deviations from normality. IDEAL decomposes the learning process into two novel components: ...
| Authors | Huan Wang, Jun Shen, Jun Yan et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 May 2026 |
| Last updated at source | 30 September 2026 |
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