LACToS: Local Anomaly Detection via Conditional Typicality of Superpixels
Local anomaly detection is the task of identifying spatially confined anomalous or out-of-distribution (OOD) regions within an otherwise in-distribution (ID) image. Most existing methods are designed for a specific context, with a strong focus on industrial defect detection, where they achieve excellent performance on well-established benchmarks. However, we observe that these methods can exhibit significant performance drops in other contexts, especially when the inlier distribution is multimodal. To account for this inlier complexity, we propose LACToS, a principled local OOD detection method based on statistical typicality and made tractable through two innovations: (i) grouping pixels...
| Authors | Jihoon Hong, Alireza Kheirandish, Rohan Sanda et al. |
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| Primary category | cs.CV |
| Published | 22 October 2026 |
| Last updated at source | 22 October 2026 |
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