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LACToS: Local Anomaly Detection via Conditional Typicality of Superpixels

Paper recorded by Signals 4 on 2026-10-22 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-22 on arXiv · recorded by Signals 4 on 2026-10-23

Category: cs.CV · 计算机视觉 · first seen 2026-10-23

Abstract

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

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#184 most recent of 756 cs.CV papers we have recorded · ↑ newer: Squaring-the-Sphere: Distortion-Free and Seamless Panorama Generation · ↓ older: Diffusion-Based Synthesis of Complete 3D Biparametric Prostate MRI
Cite this page: LACToS: Local Anomaly Detection via Conditional Typicality of Superpixels: the #184 most recent of 756 cs.CV papers we have recorded (as of 2026-10-22). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/lactos-local-anomaly-detection-via-conditional-typicality-of-superpixels.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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