AI 中文总结
OSAGEN结合感知对象掩码先验与多阶段解耦扩散，通过QBG、ISC及轻量物质化步骤生成工业异常，在MVTec AD和VisA上实现优异的异常定位性能。
AI 中文摘要
工业异常检测与定位受限于真实异常及像素级标注的稀缺性，而合成图像-掩码对可缓解该瓶颈。现有少样本掩码引导生成可能过度遵循掩码几何结构、生成的异常效果弱，或使用与当前对象实例不兼容的条件掩码。本文提出OSAGEN，它结合感知对象掩码先验与多阶段解耦扩散，其三阶段适配依次学习正常外观、粗条件下的缺陷外观，以及细粒度掩码校准，提升缺陷实现与局部控制能力。QBG从匹配的正常图像中注入对象结构到掩码扩散，以生成感知对象的先验，而ISC在采样期间限制异常传播并保留正常内容。一个轻量的物质化步骤可恢复与已实现缺陷对齐的像素级标签。在MVTec AD和VisA数据集上，OSAGEN在统一的下游定位协议下，分别达到88.1/82.2和68.5/66.1的AP-P/F1-P分数，代码将在录用后发布。
英文摘要
Industrial anomaly detection and localization are limited by scarce real anomalies and pixel-level annotations, a bottleneck that synthetic image-mask pairs can alleviate. However, existing few-shot mask-guided generation may over-follow mask geometry, produce weak anomalies, or use condition masks incompatible with the current object instance. We propose OSAGEN, which combines object-aware mask priors with multistage decoupled diffusion. Its three-stage adaptation sequentially learns normal appearance, defect appearance under coarse conditions, and fine-grained mask calibration, improving defect realization and local control. QBG injects object structure from a matched normal image into mask diffusion to produce object-aware priors, while ISC restricts anomaly propagation and preserves normal content during sampling. A lightweight materialization step recovers pixel-level labels aligned with the realized defects. On MVTec AD and VisA, OSAGEN achieves AP-P/F1-P scores of 88.1/82.2 and 68.5/66.1, respectively, under a unified downstream localization protocol. The code will be released upon acceptance.
