提出鲁棒提示驱动框架，解决多类异常检测在域偏移下的性能下降问题。
ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shift
- 通过分层类感知提示融合，动态编码类别信息以减少类间干扰。
- 在MVTec-AD和VISA上优于现有方法，尤其在分布外场景提升显著。
- 适合需要跨域稳定检测的工业异常识别场景。
近年来，多类统一异常检测（MUAD）因其可扩展性和实用性，逐渐取代传统的单类单模型方法。然而，现有MUAD方法常受类间干扰影响，且对域偏移敏感，导致实际应用中性能大幅下降。本文提出一种新型鲁棒提示驱动的MUAD框架——ROADS，通过分层类感知提示集成机制，动态将类别特定信息注入异常检测器，有效缓解类间干扰。同时，引入域适配器，学习域不变表示以增强对域偏移的鲁棒性。在MVTec-AD和VISA数据集上的大量实验表明，ROADS在异常检测与定位任务上均超越现有先进方法，尤其在分布外设置下表现突出。
原文摘要 · Abstract (English)
Recent advancements in anomaly detection have shifted focus towards Multi-class Unified Anomaly Detection (MUAD), offering more scalable and practical alternatives compared to traditional one-class-one-model approaches. However, existing MUAD methods often suffer from inter-class interference and are highly susceptible to domain shifts, leading to substantial performance degradation in real-world applications. In this paper, we propose a novel robust prompt-driven MUAD framework, called ROADS, to address these challenges. ROADS employs a hierarchical class-aware prompt integration mechanism that dynamically encodes class-specific information into our anomaly detector to mitigate interference among anomaly classes. Additionally, ROADS incorporates a domain adapter to enhance robustness against domain shifts by learning domain-invariant representations. Extensive experiments on MVTec-AD and VISA datasets demonstrate that ROADS surpasses state-of-the-art methods in both anomaly detection and localization, with notable improvements in out-of-distribution settings.
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