ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shift
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| Format: | Preprint |
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2024
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| _version_ | 1866912131857776640 |
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| author | Kashiani, Hossein Talemi, Niloufar Alipour Afghah, Fatemeh |
| author_facet | Kashiani, Hossein Talemi, Niloufar Alipour Afghah, Fatemeh |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_16049 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shift Kashiani, Hossein Talemi, Niloufar Alipour Afghah, Fatemeh Computer Vision and Pattern Recognition 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. |
| title | ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shift |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.16049 |