mlsanityData to 7 Oct 2026

ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shift

arXiv 2411.16049 ↗First posted 25 Nov 2024cs.CVcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 33 of its 42 references that are in the map. First posted on arXiv 25 Nov 2024.

Builds on

33 of 42 references.

References · 33

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21 more
  1. 2022A Unified Model for Multi-class Anomaly Detectioncited by 267
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  3. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025Robust Distribution Alignment for Industrial Anomaly Detection under Distribution Shiftcited by 5
  2. 2026Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systemscited by 3
  3. 2025MoViAD: A Modular Library for Visual Anomaly Detectioncited by 0
  4. 2026PromptMAD: Cross-Modal Prompting for Multi-Class Visual Anomaly Localizationcited by 0
  5. 2026See More, Detect Less? Taming Information Leakage in Multi-View Anomaly Detectioncited by 0