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Adapting Domain-Aware Knowledge to Vision-Language Model for Zero-Shot Anomaly Detection.

Zeqi Ma, Xiaozhao Fang, Yue Huang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 12, 2026
    PubMed
    Summary

    Domain Adaptation CLIP (DA-CLIP) enhances zero-shot anomaly detection by adapting domain knowledge to vision-language models. This approach improves generalization for detecting diverse anomalies across unseen domains.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Zero-shot anomaly detection (ZSAD) faces challenges due to anomaly rarity, diversity, and domain-specific manifestations.
    • Vision-language models (VLMs) show promise for ZSAD but struggle with domain adaptation due to limited domain-specific knowledge.

    Purpose of the Study:

    • To propose Domain Adaptation CLIP (DA-CLIP), a novel approach for enhancing ZSAD by adapting domain-aware knowledge to VLMs.
    • To improve the generalization capabilities of VLMs for detecting anomalies in unseen domains.

    Main Methods:

    • DA-CLIP employs a Domain-Aware Knowledge Adaptation (DAKA) strategy with specialized experts for target domains.
    • Learnable domain-aware prompts are injected into both CLIP encoders and DAKA modules for dual-pathway learning.

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  • This enables dynamic selection and combination of experts tailored to anomaly characteristics and domain-specific features.

    • Main Results:

    • DA-CLIP consistently outperforms state-of-the-art methods on benchmark datasets across industrial and medical domains.
    • Significant improvements were observed in both image-level and pixel-level anomaly detection tasks.
    • The dual-pathway learning effectively captures domain-specific features for better adaptation.

    Conclusions:

    • DA-CLIP offers a robust solution for domain adaptation in zero-shot anomaly detection.
    • The proposed DAKA strategy and domain-aware prompts enhance VLM performance on diverse anomaly detection tasks.
    • This approach advances the field of ZSAD by improving generalization across different domains.