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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

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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.

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.