Zero-Shot Anomaly Detection with Dual-Branch Prompt Selection


Zihan Wang (McGill University), Samira Ebrahimi Kahou (University of Calgary), Narges Armanfard (McGill University)
The 35th British Machine Vision Conference
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Abstract

Zero‑shot anomaly detection (ZSAD) enables identifying and localizing defects in unseen categories by relying solely on generalizable features rather than requiring any labeled examples of anomalies. However, existing ZSAD methods, whether using fixed or learned prompts, struggle under domain shifts because their training data are derived from limited training domains and fail to generalize to new distributions. In this paper, we introduce PILOT, a framework designed to overcome these challenges through two key innovations: (1) a novel dual-branch prompt learning mechanism that dynamically integrates a pool of learnable prompts with structured semantic attributes, enabling the model to adaptively weight the most relevant anomaly cues for each input image; and (2) a label-free test-time adaptation strategy that updates the learnable prompt parameters using high-confidence pseudo-labels from unlabeled test data. Extensive experiments on 13 industrial and medical benchmarks demonstrate that PILOT achieves state-of-the-art performance in both anomaly detection and localization under domain shift.

Citation

@inproceedings{Wang_2025_BMVC,
author    = {Zihan Wang and Samira Ebrahimi Kahou and Narges Armanfard},
title     = {Zero-Shot Anomaly Detection with Dual-Branch Prompt Selection},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_567/paper.pdf}
}


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