CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911585574846464 |
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| author | Chen, Qiyu Qu, Zhen Luo, Wei Yao, Haiming Cao, Yunkang Jiang, Yuxin Duan, Yinan Luo, Huiyuan Lv, Chengkan Zhang, Zhengtao |
| author_facet | Chen, Qiyu Qu, Zhen Luo, Wei Yao, Haiming Cao, Yunkang Jiang, Yuxin Duan, Yinan Luo, Huiyuan Lv, Chengkan Zhang, Zhengtao |
| contents | Recently, large pre-trained vision-language models have shown remarkable performance in zero-shot anomaly detection (ZSAD). With fine-tuning on a single auxiliary dataset, the model enables cross-category anomaly detection on diverse datasets covering industrial defects and medical lesions. Compared to manually designed prompts, prompt learning eliminates the need for expert knowledge and trial-and-error. However, it still faces the following challenges: (i) static learnable tokens struggle to capture the continuous and diverse patterns of normal and anomalous states, limiting generalization to unseen categories; (ii) fixed textual labels provide overly sparse category information, making the model prone to overfitting to a specific semantic subspace. To address these issues, we propose Conditional Prompt Synthesis (CoPS), a novel framework that synthesizes dynamic prompts conditioned on visual features to enhance ZSAD performance. Specifically, we extract representative normal and anomaly prototypes from fine-grained patch features and explicitly inject them into prompts, enabling adaptive state modeling. Given the sparsity of class labels, we leverage a variational autoencoder to model semantic image features and implicitly fuse varied class tokens into prompts. Additionally, integrated with our spatially-aware alignment mechanism, extensive experiments demonstrate that CoPS surpasses state-of-the-art methods by 1.4% in classification AUROC and 1.9% in segmentation AUROC across 13 industrial and medical datasets. The code is available at https://github.com/cqylunlun/CoPS. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_03447 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection Chen, Qiyu Qu, Zhen Luo, Wei Yao, Haiming Cao, Yunkang Jiang, Yuxin Duan, Yinan Luo, Huiyuan Lv, Chengkan Zhang, Zhengtao Computer Vision and Pattern Recognition Recently, large pre-trained vision-language models have shown remarkable performance in zero-shot anomaly detection (ZSAD). With fine-tuning on a single auxiliary dataset, the model enables cross-category anomaly detection on diverse datasets covering industrial defects and medical lesions. Compared to manually designed prompts, prompt learning eliminates the need for expert knowledge and trial-and-error. However, it still faces the following challenges: (i) static learnable tokens struggle to capture the continuous and diverse patterns of normal and anomalous states, limiting generalization to unseen categories; (ii) fixed textual labels provide overly sparse category information, making the model prone to overfitting to a specific semantic subspace. To address these issues, we propose Conditional Prompt Synthesis (CoPS), a novel framework that synthesizes dynamic prompts conditioned on visual features to enhance ZSAD performance. Specifically, we extract representative normal and anomaly prototypes from fine-grained patch features and explicitly inject them into prompts, enabling adaptive state modeling. Given the sparsity of class labels, we leverage a variational autoencoder to model semantic image features and implicitly fuse varied class tokens into prompts. Additionally, integrated with our spatially-aware alignment mechanism, extensive experiments demonstrate that CoPS surpasses state-of-the-art methods by 1.4% in classification AUROC and 1.9% in segmentation AUROC across 13 industrial and medical datasets. The code is available at https://github.com/cqylunlun/CoPS. |
| title | CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.03447 |