arXiv 2502.012012025-02-03cs.CV

One-to-Normal：用于少样本异常检测的异常个性化

One-to-Normal: Anomaly Personalization for Few-shot Anomaly Detection

West China Hospital, Sichuan University（四川大学华西医院）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；Sichuan University Pittsburgh Institute, Sichuan University（四川大学匹兹堡学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）

Yiyue Li, Shaoting Zhang, Kang Li, Qicheng Lao

备注：: In The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS2024)

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AI总结：

针对少样本异常检测中直接特征比较导致精度损失的问题，提出异常个性化方法，利用无异常定制生成模型对查询图像进行One-to-Normal转换，并结合三元组对比异常推理策略，在11个数据集上显著提升了准确率且能灵活迁移至其他方法。

摘要：

传统异常检测（AD）方法主要依赖于从大量正常数据中进行无监督学习。随着大型预训练视觉-语言模型的出现，近期的AD方法有所发展，增强了少样本异常检测能力。然而，这些最新的AD方法在准确率提升方面仍存在局限性。一个促成因素是它们将查询图像的特征与少样本正常图像的特征进行直接比较。这种直接比较通常会导致精度损失，并使这些技术向更复杂领域的扩展变得复杂——这一领域目前仍缺乏以更精细和全面的方式进行深入探索。为了解决这些局限性，我们引入了异常个性化方法，该方法使用无异常的定制生成模型对查询图像执行个性化的One-to-Normal转换，确保与正常流形紧密对齐。此外，为了进一步增强预测结果的稳定性和鲁棒性，我们提出了一种三元组对比异常推理策略，该策略结合了查询图像与生成的无异常数据池及提示信息之间的全面比较。在三个领域的11个数据集上的广泛评估表明，与最新的AD方法相比，我们的模型具有有效性。此外，我们的方法已被证明能够灵活迁移到其他AD方法中，其生成的图像数据能有效提升其他AD方法的性能。

英文摘要：

Traditional Anomaly Detection (AD) methods have predominantly relied on unsupervised learning from extensive normal data. Recent AD methods have evolved with the advent of large pre-trained vision-language models, enhancing few-shot anomaly detection capabilities. However, these latest AD methods still exhibit limitations in accuracy improvement. One contributing factor is their direct comparison of a query image's features with those of few-shot normal images. This direct comparison often leads to a loss of precision and complicates the extension of these techniques to more complex domains--an area that remains underexplored in a more refined and comprehensive manner. To address these limitations, we introduce the anomaly personalization method, which performs a personalized one-to-normal transformation of query images using an anomaly-free customized generation model, ensuring close alignment with the normal manifold. Moreover, to further enhance the stability and robustness of prediction results, we propose a triplet contrastive anomaly inference strategy, which incorporates a comprehensive comparison between the query and generated anomaly-free data pool and prompt information. Extensive evaluations across eleven datasets in three domains demonstrate our model's effectiveness compared to the latest AD methods. Additionally, our method has been proven to transfer flexibly to other AD methods, with the generated image data effectively improving the performance of other AD methods.

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