Anomaly detection (AD) is essential for industrial inspection, yet existing methods typically rely on ``comparing'' test images to normal references from a training set. However, variations in appearance and positioning often complicate the alignment of these references with the test image, limiting detection accuracy. We observe that most anomalies manifest as local variations, meaning that even within anomalous images, valuable normal information remains. We argue that this information is useful and may be more aligned with the anomalies since both the anomalies and the normal information originate from the same image. Therefore, rather than relying on external normality from the training set, we propose INP-Former, a novel method that extracts Intrinsic Normal Prototypes (INPs) directly from the test image. Specifically, we introduce the INP Extractor, which linearly combines normal tokens to represent INPs. We further propose an INP Coherence Loss to ensure INPs can faithfully represent normality for the testing image. These INPs then guide the INP-Guided Decoder to reconstruct only normal tokens, with reconstruction errors serving as anomaly scores. Additionally, we propose a Soft Mining Loss to prioritize hard-to-optimize samples during training. INP-Former achieves state-of-the-art performance in single-class, multi-class, and few-shot AD tasks across MVTec-AD, VisA, and Real-IAD, positioning it as a versatile and universal solution for AD. Remarkably, INP-Former also demonstrates some zero-shot AD capability. Code is available at:https://github.com/luow23/INP-Former.

翻译：异常检测（AD）在工业检测中至关重要，然而现有方法通常依赖于将测试图像与训练集中的正常参考图像进行“比较”。然而，外观和位置的变化常常使这些参考图像难以与测试图像对齐，从而限制了检测精度。我们观察到，大多数异常表现为局部变化，这意味着即使在异常图像中，仍然存在有价值的正常信息。我们认为这些信息是有用的，并且可能更易于与异常对齐，因为异常和正常信息都源自同一幅图像。因此，我们不再依赖来自训练集的外部正常性，而是提出了INP-Former，一种直接从测试图像中提取内在正常原型（INPs）的新方法。具体而言，我们引入了INP提取器，它通过线性组合正常标记来表示INPs。我们进一步提出了INP一致性损失，以确保INPs能够忠实地表示测试图像的正常性。这些INPs随后指导INP引导解码器仅重构正常标记，重构误差则作为异常分数。此外，我们提出了软挖掘损失，以在训练过程中优先处理难以优化的样本。INP-Former在MVTec-AD、VisA和Real-IAD数据集上的单类、多类和少样本AD任务中均取得了最先进的性能，使其成为AD领域一个通用且多功能的解决方案。值得注意的是，INP-Former还展示出一定的零样本AD能力。代码发布于：https://github.com/luow23/INP-Former。