We present a novel framework, i.e., Segment Any Anomaly + (SAA+), for zero-shot anomaly segmentation with hybrid prompt regularization to improve the adaptability of modern foundation models. Existing anomaly segmentation models typically rely on domain-specific fine-tuning, limiting their generalization across countless anomaly patterns. In this work, inspired by the great zero-shot generalization ability of foundation models like Segment Anything, we first explore their assembly to leverage diverse multi-modal prior knowledge for anomaly localization. For non-parameter foundation model adaptation to anomaly segmentation, we further introduce hybrid prompts derived from domain expert knowledge and target image context as regularization. Our proposed SAA+ model achieves state-of-the-art performance on several anomaly segmentation benchmarks, including VisA, MVTec-AD, MTD, and KSDD2, in the zero-shot setting. We will release the code at \href{https://github.com/caoyunkang/Segment-Any-Anomaly}{https://github.com/caoyunkang/Segment-Any-Anomaly}.

翻译：我们提出了一种新颖框架——Segment Any Anomaly+（SAA+），用于零样本异常分割，通过混合提示正则化提升现代基础模型的适应性。现有异常分割模型通常依赖领域特定的微调，限制了其对海量异常模式的泛化能力。受Segment Anything等基础模型强大零样本泛化能力的启发，本研究首次探索如何整合这些模型，利用多样化的多模态先验知识实现异常定位。为使非参数化基础模型适应异常分割任务，我们进一步引入基于领域专家知识和目标图像上下文生成的混合提示作为正则化机制。所提出的SAA+模型在VisA、MVTec-AD、MTD和KSDD2等多个异常分割基准测试中，在零样本设定下取得了最先进的性能。我们将公开发布代码于\href{https://github.com/caoyunkang/Segment-Any-Anomaly}{https://github.com/caoyunkang/Segment-Any-Anomaly}。