Segment Any Anomaly without Training via Hybrid Prompt Regularization
Yunkang Cao Xiaohao Xu Chen Sun Yuqi Cheng Zongwei Du Liang Gao Weiming Shen
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}.