PSMP-CLIP: Patch-Prompt SAM and Multi-Semantic Prompting for CLIP-Based Zero-Shot Anomaly Detection

2026-09-15

AI Summary

为解决零样本异常检测中粗略异常图和有限语义提示问题，提出PSMP-CLIP方法，结合基于补丁的SAM2分割与多语义引导提示正则化。

Abstract

Zero-shot anomaly detection aims to localize anomalies without target-domain samples. Existing CLIP-based methods suffer from coarse anomaly maps and limited semantic prompts. We propose PSMP-CLIP, integrating patch-prompt SAM2 segmentation (PPSS) and multi-semantic guided prompt regularization (MSGPR). PPSS samples prompts directly from intermediate patch features, avoiding threshold drift and guiding SAM2 to produce precise masks. MSGPR uses multiple learnable prompts constrained by semantic anchors to preserve generalization. Experiments on 14 datasets show highly competitive performance, achieving the best pixel-level AUROC on MVTec AD, BTAD, DTD-Synthetic, CVC-ClinicDB, TN3K, Endo, and Kvasir.

Problem

• zero-shot anomaly detection
• coarse anomaly maps
• limited semantic prompts

Innovation

• Patch-Prompt SAM2 Segmentation
• Multi-Semantic Guided Prompt Regularization
• Zero-Shot Anomaly Detection