Sep 15, 2026 · cs.CVJ/K move · Enter open · S save
Xuezhi Xiang, Guanghao Wu, Heqi Xiang, Jiayao Liu+3
Information and Communication Engineering, Harbin Engineering University, Harbin, China · Key Laboratory of Advanced Marine Communication and Information Technology, Harbin, China · Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada · The Department of Computer Science, Kanagawa University, Kanagawa, 221-8686, Japan
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.