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cs.CV2026
DifferAD-R1: A Difference-Guided IndustrialAnomaly Localization with Multimodal LargeLanguage Models
Dingrong Wang, Xian Tao, Zhen Qu +5
Industrial anomaly localization aims to accurately identify and localize abnormal regions in industrial products, addressing the critical challenge of detecting unseen defect categ…
cs.CV2026
AG-VAS: Anchor-Guided Zero-Shot Visual Anomaly Segmentation with Large Multimodal Models
Zhen Qu, Xian Tao, Xiaoyi Bao +4
Large multimodal models (LMMs) exhibit strong task generalization capabilities, offering new opportunities for zero-shot visual anomaly segmentation (ZSAS). However, existing LMM-b…
cs.CV2026
MRAD: Zero-Shot Anomaly Detection with Memory-Driven Retrieval
Chaoran Xu, Chengkan Lv, Qiyu Chen +2
Zero-shot anomaly detection (ZSAD) often leverages pretrained vision or vision-language models, but many existing methods use prompt learning or complex modeling to fit the data di…