AI RESEARCH
VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection
arXiv CS.CV
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ArXi:2409.20146v2 Announce Type: replace Zero-shot anomaly detection (ZSAD) recognizes and localizes anomalies in previously unseen objects by establishing feature mapping between textual prompts and inspection images, nstrating excellent research value in flexible industrial manufacturing. However, existing ZSAD methods are limited by closed-world settings, struggling to unseen defects with predefined prompts. Recently, adapting Multimodal Large Language Models (MLLMs) for Industrial Anomaly Detection (IAD) presents a viable solution.