ZSG-IAD: A Multimodal Framework for Zero-Shot Grounded Industrial Anomaly Detection

• URL: http://arxiv.org/abs/2604.17949v1
• Date: Mon, 20 Apr 2026 08:30:09 GMT
• Title: ZSG-IAD: A Multimodal Framework for Zero-Shot Grounded Industrial Anomaly Detection
• Abstract summary: ZSG-IAD is a framework for zero-shot grounded industrial anomaly detection.<n>It generates structured anomaly reports and pixel-level anomaly masks.<n>We will release code and annotations to support future research on trustworthy industrial anomaly detection systems.
• Score: 14.275030421757867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning-based industrial anomaly detectors often behave as black boxes, making it hard to justify decisions with physically meaningful defect evidence. We propose ZSG-IAD, a multimodal vision-language framework for zero-shot grounded industrial anomaly detection. Given RGB images, sensor images, and 3D point clouds, ZSG-IAD generates structured anomaly reports and pixel-level anomaly masks. ZSG-IAD introduces a language-guided two-hop grounding module: (1) anomaly-related sentences select evidence-like latent slots distilled from multimodal features, yielding coarse spatial support; (2) selected slots modulate feature maps via channel-spatial gating and a lightweight decoder to produce fine-grained masks. To improve reliability, we further apply Executable-Rule GRPO with verifiable rewards to promote structured outputs, anomaly-region consistency, and reasoning-conclusion coherence. Experiments across multiple industrial anomaly benchmarks show strong zero-shot performance and more transparent, physically grounded explanations than prior methods. We will release code and annotations to support future research on trustworthy industrial anomaly detection systems.

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