GenAU: Language-Grounded Industrial Anomaly Understanding with Vision-Language Models
Industrial inspection requires more than binary anomaly detection: a practical system should determine whether an anomaly exists, localize the defective region, identify the defect type, and provide interpretable visual evidence. Existing CLIP-based methods detect and localize anomalies well but offer limited language-level defect understanding, while instruction-tuned vision-language models can describe defects but do not natively produce pixel-level masks. We introduce GenAU, a Generalist vision-language framework for industrial Anomaly Understanding that unifies image-level detection, pixel
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
- Linked via arxiv authorHongkuan Zhou →
GenAU: Language-Grounded Industrial Anomaly Understanding with Vision-Language Models
- Linked via arxiv authorTristan Rehm →
GenAU: Language-Grounded Industrial Anomaly Understanding with Vision-Language Models
- Linked via arxiv authorNadeem Nazer →
GenAU: Language-Grounded Industrial Anomaly Understanding with Vision-Language Models
- Linked via arxiv authorLavdim Halilaj →
GenAU: Language-Grounded Industrial Anomaly Understanding with Vision-Language Models
- Linked via arxiv authorJingcheng Wu →
GenAU: Language-Grounded Industrial Anomaly Understanding with Vision-Language Models
- Linked via arxiv authorSteffen Staab →
GenAU: Language-Grounded Industrial Anomaly Understanding with Vision-Language Models