JUDO: A Juxtaposed Domain-Oriented Multimodal Reasoner for Industrial Anomaly QA
The paper presents JUDO, a new framework for industrial anomaly detection that integrates domain knowledge into visual and textual reasoning. JUDO enhances the performance of Large Multimodal Models by incorporating domain-specific context, allowing for improved anomaly understanding. Experimental results indicate that JUDO outperforms existing models on the MMAD benchmark.
- ▪JUDO is a Juxtaposed Domain-Oriented Multimodal Reasoner designed for industrial anomaly detection.
- ▪The framework uses visual reasoning to segment defect regions by comparing query images with normal images.
- ▪JUDO incorporates domain knowledge through supervised fine-tuning and reinforcement learning to enhance reasoning capabilities.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20284 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Computer Vision and Pattern Recognition arXiv:2605.20284 (cs) [Submitted on 19 May 2026] Title:JUDO: A Juxtaposed Domain-Oriented Multimodal Reasoner for Industrial Anomaly QA Authors:Hyunju Kang, Woohyun Lee, Jaewon Kim, Hogun Park View a PDF of the paper titled JUDO: A Juxtaposed Domain-Oriented Multimodal Reasoner for Industrial Anomaly QA, by Hyunju Kang and 3 other authors View PDF HTML (experimental) Abstract:Industrial anomaly detection has been significantly advanced by Large Multimodal Models (LMMs), enabling diverse human instructions beyond detection, particularly through visually grounded reasoning for better image understanding.
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