arXiv:2605.20284v1 Announce Type: cross 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. However, LMMs lack domain-specific knowledge, which limits their ability to generate accurate responses in complex industrial scenarios. In this work, we present JUDO, Juxtaposed Domain-Oriented Multimodal Reasoner, a framework that efficiently incorporates domain knowledge and context in visual and textual reasoning.
The rapid advancement of Large Multimodal Models (LMMs) is pushing the boundaries of AI application, making domain-specific fine-tuning a critical next step for real-world industrial deployments.
This work addresses a key limitation of general-purpose LMMs in industrial settings by integrating domain-specific knowledge, which is crucial for reliable and accurate anomaly detection and reasoning in high-stakes environments.
The ability to efficiently customize LMMs with proprietary industrial domain knowledge means these powerful AI models can move from generalized capabilities to highly specialized and performant solutions for complex tasks.
- · Industrial automation companies
- · Manufacturing sector
- · AI-driven inspection services
- · LMM developers
- · Legacy anomaly detection systems
- · General-purpose AI solutions lacking customization
Improved accuracy and reliability of AI-driven anomaly detection in industrial contexts, leading to reduced downtime and increased efficiency.
Accelerated adoption of LMMs across various specialized industrial verticals due to their newfound domain specificity, lowering implementation barriers.
The creation of new AI-powered service industries focused on training and maintaining domain-specific LMMs for highly specialized industrial applications, further fragmenting the AI market.
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Read at arXiv cs.LG