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Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?
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In industrial settings, the accurate detection of anomalies is essential for maintaining product quality and ensuring operational safety. Traditional industrial anomaly detection (IAD) models often struggle with flexibility and adaptability, especially in dynamic production environments where new defect types and operational changes frequently arise. Recent advancements in Multimodal Large Language Models (MLLMs) hold promise for overcoming these limitations by combining visual and textual information processing capabilities. MLLMs excel in general visual understanding due to their training on large, diverse datasets, but they lack domain-specific knowledge, such as industry-specific defect tolerance levels, which limits their effectiveness in IAD tasks. To address these challenges, we propose Echo, a novel multi-expert framework designed to enhance MLLM performance for IAD. Echo integrates four expert modules: Reference Extractor which provides a contextual baseline by retrieving similar normal images, Knowledge Guide which supplies domain-specific insights, Reasoning Expert which enables structured, stepwise reasoning for complex queries, and Decision Maker which synthesizes information from all modules to deliver precise, context-aware responses. Evaluated on the MMAD benchmark, Echo demonstrates significant improvements in adaptability, precision, and robustness, moving closer to meeting the demands of real-world industrial anomaly detection.
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Cited by 5 Pith papers
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AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization
AD-FM combines multi-stage reasoning with localization-aware rewards to fine-tune MLLMs for anomaly detection, improving average accuracy by about 22 percentage points over the base model.
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EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO
A difficulty-aware GRPO training scheme with response resampling, advantage reweighting, GPT-generated text samples, and heatmap-guided contrastive embeddings improves InternVL3-8B by 7.77 percentage points on the MMA...
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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments
With carefully layered prompts and one or three reference samples, GPT-4.1 detects anomalies in cable images and crimp-force features at F1 levels that PatchCore and Isolation Forest reach only after training on dozen...
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OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning
OmniAD unifies industrial anomaly detection and understanding in a single multimodal model using text-encoded masks and reinforcement learning, reporting 79.1 on MMAD and strong detection scores.
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Filter-And-Refine: A MLLM Based Cascade System for Industrial-Scale Video Content Moderation
A cascade of an embedding router and a fine-tuned multimodal LLM ranker is claimed to improve content moderation F1 by 66.5% while using 1.5% of the compute of direct LLM deployment.
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