MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection
This work addresses the problem of assessing MLLM capabilities for industrial inspection, providing a foundational benchmark for researchers and practitioners, though it is incremental as it focuses on evaluation rather than new methods.
The authors tackled the lack of systematic evaluation of Multimodal Large Language Models (MLLMs) in industrial anomaly detection by creating MMAD, a benchmark with 39,672 questions across seven subtasks, finding that top models like GPT-4o achieved only 74.9% accuracy, which is insufficient for industrial needs.
In the field of industrial inspection, Multimodal Large Language Models (MLLMs) have a high potential to renew the paradigms in practical applications due to their robust language capabilities and generalization abilities. However, despite their impressive problem-solving skills in many domains, MLLMs' ability in industrial anomaly detection has not been systematically studied. To bridge this gap, we present MMAD, the first-ever full-spectrum MLLMs benchmark in industrial Anomaly Detection. We defined seven key subtasks of MLLMs in industrial inspection and designed a novel pipeline to generate the MMAD dataset with 39,672 questions for 8,366 industrial images. With MMAD, we have conducted a comprehensive, quantitative evaluation of various state-of-the-art MLLMs. The commercial models performed the best, with the average accuracy of GPT-4o models reaching 74.9%. However, this result falls far short of industrial requirements. Our analysis reveals that current MLLMs still have significant room for improvement in answering questions related to industrial anomalies and defects. We further explore two training-free performance enhancement strategies to help models improve in industrial scenarios, highlighting their promising potential for future research.