Summary of Mmad: a Comprehensive Benchmark For Multimodal Large Language Models in Industrial Anomaly Detection, by Xi Jiang et al.
MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection
by Xi Jiang, Jian Li, Hanqiu Deng, Yong Liu, Bin-Bin Gao, Yifeng Zhou, Jialin Li, Chengjie Wang, Feng Zheng
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel benchmark for multimodal large language models (MLLMs) is proposed to evaluate their performance in industrial anomaly detection. The MMAD dataset consists of 39,672 questions related to 8,366 industrial images, with seven key subtasks defined. A comprehensive evaluation of state-of-the-art MLLMs shows that commercial models performed best, with GPT-4o models achieving an average accuracy of 74.9%. However, this result falls short of industrial requirements, highlighting the need for further improvement. Two training-free performance enhancement strategies are explored to help models improve in industrial scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Industrial inspection may soon see a revolution thanks to multimodal large language models (MLLMs). These powerful tools can understand and process complex information from various sources. However, their ability to detect anomalies in industrial settings has not been thoroughly studied. To change this, researchers created the MMAD dataset, which includes 39,672 questions related to 8,366 images of industrial equipment. They tested several MLLMs on this data and found that commercial models did best. But even they didn’t meet the high standards needed in industry. |
Keywords
» Artificial intelligence » Anomaly detection » Gpt
