Using In-Context Learning for Automatic Defect Labelling of Display Manufacturing Data
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916781004685312 |
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| author | Hussain, Babar Liu, Qiang Chen, Gang She, Bihai Yu, Dahai |
| author_facet | Hussain, Babar Liu, Qiang Chen, Gang She, Bihai Yu, Dahai |
| contents | This paper presents an AI-assisted auto-labeling system for display panel defect detection that leverages in-context learning capabilities. We adopt and enhance the SegGPT architecture with several domain-specific training techniques and introduce a scribble-based annotation mechanism to streamline the labeling process. Our two-stage training approach, validated on industrial display panel datasets, demonstrates significant improvements over the baseline model, achieving an average IoU increase of 0.22 and a 14% improvement in recall across multiple product types, while maintaining approximately 60% auto-labeling coverage. Experimental results show that models trained on our auto-labeled data match the performance of those trained on human-labeled data, offering a practical solution for reducing manual annotation efforts in industrial inspection systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04717 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Using In-Context Learning for Automatic Defect Labelling of Display Manufacturing Data Hussain, Babar Liu, Qiang Chen, Gang She, Bihai Yu, Dahai Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning This paper presents an AI-assisted auto-labeling system for display panel defect detection that leverages in-context learning capabilities. We adopt and enhance the SegGPT architecture with several domain-specific training techniques and introduce a scribble-based annotation mechanism to streamline the labeling process. Our two-stage training approach, validated on industrial display panel datasets, demonstrates significant improvements over the baseline model, achieving an average IoU increase of 0.22 and a 14% improvement in recall across multiple product types, while maintaining approximately 60% auto-labeling coverage. Experimental results show that models trained on our auto-labeled data match the performance of those trained on human-labeled data, offering a practical solution for reducing manual annotation efforts in industrial inspection systems. |
| title | Using In-Context Learning for Automatic Defect Labelling of Display Manufacturing Data |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2506.04717 |