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.
@article{arxiv.2506.04717,
title = {Using In-Context Learning for Automatic Defect Labelling of Display Manufacturing Data},
author = {Babar Hussain and Qiang Liu and Gang Chen and Bihai She and Dahai Yu},
journal= {arXiv preprint arXiv:2506.04717},
year = {2025}
}