Using In-Context Learning for Automatic Defect Labelling of Display Manufacturing Data

TL;DR

This paper introduces an AI-assisted auto-labeling system using in-context learning and enhanced SegGPT architecture to efficiently label display manufacturing data, significantly reducing manual effort while maintaining high detection performance.

Contribution

The paper presents a novel auto-labeling approach with domain-specific training and scribble-based annotation, improving defect detection accuracy and efficiency in industrial display manufacturing.

Findings

Achieved 0.22 IoU improvement over baseline

Improved recall by 14% across product types

Maintained performance comparable to human-labeled data

Abstract

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.