GIE-Bench: A Grounded Evaluation for Text-Guided Image Editing
Analysis
This article introduces GIE-Bench, a new benchmark developed by Apple ML to improve the evaluation of text-guided image editing models. The current evaluation methods, which rely on image-text similarity metrics like CLIP, are considered imprecise. GIE-Bench aims to provide a more grounded evaluation by focusing on functional correctness. This is achieved through automatically generated multiple-choice questions that assess whether the intended changes were successfully implemented. This approach represents a significant step towards more accurate and reliable evaluation of AI models in image editing.
Key Takeaways
- •GIE-Bench is a new benchmark for evaluating text-guided image editing models.
- •It addresses the limitations of existing evaluation methods that rely on image-text similarity.
- •The benchmark focuses on functional correctness using automatically generated multiple-choice questions.
“Editing images using natural language instructions has become a natural and expressive way to modify visual content; yet, evaluating the performance of such models remains challenging.”