Multi-Modal Language Models as Text-to-Image Model Evaluators

TMLR Paper5137 Authors

Abstract:

The steady improvements of text-to-image (T2I) generative models lead to slow deprecation of automatic evaluation benchmarks that rely on static datasets, motivating researchers to seek alternative ways to evaluate the T2I progress. In this paper, we explore the potential of multi-modal large language models (MLLMs) as evaluator agents that interact with a T2I model, with the objective of assessing prompt-generation consistency and image aesthetics. We present Multimodal Text-to-Image Eval (MT2IE), an evaluation framework that iteratively generates prompts for evaluation, scores generated images and matches T2I evaluation of existing benchmarks with a fraction of the prompts used in existing static benchmarks. We show that MT2IE’s prompt-generation consistency scores have higher correlation with human judgment than prompt consistency metrics previously introduced
in the literature. MT2IE generates prompts that are efficient at probing T2I model performance, producing the same relative T2I model rankings as existing benchmarks while evaluating on 80× less prompts. We hope that these results will unlock the development of dynamic and interactive evaluation frameworks, and mitigate the deprecation of automatic evaluation benchmarks.

Submission Length: Regular submission (no more than 12 pages of main content)

Previous TMLR Submission Url: https://openreview.net/forum?id=RcdX7U5CGw&nesting=2&sort=date-desc

Changes Since Last Submission: Removed author-initialed comments from the Appendix that were mistakenly included in the first submission.

Assigned Action Editor: ~Chinmay_Hegde1

Submission Number: 5137