Towards Geographic Inclusion in the Evaluation of Text-to-Image Models

TL;DR

This study highlights significant cross-cultural differences in perceptions of geographic representation and visual appeal in text-to-image models, revealing limitations of current automated metrics and proposing improvements for more inclusive evaluations.

Contribution

It provides a large-scale cross-cultural analysis of human preferences in image generation, exposing biases in automated metrics and suggesting ways to enhance evaluation methods.

Findings

Human preferences vary across regions for geographic representation and visual appeal.

Current automated metrics do not fully capture cultural diversity in perceptions.

Disagreements exist among annotators from different regions on stereotypical depictions.

Abstract

Rapid progress in text-to-image generative models coupled with their deployment for visual content creation has magnified the importance of thoroughly evaluating their performance and identifying potential biases. In pursuit of models that generate images that are realistic, diverse, visually appealing, and consistent with the given prompt, researchers and practitioners often turn to automated metrics to facilitate scalable and cost-effective performance profiling. However, commonly-used metrics often fail to account for the full diversity of human preference; often even in-depth human evaluations face challenges with subjectivity, especially as interpretations of evaluation criteria vary across regions and cultures. In this work, we conduct a large, cross-cultural study to study how much annotators in Africa, Europe, and Southeast Asia vary in their perception of geographic representation, visual appeal, and consistency in real and generated images from state-of-the art public APIs. We collect over 65,000 image annotations and 20 survey responses. We contrast human annotations with common automated metrics, finding that human preferences vary notably across geographic location and that current metrics do not fully account for this diversity. For example, annotators in different locations often disagree on whether exaggerated, stereotypical depictions of a region are considered geographically representative. In addition, the utility of automatic evaluations is dependent on assumptions about their set-up, such as the alignment of feature extractors with human perception of object similarity or the definition of "appeal" captured in reference datasets used to ground evaluations. We recommend steps for improved automatic and human evaluations.