Revisiting text-to-image evaluation with Gecko: on metrics, prompts, and human rating

Olivia Wiles, Chuhan Zhang, Isabela Albuquerque, Ivana Kajic, Su Wang, Emanuele Bugliarello, Yasumasa Onoe, Pinelopi Papalampidi, Ira Ktena, Christopher Knutsen, Cyrus Rashtchian, Anant Nawalgaria, Jordi Pont-Tuset, Aida Nematzadeh

Keywords: text-to-image evaluation; text-to-image alignment; human evaluation;

TL;DR: We create a large benchmark for T2I alignment to evaluate models and metrics across skills, evaluation tasks, and human annotation templates.

Abstract:

While text-to-image (T2I) generative models have become ubiquitous, they do not necessarily generate images that align with a given prompt.
While many metrics and benchmarks have been proposed to evaluate T2I models and alignment metrics, the impact of the evaluation components (prompt sets, human annotations, evaluation task) has not been systematically measured.
We find that looking at only *one slice of data*, i.e. one set of capabilities or human annotations, is not enough to obtain stable conclusions that generalise to new conditions or slices when evaluating T2I models or alignment metrics.
We address this by introducing an evaluation suite of $>$100K annotations across four human annotation templates that comprehensively evaluates models' capabilities across a range of methods for gathering human annotations and comparing models.
In particular, we propose (1) a carefully curated set of prompts -- *Gecko2K*; (2) a statistically grounded method of comparing T2I models; and (3) how to systematically evaluate metrics under three *evaluation tasks* -- *model ordering, pair-wise instance scoring, point-wise instance scoring*.
Using this evaluation suite, we evaluate a wide range of metrics and find that a metric may do better in one setting but worse in another.
As a result, we introduce a new, interpretable auto-eval metric that is consistently better correlated with human ratings than such existing metrics on our evaluation suite--across different human templates and evaluation settings--and on TIFA160.

Primary Area: datasets and benchmarks

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Submission Number: 6464