Evaluating Image Hallucination in Text-to-Image Generation with Question-Answering

TL;DR

This paper introduces I-HallA, an automated evaluation metric and benchmark dataset for assessing factual accuracy in text-to-image models using visual question answering, revealing current models often hallucinate facts.

Contribution

The paper presents I-HallA, a novel metric and dataset for evaluating factual correctness in text-to-image generation through VQA-based assessment.

Findings

State-of-the-art TTI models often hallucinate factual content.

I-HallA metric shows a high correlation (ρ=0.95) with human judgments.

The dataset includes 1.2K image-text pairs with 1,000 curated questions.

Abstract

Despite the impressive success of text-to-image (TTI) generation models, existing studies overlook the issue of whether these models accurately convey factual information. In this paper, we focus on the problem of image hallucination, where images created by generation models fail to faithfully depict factual content. To address this, we introduce I-HallA (Image Hallucination evaluation with Question Answering), a novel automated evaluation metric that measures the factuality of generated images through visual question answering (VQA). We also introduce I-HallA v1.0, a curated benchmark dataset for this purpose. As part of this process, we develop a pipeline that generates high-quality question-answer pairs using multiple GPT-4 Omni-based agents, with human judgments to ensure accuracy. Our evaluation protocols measure image hallucination by testing if images from existing TTI models can correctly respond to these questions. The I-HallA v1.0 dataset comprises 1.2K diverse image-text pairs across nine categories with 1,000 rigorously curated questions covering various compositional challenges. We evaluate five TTI models using I-HallA and reveal that these state-of-the-art models often fail to accurately convey factual information. Moreover, we validate the reliability of our metric by demonstrating a strong Spearman correlation ($\rho$=0.95) with human judgments. We believe our benchmark dataset and metric can serve as a foundation for developing factually accurate TTI generation models. Additional resources can be found on our project page: https://sgt-lim.github.io/I-HallA/.