Evaluating Hallucination in Text-to-Image Diffusion Models with Scene-Graph based Question-Answering Agent

TL;DR

This paper introduces a novel automatic evaluation method for text-to-image models using scene-graph based question-answering with large language models, aiming to detect hallucinations and align more closely with human judgments.

Contribution

It proposes a scene-graph based question-answering approach combined with LLMs for more accurate T2I evaluation, and provides a new dataset with human scores for validation.

Findings

Method aligns better with human scoring than existing metrics

Generated a dataset of 12,000 images with human ratings

Demonstrates effectiveness in detecting hallucinations

Abstract

Contemporary Text-to-Image (T2I) models frequently depend on qualitative human evaluations to assess the consistency between synthesized images and the text prompts. There is a demand for quantitative and automatic evaluation tools, given that human evaluation lacks reproducibility. We believe that an effective T2I evaluation metric should accomplish the following: detect instances where the generated images do not align with the textual prompts, a discrepancy we define as the `hallucination problem' in T2I tasks; record the types and frequency of hallucination issues, aiding users in understanding the causes of errors; and provide a comprehensive and intuitive scoring that close to human standard. To achieve these objectives, we propose a method based on large language models (LLMs) for conducting question-answering with an extracted scene-graph and created a dataset with human-rated scores for generated images. From the methodology perspective, we combine knowledge-enhanced question-answering tasks with image evaluation tasks, making the evaluation metrics more controllable and easier to interpret. For the contribution on the dataset side, we generated 12,000 synthesized images based on 1,000 composited prompts using three advanced T2I models. Subsequently, we conduct human scoring on all synthesized images and prompt pairs to validate the accuracy and effectiveness of our method as an evaluation metric. All generated images and the human-labeled scores will be made publicly available in the future to facilitate ongoing research on this crucial issue. Extensive experiments show that our method aligns more closely with human scoring patterns than other evaluation metrics.