KITTEN: A Knowledge-Intensive Evaluation of Image Generation on Visual Entities

TMLR Paper6310 Authors

Abstract: Recent advances in text-to-image generation have improved the quality of synthesized images, but evaluations mainly focus on aesthetics or alignment with text prompts. Thus, it remains unclear whether these models can accurately represent a wide variety of realistic visual entities. To bridge this gap, we propose Kitten, a benchmark for Knowledge-InTensive image generaTion on real-world ENtities. Using Kitten, we conduct a systematic study of recent text-to-image models, retrieval-augmented models, and unified understanding and generation models, focusing on their ability to generate real-world visual entities such as landmarks and animals. Analyses using carefully designed human evaluations, automatic metrics, and MLLMs as judges show that even advanced text-to-image and unified models fail to generate accurate visual details of entities. While retrieval-augmented models improve entity fidelity by incorporating reference images, they tend to over-rely on them and struggle to create novel configurations of the entities in creative text prompts.

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

Changes Since Last Submission: N/A

Assigned Action Editor: ~Ning_Yu2

Submission Number: 6310