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arXiv · 2410.11824

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

Hsin-Ping Huang·Xinyi Wang·Yonatan Bitton·Hagai Taitelbaum·Gaurav Singh Tomar·Ming-Wei Chang·Xuhui Jia·Kelvin C. K. Chan·Hexiang Hu·Yu-Chuan Su·Ming-Hsuan Yang
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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 the latest text-to-image models and retrieval-augmented models, focusing on their ability to generate real-world visual entities, such as landmarks and animals. Analysis using carefully designed human evaluations, automatic metrics, and MLLM evaluations show that even advanced text-to-image 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 entity in creative text prompts.

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Hsin-Ping Huang, Xinyi Wang, Yonatan Bitton, Hagai Taitelbaum, Gaurav Singh Tomar, Ming-Wei Chang, Xuhui Jia, Kelvin C. K. Chan, Hexiang Hu, Yu-Chuan Su, Ming-Hsuan Yang. 2024-10-15. KITTEN: A Knowledge-Intensive Evaluation of Image Generation on Visual Entities. https://arxiv.org/abs/2410.11824

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