LongT2IBench: A Benchmark for Evaluating Long Text-to-Image Generation with Graph-structured Annotations

Understand

The increasing popularity of long Text-to-Image (T2I) generation has created an urgent need for automatic and interpretable models that can evaluate the image-text alignment in long prompt scenarios.

• However, the existing T2I alignment benchmarks predominantly focus on short prompt scenarios and only provide MOS or Likert scale annotations.
• This inherent limitation hinders the development of long T2I evaluators, particularly in terms of the interpretability of alignment.
• In this study, we contribute LongT2IBench, which comprises 14K long text-image pairs accompanied by graph-structured human annotations.