Commonsense-T2I Challenge: Can Text-to-Image Generation Models
  Understand Commonsense?
Authors: name: Xingyu Fu, name: Muyu He, name: Yujie Lu, name: William Yang Wang, name: Dan Roth

We present a novel task and benchmark for evaluating the ability of
text-to-image(T2I) generation models to produce images that fit commonsense in
real life, which we call Commonsense-T2I. Given two adversarial text prompts
containing an identical set of action words with minor differences, such as "a
lightbulb without electricity" v.s. "a lightbulb with electricity", we evaluate
whether T2I models can conduct visual-commonsense reasoning, e.g. produce
images that fit "the lightbulb is unlit" vs. "the lightbulb is lit"
correspondingly. Commonsense-T2I presents an adversarial challenge, providing
pairwise text prompts along with expected outputs. The dataset is carefully
hand-curated by experts and annotated with fine-grained labels, such as
commonsense type and likelihood of the expected outputs, to assist analyzing
model behavior. We benchmark a variety of state-of-the-art (sota) T2I models
and surprisingly find that, there is still a large gap between image synthesis
and real life photos--even the DALL-E 3 model could only achieve 48.92% on
Commonsense-T2I, and the stable diffusion XL model only achieves 24.92%
accuracy. Our experiments show that GPT-enriched prompts cannot solve this
challenge, and we include a detailed analysis about possible reasons for such
deficiency. We aim for Commonsense-T2I to serve as a high-quality evaluation
benchmark for T2I commonsense checking, fostering advancements in real life
image generation.