Commonsense-T2I Challenge: Can Text-to-Image Generation Models
  Understand Commonsense?
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.
666bb618925d891550f98440
Xingyu Fu
admin_assigned
2024-06-14T11:24:05.378Z
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/avatars/829f69436225d05d2c2136bc90f640d7.svg
Xingyu Fu
user
Fiaa
666bb618925d891550f98441
Muyu He
666bb618925d891550f98442
Yujie Lu
claimed_verified
2024-06-14T07:22:26.614Z
633cfb6ad5935998f7582b7b
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Yujie Lu
user
FunCube
666bb618925d891550f98443
William Yang Wang
admin_assigned
2024-06-14T11:24:32.258Z
6476607efb22e3b77f3f1452
/avatars/22f40c81e6b6e09bdb66a5afd978f98b.svg
William Yang Wang
user
wangwilliamyang
666bb618925d891550f98444
Dan Roth
admin_assigned
2024-06-14T11:24:56.092Z
63191aa1f18d0b5d999a47fc
/avatars/d7eadb22aa3d8a067189b924767864e9.svg
Dan Roth
user
danielpaulroth
Commonsense-T2I Challenge: Can Text-to-Image Generation Models
  Understand Commonsense?
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.
A new benchmark, Commonsense-T2I, evaluates text-to-image models' ability to produce commonsense-aligned images from adversarial prompts, highlighting significant gaps in current models' performance.
text-to-image Commonsense-T2I visual-commonsense reasoning state-of-the-art DALL-E 3 stable diffusion XL GPT-enriched prompts