Commonsense-T2I Challenge: Can Text-to-Image Generation Models Understand Commonsense?

Xingyu Fu, Muyu He, Yujie Lu, William Yang Wang, Dan Roth

Research Area: Evaluation, LMs and the world, LMs and embodiment

Keywords: text to image generation, alignment with reality, commonsense reasoning

TL;DR: This paper finds that text-to-image generation models do not align with the reality, in real world commonsense.

Abstract:

We present a novel task and benchmark for evaluating the ability of text-to-image(T2I) generation models to produce images that align with 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* vs. *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.

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Submission Number: 120