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

**Authors:** Xingyu Fu, Muyu He, Yujie Lu, William Yang Wang, Dan Roth  
**arXiv:** [2406.07546](https://arxiv.org/abs/2406.07546)  
**Date:** 2024-06-11  
**Summary page:** https://www.summarizepaper.com/en/paper/2406.07546v1/

## Key points

- Significant progress in text-to-image (T2I) synthesis field
- Introduction of Commonsense-T2I task and benchmark for evaluating T2I models' ability to generate images aligned with common sense
- Dataset curated by experts with fine-grained labels for analyzing model behavior
- State-of-the-art T2I models like DALL·E 3 and stable diffusion XL struggled on the Commonsense-T2I task
- GPT-enriched prompts did not significantly improve model performance on the challenge
- Proposed evaluation metrics specific to Commonsense-T2I align with human perceptions
- Interest in reasoning-related research questions about multimodality sparked by multimodal Large Language Models (LLMs)
- Need for comprehensive studies on commonsense reasoning within T2I models
- Ongoing advancements in generative modeling pushing boundaries of text-to-image synthesis, highlighting complexity in bridging language and visual information seamlessly

## Summary

Significant progress has been made in the field of text-to-image (T2I) synthesis in recent years. Models such as DALL·E and diffusion models have demonstrated impressive results. However, there remains a gap between synthesized images and real-life photos. To address this challenge, a new task and benchmark called Commonsense-T2I have been introduced to evaluate T2I models' ability to generate images that align with common sense in real-world scenarios. This dataset presents an adversarial challenge by providing pairs of text prompts with minor differences in action words and evaluating whether T2I models can conduct visual-commonsense reasoning to produce corresponding images that fit the prompts. The dataset is meticulously curated by experts and annotated with fine-grained labels to assist in analyzing model behavior. Various state-of-the-art T2I models were benchmarked on Commonsense-T2I, revealing that even advanced models like DALL·E 3 and stable diffusion XL struggled to achieve high accuracy on the task. Surprisingly, GPT-enriched prompts did not significantly improve model performance on this challenge. Existing evaluation metrics for T2I models primarily focus on fidelity, image-text alignment, or use of large language models but lack comprehensive assessment of commonsense understanding in image generation tasks. The proposed evaluation metrics specific to Commonsense-T2I demonstrate alignment with human perceptions. The introduction of multimodal Large Language Models (LLMs) has sparked interest in reasoning-related research questions about multimodality. However, there is still a need for comprehensive studies on commonsense reasoning within T2I models. In conclusion, Commonsense-T2I provides a valuable test set for evaluating the commonsense reasoning abilities of T2I models. While current models show moderate performance on the dataset, future research in this direction is encouraged. Limitations exist due to manual curation constraints; however, leveraging inspiration generation methods could facilitate the creation of larger weak-supervision datasets for further exploration in this area. Overall, ongoing advancements in generative modeling continue to push the boundaries of text-to-image synthesis but also highlight the complexity inherent in bridging language and visual information seamlessly.