Summary of Commonsense-t2i Challenge: Can Text-to-image Generation Models Understand Commonsense?, by Xingyu Fu et al.
Commonsense-T2I Challenge: Can Text-to-Image Generation Models Understand Commonsense?
by Xingyu Fu, Muyu He, Yujie Lu, William Yang Wang, Dan Roth
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper introduces a novel task called Commonsense-T2I, which evaluates the ability of text-to-image (T2I) generation models to produce images that align with real-life commonsense. The task involves providing two adversarial text prompts containing identical action words but with minor differences, and assessing whether T2I models can conduct visual-commonsense reasoning by generating corresponding images. For instance, given the prompts “a lightbulb without electricity” vs. “a lightbulb with electricity”, the model should produce an image that fits the expected output (i.e., unlit vs. lit). The dataset is carefully curated and annotated to facilitate analyzing model behavior. State-of-the-art T2I models, including DALL-E 3 and stable diffusion XL, are benchmarked on this task, revealing a significant gap between image synthesis and real-life photos. The authors attribute the deficiency in performance to the limitations of GPT-enriched prompts and propose Commonsense-T2I as a high-quality evaluation benchmark for T2I commonsense checking. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to test how well computers can generate images that make sense in everyday life. It’s like asking an AI to take a picture of something based on what you tell it, but making sure the image makes sense with what you’re saying. For example, if you ask for a picture of a lightbulb without electricity, the computer should take a picture of an unlit lightbulb. The authors created a special set of prompts and images to test this ability and found that even the best AI models struggle to get it right. |
Keywords
» Artificial intelligence » Diffusion » Gpt » Image synthesis
