Summary of Diffusyn Bench: Evaluating Vision-language Models on Real-world Complexities with Diffusion-generated Synthetic Benchmarks, by Haokun Zhou et al.
DiffuSyn Bench: Evaluating Vision-Language Models on Real-World Complexities with Diffusion-Generated Synthetic Benchmarks
by Haokun Zhou, Yipeng Hong
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 This study evaluates the ability of Large Vision-Language Models (LVLMs) to differentiate between AI-generated and human-generated images. The research introduces a new automated benchmark construction method for this evaluation. Compared to humans, LVLMs showed some extent of differentiation but exhibited a rightward bias and performed worse than humans. To build upon these findings, the study developed an automated benchmark construction process using AI, which involved topic retrieval, narrative script generation, error embedding, and image generation. This approach created diverse text-image pairs with intentional errors. The method was validated by constructing two capable benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well Large Vision-Language Models (LVLMs) can tell the difference between pictures made by computers and those made by humans. It also introduces a new way to make tests for these models. The results show that LVLMs are not as good at telling the difference as humans are, but they do get some things right. To help improve this, the study created a new way to make tests using AI, which makes it easier and faster to test these models. |
Keywords
» Artificial intelligence » Embedding » Image generation
