Summary of Holistic Evaluation For Interleaved Text-and-image Generation, by Minqian Liu et al.
Holistic Evaluation for Interleaved Text-and-Image Generation
by Minqian Liu, Zhiyang Xu, Zihao Lin, Trevor Ashby, Joy Rimchala, Jiaxin Zhang, Lifu Huang
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers introduce InterleavedBench, a novel benchmark for evaluating interleaved text-and-image generation models. These models generate both images and text pieces in an arbitrary order, which is essential for applications like storytelling and news reporting. The existing evaluation benchmarks fall short as they only cover a limited number of domains and use cases. To overcome this limitation, the authors develop InterleavedEval, a strong reference-free metric powered by GPT-4o to deliver accurate and explainable evaluation. This metric assesses five essential aspects: text quality, perceptual quality, image coherence, text-image coherence, and helpfulness. The results show that InterleavedBench and InterleavedEval can effectively evaluate existing models with a strong correlation with human judgments, surpassing previous reference-based metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists created a new way to test how well machines can make up stories using pictures and words. Right now, there’s no good way to check if these machines are doing a good job or not. The researchers made two new tools: InterleavedBench and InterleavedEval. InterleavedBench is like a guidebook that shows what kind of tasks the machine should be able to do. InterleavedEval is like a special test that checks how well the machine does these tasks without comparing it to anything else. The scientists tested their tools and found that they worked really well, which will help make better machines in the future. |
Keywords
» Artificial intelligence » Gpt » Image generation
