Summary of Autobench-v: Can Large Vision-language Models Benchmark Themselves?, by Han Bao et al.
AutoBench-V: Can Large Vision-Language Models Benchmark Themselves?
by Han Bao, Yue Huang, Yanbo Wang, Jiayi Ye, Xiangqi Wang, Xiuying Chen, Yue Zhao, Tianyi Zhou, Mohamed Elhoseiny, Xiangliang Zhang
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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 This paper introduces AutoBench-V, a novel framework for automatically evaluating Large Vision-Language Models (LVLMs) in the visual domain. The traditional method of constructing evaluation benchmarks is time-consuming and static, whereas AutoBench-V leverages text-to-image models to generate relevant image samples and utilizes LVLMs for visual question-answering tasks. This approach enables efficient and flexible evaluation of LVLMs’ capabilities. The framework is evaluated on nine popular LVLMs across five user-defined input scenarios, demonstrating its effectiveness and reliability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super smart AI that can understand images and text. But how do you test if it’s really good at doing that? Right now, testing these AIs takes a lot of human work and is hard to change once set up. This paper shows a new way to automate the testing process using the same AIs that are being tested. It uses special models that generate images and then asks the AI questions about those images. By testing nine different AI models in five different ways, this new approach proves it can be reliable and efficient. |
Keywords
» Artificial intelligence » Question answering
