Summary of Irr: Image Review Ranking Framework For Evaluating Vision-language Models, by Kazuki Hayashi et al.
IRR: Image Review Ranking Framework for Evaluating Vision-Language Models
by Kazuki Hayashi, Kazuma Onishi, Toma Suzuki, Yusuke Ide, Seiji Gobara, Shigeki Saito, Yusuke Sakai, Hidetaka Kamigaito, Katsuhiko Hayashi, Taro Watanabe
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
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 proposes IRR (Image Review Rank), a novel framework to evaluate Large-scale Vision-Language Models’ (LVLMs) ability to generate and evaluate texts reflecting perspectives on images. The proposed framework assesses LVLMs by measuring how closely their judgments align with human interpretations. To validate IRR, the authors use a dataset of 2,000+ data instances from 15 image categories, each with five critic review texts and annotated rankings in both English and Japanese. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how well computers can understand and describe pictures. It shows that these machines are good at describing what’s happening in a picture, but they’re not very good at understanding different perspectives on the same picture. The researchers created a new way to test computer programs for this kind of task. They used a big collection of pictures and asked people to write reviews about each one from different points of view. Then, they tested how well the computer programs could match up with what the people wrote. |
