Summary of 2afc Prompting Of Large Multimodal Models For Image Quality Assessment, by Hanwei Zhu et al.
2AFC Prompting of Large Multimodal Models for Image Quality Assessment
by Hanwei Zhu, Xiangjie Sui, Baoliang Chen, Xuelin Liu, Peilin Chen, Yuming Fang, Shiqi Wang
First submitted to arxiv on: 2 Feb 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 In this research, the authors focus on improving high-level visual understanding and reasoning capabilities of large multimodal models (LMMs). Specifically, they investigate the visual quality assessment (IQA) ability of LMMs using the two-alternative forced choice (2AFC) prompting method. The authors introduce three evaluation criteria to assess the IQA capability of five LMMs: consistency, accuracy, and correlation. They find that existing LMMs perform well on coarse-grained quality comparison but struggle with fine-grained quality discrimination. To facilitate future development of IQA models based on LMMs, the authors propose a new dataset and provide publicly available codes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about making sure large computers can tell good pictures from bad ones. The scientists used a special way to ask people what they think of different pictures, and then compared it to how well some big computer models could do the same thing. They found that these big computer models are pretty good at saying if one picture is better than another, but they’re not as good at saying exactly what makes one picture better than another. The scientists hope this will help them make even better computer models in the future. |
Keywords
» Artificial intelligence » Prompting
