Summary of Pscr: Patches Sampling-based Contrastive Regression For Aigc Image Quality Assessment, by Jiquan Yuan et al.
PSCR: Patches Sampling-based Contrastive Regression for AIGC Image Quality Assessment
by Jiquan Yuan, Xinyan Cao, Linjing Cao, Jinlong Lin, Xixin Cao
First submitted to arxiv on: 10 Dec 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: None
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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 paper proposes a novel approach to evaluating the quality of Artificial Intelligence-generated images (AIGIs) called patches sampling-based contrastive regression (PSCR). The existing AIGI quality assessment methods have limitations, such as overlooking differences among AIGIs and scores, and failing to utilize reference images. To address these issues, PSCR introduces a contrastive regression framework that leverages differences among generated images to learn a better representation space. Additionally, the framework employs a patches sampling strategy to avoid geometric distortions and information loss in image inputs. The proposed approach is tested on three mainstream AIGI quality assessment databases, demonstrating significant improvements in model performance. This breakthrough has implications for the development of more accurate AI-generated content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine if you could create fake images using a computer program. But how do we know if those images are good or not? Right now, there’s no easy way to tell. A team of researchers has developed a new method to evaluate the quality of these artificial intelligence-generated images. They used a special approach that looks at differences between many different images and scores them based on how well they match each other. This helps to overcome some problems with previous methods that didn’t take into account all the differences in the images. The new method was tested on three sets of images and showed big improvements over the old ways of doing things. |
Keywords
* Artificial intelligence * Regression
