Summary of I2ebench: a Comprehensive Benchmark For Instruction-based Image Editing, by Yiwei Ma et al.
I2EBench: A Comprehensive Benchmark for Instruction-based Image Editing
by Yiwei Ma, Jiayi Ji, Ke Ye, Weihuang Lin, Zhibin Wang, Yonghan Zheng, Qiang Zhou, Xiaoshuai Sun, Rongrong Ji
First submitted to arxiv on: 26 Aug 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 The proposed benchmark, I2EBench, addresses the need for a comprehensive evaluation framework in Instruction-based Image Editing (IIE). The benchmark consists of over 2,000 images for editing, along with 4,000+ corresponding original and diverse instructions. It offers three key features: 16 evaluation dimensions that cover both high-level and low-level aspects, human perception alignment through an extensive user study, and valuable research insights to guide future development. I2EBench aims to provide a comprehensive assessment of IIE models from multiple perspectives, facilitating the advancement of this field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary I2EBench is a new way to test how good image editing models are. These models help change images based on instructions. Before, it was hard to compare these models because there wasn’t a standard way to measure their quality. The I2EBench team created a big set of images and instructions (over 6,000!) that will make it easier to test and compare these models. They also asked people what they think about the edited images, which helps make sure the benchmark is accurate. This new tool will help scientists develop better image editing models in the future. |
Keywords
» Artificial intelligence » Alignment
