Summary of Hq-edit: a High-quality Dataset For Instruction-based Image Editing, by Mude Hui et al.
HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing
by Mude Hui, Siwei Yang, Bingchen Zhao, Yichun Shi, Heng Wang, Peng Wang, Yuyin Zhou, Cihang Xie
First submitted to arxiv on: 15 Apr 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 study introduces HQ-Edit, a high-quality instruction-based image editing dataset with around 200,000 edits. The dataset is collected using advanced foundation models GPT-4V and DALL-E 3, without relying on attribute guidance or human feedback. The dataset consists of diptychs featuring input and output images with detailed text prompts, ensuring precise alignment through post-processing. Two evaluation metrics, Alignment and Coherence, are proposed to quantify the quality of image edit pairs using GPT-4V. HQ-Edit’s high-resolution images and comprehensive editing prompts enhance the capabilities of existing image editing models. For example, an HQ-Edit finetuned InstructPix2Pix can attain state-of-the-art image editing performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a new dataset called HQ-Edit that helps computers learn to edit pictures better. It collects lots of examples of edits online and then uses them to create high-quality pairs of input and output images with clear instructions. This makes it easier for computer models to learn how to edit pictures by themselves, without needing human help. The researchers also come up with two ways to measure how well the computers are doing at editing pictures. |
Keywords
» Artificial intelligence » Alignment » Gpt
