Summary of Cot Flow: Learning Optimal-transport Image Sampling and Editing by Contrastive Pairs, By Xinrui Zu et al.
COT Flow: Learning Optimal-Transport Image Sampling and Editing by Contrastive Pairs
by Xinrui Zu, Qian Tao
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 paper presents a new method called Contrastive Optimal Transport Flow (COT Flow) that improves upon existing diffusion models in sampling and editing multi-modal data. The key innovations are the use of optimal transport (OT), which allows for unpaired image-to-image translation and increases the editable space, and a single-step generation process that achieves competitive results to state-of-the-art methods. The COT Editor is introduced as a tool for user-guided editing with excellent flexibility and quality. The method can be used for various applications such as image-to-image translation and zero-shot editing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way of generating images using something called Contrastive Optimal Transport Flow (COT Flow). It’s like a super-powerful tool that can change one picture into another without needing to see both pictures beforehand. This is important because it means the computer doesn’t need to learn how to change specific types of pictures, just that it knows how to change pictures in general. The COT Editor helps people edit images by giving them more control over what changes are made. |
Keywords
» Artificial intelligence » Diffusion » Multi modal » Translation » Zero shot
