Summary of Conditional Diffusion on Web-scale Image Pairs Leads to Diverse Image Variations, by Manoj Kumar et al.
Conditional Diffusion on Web-Scale Image Pairs leads to Diverse Image Variations
by Manoj Kumar, Neil Houlsby, Emiel Hoogeboom
First submitted to arxiv on: 23 May 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 A novel approach to generating image variations is introduced, where a model produces modified images while preserving their semantic meaning. Unlike existing methods that adapt text-to-image models to reconstruct input images, this paper proposes a diffusion-based framework, dubbed Semantica, which leverages large collections of image pairs to generate variations. By training Semantica on web-scale text-image data, the model learns to extract relevant context from input images and can be adapted to generate new images from a dataset. The paper also critiques standard image consistency metrics for evaluating image variations and proposes alternative few-shot generation-based metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a machine that can change pictures in creative ways while keeping their original meaning. This is the goal of a new technique called Semantica, which uses big collections of pictures and text to generate new images. The idea is to train this model on lots of picture pairs and then use it to create variations of other images. The authors also talk about how we need better ways to measure if these generated images are good or not. |
Keywords
» Artificial intelligence » Diffusion » Few shot
