Summary of Consistency-diversity-realism Pareto Fronts Of Conditional Image Generative Models, by Pietro Astolfi et al.
Consistency-diversity-realism Pareto fronts of conditional image generative models
by Pietro Astolfi, Marlene Careil, Melissa Hall, Oscar Mañas, Matthew Muckley, Jakob Verbeek, Adriana Romero Soriano, Michal Drozdzal
First submitted to arxiv on: 14 Jun 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 paper proposes a new approach to evaluating conditional image generative models, focusing on their ability to accurately represent the real world and generate diverse images that are consistent with prompts. The authors use state-of-the-art text-to-image and image-and-text-to-image models to create Pareto fronts that visualize the tradeoff between realism, consistency, and diversity. They find that earlier models excel in representation diversity but struggle with consistency and realism, while more recent models prioritize consistency and realism at the expense of diversity. The authors also analyze a geodiverse dataset and find significant disparities in consistency-diversity-realism performance across different geographical regions. Overall, the paper suggests that there is no single “best” model and that the choice of model should be determined by the specific application. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research aims to create world models using conditional image generative models. These models need to balance image quality, prompt-image consistency, and representation diversity. The authors look at how different knobs in these models affect their performance. They find that you can get both high realism and consistency, but it’s hard to achieve high diversity as well. Older models are better at generating diverse images, while newer models do a great job with realism and consistency. The study also looks at how models perform in different parts of the world. |
Keywords
» Artificial intelligence » Prompt
