Summary of Personalized and Sequential Text-to-image Generation, by Ofir Nabati et al.
Personalized and Sequential Text-to-Image Generation
by Ofir Nabati, Guy Tennenholtz, ChihWei Hsu, Moonkyung Ryu, Deepak Ramachandran, Yinlam Chow, Xiang Li, Craig Boutilier
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Systems and Control (eess.SY)
GrooveSquid.com Paper Summaries
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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 We present Personalized And Sequential Text-to-image Agent (PASTA), a reinforcement learning agent that iteratively improves text-to-image (T2I) generation through prompt expansions. PASTA leverages a large multimodal language model, value-based RL approach, and sequential preferences dataset to provide personalized and diverse image suggestions. This collaborative co-creation framework addresses uncertainty in user intent, enabling users to interactively generate images that align with their preferences. Our evaluation using human raters shows significant improvement over baseline methods, demonstrating PASTA’s effectiveness in personalized T2I generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to create your own pictures by typing what you want the image to look like! This paper introduces a new way for computers and humans to work together to generate images. The system uses artificial intelligence and learning from people to make sure the generated images match what you want. It’s like having a partner that helps you bring your ideas to life. The researchers tested their system with real people and found it was much better than previous methods. They also shared some of the data they used so others can build on this idea. |
Keywords
» Artificial intelligence » Language model » Prompt » Reinforcement learning
