Summary of X-prompt: Towards Universal In-context Image Generation in Auto-regressive Vision Language Foundation Models, by Zeyi Sun et al.
X-Prompt: Towards Universal In-Context Image Generation in Auto-Regressive Vision Language Foundation Models
by Zeyi Sun, Ziyang Chu, Pan Zhang, Tong Wu, Xiaoyi Dong, Yuhang Zang, Yuanjun Xiong, Dahua Lin, Jiaqi Wang
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
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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 Medium Difficulty summary: This paper introduces X-Prompt, a large-vision language model that leverages in-context learning for general image generation tasks. Building upon advancements in auto-regressive vision-language models, X-Prompt is designed to deliver competitive performance across various seen and unseen image generation tasks within a unified framework. The model incorporates a specialized design that efficiently compresses features from in-context examples, enabling longer token sequences and improved generalization capabilities. A unified training task for text and image prediction enables X-Prompt to handle general image generation with enhanced task awareness. The paper showcases extensive experiments validating the model’s performance across diverse seen tasks and its ability to generalize to previously unseen ones. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine a machine that can generate images based on just a few examples it sees. This is called in-context learning, and it’s really important for big language models like this one. Right now, we can use these models to create text-to-image combinations, but we don’t know how well they can do when generating images without any context at all. That’s what this paper tries to solve by introducing a new model called X-Prompt. It’s designed to be really good at generating different types of images, and it does this by learning from examples and compressing important details. The results are impressive, showing that X-Prompt can generate a wide range of images, even ones it’s never seen before. |
Keywords
» Artificial intelligence » Generalization » Image generation » Language model » Prompt » Token
