Summary of Capability-aware Prompt Reformulation Learning For Text-to-image Generation, by Jingtao Zhan et al.
Capability-aware Prompt Reformulation Learning for Text-to-Image Generation
by Jingtao Zhan, Qingyao Ai, Yiqun Liu, Jia Chen, Shaoping Ma
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
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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 This paper addresses the challenge of prompt crafting in text-to-image generation systems by developing an automatic prompt reformulation model using user reformulation data from interaction logs. The analysis reveals significant variance in the quality of reformulation pairs, dependent on individual user capability. To effectively use this data for training, the Capability-aware Prompt Reformulation (CAPR) framework is introduced. CAPR integrates user capability into the reformulation process through two key components: the Conditional Reformulation Model (CRM) and Configurable Capability Features (CCF). CRM reformulates prompts according to a specified user capability, as represented by CCF. This enables CAPR to effectively learn diverse reformulation strategies across various user capacities and simulate high-capability user reformulation during inference. The paper showcases CAPR’s superior performance over existing baselines on standard text-to-image generation benchmarks and its robustness on unseen systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make it easier for people to create artwork using computer programs. These programs can turn words into pictures, but they need good instructions (called prompts) to do a great job. The problem is that not everyone knows how to write good prompts. To fix this, the researchers developed a special tool called CAPR. This tool uses data from when people use these programs to learn how to make better prompts. It can even pretend to be someone who is really good at making prompts! This means that more people can create amazing artwork using these programs. |
Keywords
» Artificial intelligence » Image generation » Inference » Prompt
