Summary of Chatgen: Automatic Text-to-image Generation From Freestyle Chatting, by Chengyou Jia et al.
ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting
by Chengyou Jia, Changliang Xia, Zhuohang Dang, Weijia Wu, Hangwei Qian, Minnan Luo
First submitted to arxiv on: 26 Nov 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 proposed Automatic T2I generation system aims to simplify the text-to-image generation process by automating tedious steps such as crafting prompts, selecting models, and configuring arguments. This is achieved through a multi-stage evolution strategy called ChatGen-Evo, which progressively equips models with essential automation skills. The system’s performance is evaluated across step-wise accuracy and image quality, showing significant enhancements over various baselines. Additionally, the paper introduces ChatGenBench, a novel benchmark designed for Automatic T2I, featuring high-quality paired data with diverse freestyle inputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automatic T2I generation is a complex task that requires users to describe their needs in a freestyle chatting way. The proposed system automates tedious steps like crafting prompts and selecting models, making it easier for users to get the desired images. It uses a multi-stage evolution strategy called ChatGen-Evo, which enhances performance over various baselines. The paper also introduces a new benchmark, ChatGenBench, which has high-quality paired data with diverse inputs. |
Keywords
» Artificial intelligence » Image generation
