具体性
计算机科学
生成语法
生成模型
表达式(计算机科学)
自然语言处理
人工智能
图像编辑
名词
特征(语言学)
图像(数学)
认知心理学
心理学
语言学
程序设计语言
哲学
作者
Yunlong Wang,Shuyuan Shen,Brian Y. Lim
标识
DOI:10.1145/3544548.3581402
摘要
Generative AI models have shown impressive ability to produce images with text prompts, which could benefit creativity in visual art creation and self-expression. However, it is unclear how precisely the generated images express contexts and emotions from the input texts. We explored the emotional expressiveness of AI-generated images and developed RePrompt, an automatic method to refine text prompts toward precise expression of the generated images. Inspired by crowdsourced editing strategies, we curated intuitive text features, such as the number and concreteness of nouns, and trained a proxy model to analyze the feature effects on the AI-generated image. With model explanations of the proxy model, we curated a rubric to adjust text prompts to optimize image generation for precise emotion expression. We conducted simulation and user studies, which showed that RePrompt significantly improves the emotional expressiveness of AI-generated images, especially for negative emotions.
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