相关实验视频
通过以注意为导向的图像编辑来缓解文本到图像生成中的不适当概念
Jiyeon Oh1, Jae-Yeop Jeong1, Yeong-Gi Hong1
1Department of Data Science, Seoul National University of Science and Technology, Seoul, Republic of South Korea.
PeerJ. Computer science
|September 24, 2025
概括
本研究引入了一种新的方法,使用注意力图来减少文本到图像生成中的不适当内容. 该方法有效地过有害输出,同时保持图像质量和效率.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 文本到图像生成模型正在快速发展,从文本提示程序创建多样化的视觉效果.
- 对于这些模型生成不适当,冒犯性或明确内容存在担忧.
研究的目的:
- 开发一种方法,在文本到图像生成过程中选择性地抑制不适当的概念.
- 在不损害图像完整性或计算效率的情况下解决安全问题.
主要方法:
- 利用注意力地图来识别和抑制不适当的概念.
- 通过定量评估和人类感知研究来评估方法.
- 专注于一个简单,有效的方法,没有额外的模型培训.
主要成果:
- 证明有效地减少不适当的内容.
- 保存了原始图像的完整性和上下文.
- 与现有方法相比,实现了高计算效率.
结论:
- 提出的基于注意力图的方法提供了一种有效和高效的解决方案,用于减轻生成模型中不适当的内容.
- 该技术保持图像质量和上下文,不需要额外的培训或重大工程工作.
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