图像-文本连贯性及其对多式联络AI的影响
Malihe Alikhani1, Baber Khalid2, Matthew Stone2
1Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, United States.
Frontiers in artificial intelligence
|May 31, 2023
概括
本研究介绍了在AI中模拟图像-文本呈现的连贯关系. 这种方法为理解多式联络人工智能系统中的沟通目标提供了一个强大的框架.
科学领域:
- 人工智能的人工智能
- 计算语言学 计算语言学
- 人与计算机的交互
背景情况:
- 人类的沟通经常整合图像和文本,特别是在在线环境中.
- 现有的图像文本分析框架往往侧重于信息重叠或相关性.
- 凝聚力理论通过检查话语单元之间的务实关系,提供了另一种观点.
研究的目的:
- 为了证明图像-文本连贯关系的实用性,以建模人工智能演示的务实方面.
- 将连贯理论作为人工智能系统中沟通目标的强有力的和有效的抽象形式呈现.
- 在图像-文本数据分析和人工智能任务中探索连贯性的应用.
主要方法:
- 审查图像-文本数据集一致性的案例研究.
- 从有限的注释中研究连贯性预测 (少量学习).
- 开发AI任务的连贯性模型,如图像标题生成和评估.
主要成果:
- 凝聚力理论为沟通目标提供了简化和有效的抽象.
- 该框架适用于各种图像-文本任务,包括标题和评估.
- 从最小的数据可以预测一致性关系,从而提高AI模型的效率.
结论:
- 图像-文本连贯关系为理解和建模人工智能的多式联络提供了强大的工具.
- 这种方法增强了处理集成图像和文本的AI系统的务实能力.
- 凝聚力理论为开发更复杂的AI通信系统提供了一个可扩展和有效的方法.
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