一项关于学习对象关系的调查,用于图像标题
Du Runyan1,2,3,4, Zhang Wenkai1,2,3,4, Guo Zhi1,2,3,4
1Aerospace Information Research Institute, Chinese Academy Sciences, Beijing, China.
Computational intelligence and neuroscience
|June 7, 2023
概括
本研究回顾了将对象关系纳入图像标题的方法. 了解这些关系可以改善图像描述,增强计算机视觉和自然语言处理模型.
科学领域:
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
- 人工智能的人工智能
背景情况:
- 图像标题是一个复杂的任务,涉及将视觉信息转化为文本描述.
- 整合图像对象之间的关系信息显著提高了标题质量和可读性.
- 现有的研究重点是关系挖掘和学习,以改善图像标题.
研究的目的:
- 在图像标题中总结关系表示和编码方法.
- 分析各种关系方法的优缺点.
- 识别常见的数据集和关系图像标题的当前挑战.
主要方法:
- 对关系式表示技术的审查.
- 对关系编码策略的分析.
- 讨论用于关系式标题的常用数据集.
主要成果:
- 确定关系表示和编码的关键方法.
- 评估不同方法的优缺点.
- 编制与任务相关的数据集.
结论:
- 关系信息对于先进的图像标题至关重要.
- 需要进一步的研究来解决关系图像标题的当前挑战.
- 本综述为该领域的研究人员提供了全面的概述.
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