通过评估对不配对图像标题的语义重要性来挖掘核心信息
Jiahui Wei1, Zhixin Li1, Canlong Zhang1
1Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004, China; Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin 541004, China.
概括
本研究介绍了通过评估语义重要性 (MCIESI) 来挖掘核心信息,用于未配对的图像标题. MCIESI有效地挖掘并将核心图像信息生成为类似人类的句子,克服数据限制.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 监督的图像标题需要大量的手动注释,这是昂贵和耗时的.
- 获得配对的图像注释数据的挑战需要用于未配对的图像标题的方法.
研究的目的:
- 开发一种用于未配对图像标题的新方法,可以生成语义上相关和语法上正确的标题.
- 通过专注于挖掘和体现核心图像信息来解决现有方法的局限性.
主要方法:
- 利用场景图表来表示图像语义,并评估对象/交互的重要性,以挖掘核心信息.
- 采用语义约束来指导基于挖掘的图像信息的句子生成.
- 通过对抗性训练将语法约束和使用三重损失的相对约束纳入.
主要成果:
- 拟议的通过评估语义重要性 (MCIESI) 挖掘核心信息的方法在未配对的图像标题中表现出有效性.
- 生成的标题在语义上是可信的,在语法上是正确的,与人类的认知过程保持一致.
结论:
- MCIESI成功地挖掘了基本的图像内容,并将其转化为连贯的,类似人类的描述,没有配对数据.
- 该方法为在数据稀缺的情况下生成高质量的图像字幕提供了一个有希望的解决方案.
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