用双暗示问问题:视觉问题生成与答案意识和区域参考
概括
本研究引入了一种新的视觉问题生成 (VQG) 方法,使用双线索 (文本答案和视觉区域) 来从图像中创建更相关和有意义的问题. 该方法有效地解决了现有的VQG模型中的局限性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 视觉问题生成 (VQG) 旨在从图像中创建类似人类的问题.
- 现有的VQG方法在一对多问题映射问题上扎,并且无法建模复杂的对象关系或侧信息交互.
研究的目的:
- 为VQG开发一种新的学习范式,产生答案意识和区域参考问题.
- 解决现有的VQG方法的局限性,特别是一对多映射问题和视觉关系的不充分建模.
主要方法:
- 提出了一种新的学习模式,使用"双暗示":文本答案和感兴趣的视觉区域.
- 在没有额外的人类注释的情况下开发了视觉提示的自学方法.
- 引入了一种双线索引导的图形对序列学习框架,以作为动态图形来建模对象关系并生成问题.
主要成果:
- 拟议的方法有效地减轻了VQG.中的一对多映射问题.
- 图表到序列模型成功地捕捉了视觉对象之间的复杂关系.
- 实验结果表明,拟议的方法优于现有方法.
结论:
- 新的学习范式和图形到序列框架显著改善了视觉问题生成.
- "双暗示"方法提高了问题的相关性和参考性.
- 该方法提供了一种更有效的方式来建模复杂的视觉场景,用于生成问题.
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