概括
本研究介绍了视觉推理的知识嵌入式相互指导,整合视觉,语言和知识图. 这种新的方法显著提高了视觉关系检测任务的性能.
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:
- 整合视觉和语言的视觉推理是一个重大挑战.
- 现有的方法通常会单独分析图像和问题,或者平整知识图,忽略关键的结构信息.
- 当前的模型忽略了视觉和听觉/口语在现实世界的相互联系.
研究的目的:
- 开发一种新的视觉推理框架,共同考虑视觉,语言和知识图.
- 通过结合知识图的结构和模式之间的相互作用来解决现有方法的局限性.
- 通过知识嵌入式的相互指导方法来增强视觉关系检测.
主要方法:
- 提出了一个名为知识嵌入式相互指导的一般联合代表学习框架.
- 启用视觉数据和自然语言描述之间的相互指导.
- 促进知识图和推理模型之间的相互指导.
- 利用从推理模型中获得的知识来增强用于视觉关系检测的知识图.
主要成果:
- 拟议的方法在两个视觉推理基准上明显优于最先进的方法.
- 证明了从视觉,语言和知识图表中共同学习表示的有效性.
- 在视觉关系检测任务中表现出更好的性能.
结论:
- 嵌入式知识相互指导框架为视觉推理提供了更全面的方法.
- 从知识图中整合结构化知识可以增强视觉推理模型的功能.
- 该方法有效地弥合了视觉感知,语言理解和结构化知识之间的差距.
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