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Ki-Cook:通过知识注入的学习,聚合多式调理表现
Revathy Venkataramanan1, Swati Padhee2, Saini Rohan Rao3
1Department of Computer Science, Artificial Intelligence Research Institute, University of South Carolina, Columbia, SC, United States.
Frontiers in big data
|August 9, 2023
概括
这项研究介绍了Ki-Cook,这是一个新的网络,用于用成分和标题等详细信息对食谱进行聚类,改善食物图像检索和理解食谱相似性,特别是对于罕见的成分.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 交叉模式的食谱检索可以实现图像到文本和文本到图像的搜索.
- 目前的方法基于类名的集群配方,由于类内部的变化和类之间的相似性,这是不够的.
- 详细的配方信息,包括标题和成分,为相似性确定提供了更强大的基础.
研究的目的:
- 开发一种用于集群配方表示的新方法,以捕捉更深层次的配方相似性.
- 提高对未知的食物图像检索相关信息的准确性.
- 通过专注于食材,特别是罕见的食材,提高对配方关系的理解.
主要方法:
- 提出了一个知识透的多式联网代理学习网络,Ki-Cook.
- 将食谱标题,食材 (重点是稀有食材) 和动作纳入表现学习中.
- 在网络中使用成分图像来学习多式调理表示.
- 在过程的程序属性上构建了网络.
主要成果:
- 与基线模型相比,Ki-Cook模型在成分检索任务中显示了基本事实覆盖率的12%的改善,以及与基线模型相比,十字路口对欧盟的10%的改善.
- 学习的表征平均含有比基线模型多15.33%的稀有成分.
- 在隐藏空间中对类似食谱进行集群方面取得了39%的改进,Fleiss kappa得分为0.35,用于注释者间的协议.
结论:
- 利用包括稀有成分在内的全面的配方细节,显著提高了配方相似性聚类和检索.
- Ki-Cook网络提供了一种更有效的方法来学习多式联调配方表示.
- 这项工作代表了从视觉和文本数据中理解和检索食谱信息的重大进步.
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