清晰度和对齐:一种有效的多模式对齐方法,用于跨模式的配方检索
Zhuoyang Zou1, Xinghui Zhu1, Qinying Zhu1
1College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China.
Foods (Basel, Switzerland)
|June 19, 2024
概括
这项研究引入了一种用于交叉模式食谱检索的新方法,该方法可以改善食物图像和食谱之间的语义对齐. 这种方法解决了食物图像的模糊性,提高了检索准确性,以获得更好的食品计算应用程序.
科学领域:
- 食品计算 食品计算
- 计算机视觉 计算机视觉 计算机视觉
- 信息检索 信息检索
背景情况:
- 跨模式的食谱检索在食品计算中至关重要.
- 现有的方法缺乏模式内对齐,阻碍语义理解.
- 食物形象的模糊性是一个关键的,被忽视的挑战.
研究的目的:
- 提出一种新的跨模态配方检索多模态对齐方法 (MMACMR).
- 通过考虑跨模态和内模态对齐来增强语义对齐.
- 为了解决在检索模型中食物图像模两可的问题.
主要方法:
- MMACMR通过相应的食谱来衡量模两可的食物图像相似性.
- 在成分和说明之间集成了一个交叉关注模块,以增强配方表示.
- 该方法共同优化了模式间和模式内对齐.
主要成果:
- 在Recipe1M数据集上进行的实验.
- 拟议的MMACMR方法显著超过了一些最先进的方法.
- 在用于跨模式检索的常用评估标准中表现得更好.
结论:
- MMACMR有效地增强了语义对齐,用于跨模式的配方检索.
- 该方法成功地解决了食品图像的模两可,改善了模型的融合.
- 这项工作在食品计算和食谱检索系统方面取得了重大进展.
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