Pic2Plate:一个视觉语言和检索增强的框架,用于个性化的食谱建议
Yosua Setyawan Soekamto1,2, Andreas Lim1, Leonard Christopher Limanjaya1
1Department of Computer Engineering, Dongseo University, Busan 47011, Republic of Korea.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
Pic2Plate使用先进的图像识别和人工智能,根据你拥有的成分建议个性化的食谱. 这个创新的系统通过提供上下文意识和可靠的配方建议来增强.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 个性化的配方建议对于健康饮食至关重要,但在成分识别和个性化方面往往缺乏准确性.
- 现有的系统难以整合用户偏好,饮食需求和实时成分可用性.
研究的目的:
- 引入Pic2Plate,这是使用视觉语言模型 (VLM) 和检索增强生成 (RAG) 进行个性化配方建议的新框架.
- 通过从图像中准确识别食材并提供量身定制的食谱建议,增强用户在方面的体验.
主要方法:
- 开发了Pic2Plate,集成VLM以图像为基础的成分检测和RAG以进行个性化食谱检索.
- 使用智能手机摄像头传感器进行可访问的成分捕获和GPT-4o进行成分识别.
- 通过用户调查和布拉德利-特里模型评估了使用精度,回忆,准确性和F1得分的成分检测以及配方相关性.
主要成果:
- 成分检测模块实现了高性能指标:精度 (0.83),回忆 (0.91),准确性 (0.77),和F1得分 (0.86).
- 在用户评估中,Pic2Plate的VLM和RAG组合显示出与其他模型相比,其配方相关性优越.
- 该系统有效地提供了背景感知,可靠和多样化的配方建议.
结论:
- Pic2Plate提供了一个可扩展和以用户为中心的方法来改变个性化的和食谱推系统.
- 图像识别和先进的人工智能模型的整合显著提高了食谱建议的准确性和相关性.
- 这一框架有可能使个性化营养更容易获得,更为日常用户实用.
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