VAFA: A Visually-Aware Food Analysis System for Socially-Engaged Diet Management

被引：0
|
作者：
Wu, Hang [1 ]
Chen, Xi [1 ]
Li, Xuelong [1 ]
Ma, Haokai [1 ]
Zheng, Yuze [1 ]
Li, Xiangxian [1 ]
Meng, Xiangxu [1 ]
Meng, Lei [1 ]
机构：
[1] Shandong Univ, Sch Software, Jinan, Peoples R China
来源：
ARTIFICIAL INTELLIGENCE, CICAI 2022, PT III | 2022年 / 13606卷
关键词：
Diet management; Classification; Recommendation; Community discovery;
D O I：
10.1007/978-3-031-20503-3_48
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this demo, we demonstrate a visually-aware food analysis (VAFA) system for socially-engaged diet management. VAFA is capable of receiving multimedia inputs, such as the images of food with/without a description to record a user's daily diet. A set of AI algorithms for food classification, ingredient identification, and nutritional analysis are provided with this information to produce a nutrition report for the user. Furthermore, by profiling users' eating habits, VAFA can recommend individualized recipes and detect social communities that share similar dietary appetites for them. With the support of state-of-the-art AI algorithms and a large-scale Chinese food dataset that includes 300K users, 400K recipes, and over 10M user-recipe interactions, VAFA has won several awards in China's national artificial intelligence competitions.
引用
收藏
页码：554 / 558
页数：5
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