A Visually-Aware Food Analysis System for Diet Management

Hang Wu, Xi Chen, Xuelong Li, Haokai Ma, Yuze Zheng, Xiangxian Li, Xiangxu Meng, Lei Meng

Abstract: This demo illustrates a visually-aware food analysis (VAFA) system for socially-engaged diet management. VAFA is able to receive multimedia inputs, such as the images of food with/without a description to record a user’s daily diet. Such information will be passed to AI algorithms for food classification, ingredient recognition, and nutrition analysis, to produce a nutrition report for the user. Moreover, VAFA profiles the users’ eating habits to make personalized recipe recommendation and identify the social communities with similar eating preferences. VAFA is empowered by state-of-the-art AI algorithms and a large-scale dataset with 300K users, 400K recipes, and over 10M user-recipe interactions.