Abstract
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.
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Acknowledgments
This work is supported in part by the Excellent Youth Scholars Program of Shandong Province (Grant no. 2022HWYQ-048) and the Oversea Innovation Team Project of the “20 Regulations for New Universities” funding program of Jinan (Grant no. 2021GXRC073)
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Wu, H. et al. (2022). VAFA: A Visually-Aware Food Analysis System for Socially-Engaged Diet Management. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13606. Springer, Cham. https://doi.org/10.1007/978-3-031-20503-3_48
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