Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2024

Abstract

Current research in food analysis primarily concentrates on tasks such as food recognition, recipe retrieval and nutrition estimation from a single image. Nevertheless, there is a significant gap in exploring the impact of food intake on physiological indicators (e.g., weight) over time. This paper addresses this gap by introducing the DietDiary dataset, which encompasses daily dietary diaries and corresponding weight measurements of real users. Furthermore, we propose a novel task of weight prediction with a dietary diary that aims to leverage historical food intake and weight to predict future weights. To tackle this task, we propose a model-agnostic time series forecasting framework. Specifically, we introduce a Unified Meal Representation Learning (UMRL) module to extract representations for each meal. Additionally, we design a diet-aware loss function to associate food intake with weight variations. By conducting experiments on the DietDiary dataset with two state-of-the-art time series forecasting models, NLinear and iTransformer, we demonstrate that our proposed framework achieves superior performance compared to the original models. We make our dataset, code, and models publicly available at: https://yxg1005.github.io/weight-prediction.

Keywords

Weight prediction, food analysis, time series forecasting models

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

MM '24: Proceedings of the 32nd ACM International Conference on Multimedia, Melbourne, Australia, October 28 - November 1

First Page

127

Last Page

136

ISBN

9798400706868

Identifier

10.1145/3664647.3680977

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3664647.3680977

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