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/.

翻译：当前食品分析研究主要集中于从单张图像进行食物识别、食谱检索和营养估算等任务。然而，在探索食物摄入随时间对生理指标（如体重）的影响方面仍存在显著空白。本文通过引入DietDiary数据集填补了这一空白，该数据集包含真实用户的每日饮食日记及对应体重测量数据。此外，我们提出了一项新颖的基于饮食日记的体重预测任务，旨在利用历史食物摄入和体重数据预测未来体重。针对该任务，我们提出了一个模型无关的时间序列预测框架。具体而言，我们引入了统一膳食表征学习（UMRL）模块来提取每餐的表示特征。同时，我们设计了饮食感知损失函数以关联食物摄入与体重变化。通过在DietDiary数据集上使用NLinear和iTransformer两种先进时间序列预测模型进行实验，我们证明了所提框架相比原始模型实现了更优性能。我们的数据集、代码和模型已在以下网址公开发布：https://yxg1005.github.io/weight-prediction/。