# Navigating Weight Prediction with Diet Diary

arXiv: https://arxiv.org/abs/2408.05445 · HTML: https://mlsanity.com/paper/2408.05445/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2024.

## Built on by (most cited first, 7 of 7)

- [Advancing Food Nutrition Estimation via Visual-Ingredient Feature Fusion](https://mlsanity.com/paper/2505.08747/) (2025)
- [Retrieval Augmented Recipe Generation](https://mlsanity.com/paper/2411.08715/) (2024)
- [Enhancing Action and Ingredient Modeling for Semantically Grounded Recipe Generation](https://mlsanity.com/paper/2602.15862/) (2026)
- [OmniFood8K: Single-Image Nutrition Estimation via Hierarchical Frequency-Aligned Fusion](https://mlsanity.com/paper/2604.12356/) (2026)
- [CookAnything: A Framework for Flexible and Consistent Multi-Step Recipe Image Generation](https://mlsanity.com/paper/2512.03540/) (2025)
- [Pushing the Limits of Distillation-Based Continual Learning via Classifier-Proximal Lightweight Plugins](https://mlsanity.com/paper/2512.03537/) (2025)
- [ReGraph: Learning to Generate Recipe Graphs from Food Images](https://mlsanity.com/paper/2608.06917/) (2026)

## Abstract (arXiv)

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/.