# Personalized Class Incremental Context-Aware Food Classification for Food Intake Monitoring Systems

arXiv: https://arxiv.org/abs/2503.06647 · HTML: https://mlsanity.com/paper/2503.06647/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 41 references that are in the map.
- First posted on arXiv 9 Mar 2025.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines](https://mlsanity.com/paper/1810.12488/) (2018)
- [Reinforced Continual Learning](https://mlsanity.com/paper/1805.12369/) (2018)
- [Large Scale Visual Food Recognition](https://mlsanity.com/paper/2103.16107/) (2021)
- [Continual Learning with Extended Kronecker-factored Approximate Curvature](https://mlsanity.com/paper/2004.07507/) (2020)
- [Visual Aware Hierarchy Based Food Recognition](https://mlsanity.com/paper/2012.03368/) (2020)
- [Long-Tailed Continual Learning For Visual Food Recognition](https://mlsanity.com/paper/2307.00183/) (2023)
- [Online Class-Incremental Learning For Real-World Food Image Classification](https://mlsanity.com/paper/2301.05246/) (2023)
- [Single-Stage Heavy-Tailed Food Classification](https://mlsanity.com/paper/2307.00182/) (2023)
- [Personalized Food Image Classification: Benchmark Datasets and New Baseline](https://mlsanity.com/paper/2309.08744/) (2023)
- [Bias Mitigating Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2402.00481/) (2024)

## Abstract (arXiv)

Accurate food intake monitoring is crucial for maintaining a healthy diet and preventing nutrition-related diseases. With the diverse range of foods consumed across various cultures, classic food classification models have limitations due to their reliance on fixed-sized food datasets. Studies show that people consume only a small range of foods across the existing ones, each consuming a unique set of foods. Existing class-incremental models have low accuracy for the new classes and lack personalization. This paper introduces a personalized, class-incremental food classification model designed to overcome these challenges and improve the performance of food intake monitoring systems. Our approach adapts itself to the new array of food classes, maintaining applicability and accuracy, both for new and existing classes by using personalization. Our model's primary focus is personalization, which improves classification accuracy by prioritizing a subset of foods based on an individual's eating habits, including meal frequency, times, and locations. A modified version of DSN is utilized to expand on the appearance of new food classes. Additionally, we propose a comprehensive framework that integrates this model into a food intake monitoring system. This system analyzes meal images provided by users, makes use of a smart scale to estimate food weight, utilizes a nutrient content database to calculate the amount of each macro-nutrient, and creates a dietary user profile through a mobile application. Finally, experimental evaluations on two new benchmark datasets FOOD101-Personal and VFN-Personal, personalized versions of well-known datasets for food classification, are conducted to demonstrate the effectiveness of our model in improving the classification accuracy of both new and existing classes, addressing the limitations of both conventional and class-incremental food classification models.