Advancing Food Nutrition Estimation via Visual-Ingredient Feature Fusion

Huiyan Qi, Bin Zhu, Chong-Wah Ngo, Jingjing Chen, Ee-Peng Lim·May 13, 2025

Summary

FastFood dataset, featuring 84,446 images across 908 categories, aids nutrition estimation in fast food. VIF2, a model-agnostic method, integrates visual and ingredient features for improved predictions. Ingredient robustness is enhanced through training strategies, and an ingredient-aware fusion module combines features for accuracy. Experiments on FastFood and Nutrition5k datasets validate the method's effectiveness across different backbones, emphasizing ingredient information's importance.

Introduction
Background
Overview of the FastFood dataset
Importance of nutrition estimation in fast food
Objective
Aim of using the FastFood dataset and VIF2 model
Focus on improving nutrition estimation accuracy
Method
Data Collection
Description of the FastFood dataset
Process of collecting 84,446 images across 908 categories
Data Preprocessing
Techniques for preparing the dataset for model training
Model Integration
Explanation of the VIF2 model
How it integrates visual and ingredient features
Training Strategies
Methods for enhancing ingredient robustness
Importance of ingredient information in nutrition estimation
Ingredient-Aware Fusion Module
Description of the module
How it combines features for improved accuracy
Experiments
Dataset Utilization
Application of the FastFood dataset
Use of the Nutrition5k dataset for validation
Backbone Evaluation
Testing across different model architectures
Importance of backbone selection in model performance
Results Analysis
Validation of the method's effectiveness
Insights into ingredient information's impact on nutrition estimation
Conclusion
Summary of Findings
Recap of the research outcomes
Future Directions
Potential areas for further investigation
Impact and Applications
Real-world implications of the research
Opportunities for integrating the method into existing systems
Basic info
papers
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
What training strategies are used to enhance ingredient robustness in the VIF2 method?
What role does the ingredient-aware fusion module play in improving prediction accuracy?
How does the VIF2 method perform across different backbones on the FastFood and Nutrition5k datasets?
How does the VIF2 method integrate visual and ingredient features for improved predictions?