# Advancing Food Nutrition Estimation via Visual-Ingredient Feature Fusion

arXiv: https://arxiv.org/abs/2505.08747 · HTML: https://mlsanity.com/paper/2505.08747/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 46 references that are in the map.
- First posted on arXiv 13 May 2025.

## Builds on (most cited first, 21 of 21)

- [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)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [LLaVA-Video: Video Instruction Tuning With Synthetic Data](https://mlsanity.com/paper/2410.02713/) (2024)
- [Hallucination is Inevitable: An Innate Limitation of Large Language Models](https://mlsanity.com/paper/2401.11817/) (2024)
- [A Survey on Food Computing](https://mlsanity.com/paper/1808.07202/) (2018)
- [Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food](https://mlsanity.com/paper/2103.03375/) (2021)
- [ISIA Food-500: A Dataset for Large-Scale Food Recognition via Stacked Global-Local Attention Network](https://mlsanity.com/paper/2008.05655/) (2020)
- [A Large-Scale Benchmark for Food Image Segmentation](https://mlsanity.com/paper/2105.05409/) (2021)
- [FoodLMM: A Versatile Food Assistant using Large Multi-modal Model](https://mlsanity.com/paper/2312.14991/) (2023)
- [Res-VMamba: Fine-Grained Food Category Visual Classification Using Selective State Space Models with Deep Residual Learning](https://mlsanity.com/paper/2402.15761/) (2024)
- [FIRE: Food Image to REcipe generation](https://mlsanity.com/paper/2308.14391/) (2023)
- [RoDE: Linear Rectified Mixture of Diverse Experts for Food Large Multi-Modal Models](https://mlsanity.com/paper/2407.12730/) (2024)
- [Image Based Food Energy Estimation With Depth Domain Adaptation](https://mlsanity.com/paper/2208.12153/) (2022)
- [DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion](https://mlsanity.com/paper/2310.11702/) (2023)
- [Enhancing Recipe Retrieval with Foundation Models: A Data Augmentation Perspective](https://mlsanity.com/paper/2312.04763/) (2023)
- [Navigating Weight Prediction with Diet Diary](https://mlsanity.com/paper/2408.05445/) (2024)
- [Cross-lingual Adaptation for Recipe Retrieval with Mixup](https://mlsanity.com/paper/2205.03891/) (2022)
- [Monte Carlo Tree Search for Recipe Generation using GPT-2](https://mlsanity.com/paper/2401.05199/) (2024)

## Built on by (most cited first, 5 of 5)

- [OmniFood8K: Single-Image Nutrition Estimation via Hierarchical Frequency-Aligned Fusion](https://mlsanity.com/paper/2604.12356/) (2026)
- [Dual-LoRA and Quality-Enhanced Pseudo Replay for Multimodal Continual Food Learning](https://mlsanity.com/paper/2511.13351/) (2025)
- [Open-KNEAD: Knowledge-grounded Nutrition Estimation via Agentic Decomposition](https://mlsanity.com/paper/2607.12911/) (2026)
- [ReGraph: Learning to Generate Recipe Graphs from Food Images](https://mlsanity.com/paper/2608.06917/) (2026)
- [NutriBench-Kitchen: Benchmarking Embodied AI for Nutrition Management](https://mlsanity.com/paper/2609.07135/) (2026)

## Abstract (arXiv)

Nutrition estimation is an important component of promoting healthy eating and mitigating diet-related health risks. Despite advances in tasks such as food classification and ingredient recognition, progress in nutrition estimation is limited due to the lack of datasets with nutritional annotations. To address this issue, we introduce FastFood, a dataset with 84,446 images across 908 fast food categories, featuring ingredient and nutritional annotations. In addition, we propose a new model-agnostic Visual-Ingredient Feature Fusion (VIF2) method to enhance nutrition estimation by integrating visual and ingredient features. Ingredient robustness is improved through synonym replacement and resampling strategies during training. The ingredient-aware visual feature fusion module combines ingredient features and visual representation to achieve accurate nutritional prediction. During testing, ingredient predictions are refined using large multimodal models by data augmentation and majority voting. Our experiments on both FastFood and Nutrition5k datasets validate the effectiveness of our proposed method built in different backbones (e.g., Resnet, InceptionV3 and ViT), which demonstrates the importance of ingredient information in nutrition estimation. https://huiyanqi.github.io/fastfood-nutrition-estimation/.