# Simultaneous Food Localization and Recognition

arXiv: https://arxiv.org/abs/1604.07953 · HTML: https://mlsanity.com/paper/1604.07953/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2016.

## Built on by (most cited first, 14 of 14)

- [Large Scale Visual Food Recognition](https://mlsanity.com/paper/2103.16107/) (2021)
- [Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food](https://mlsanity.com/paper/2103.03375/) (2021)
- [An End-to-End Food Image Analysis System](https://mlsanity.com/paper/2102.00645/) (2021)
- [Long-Tailed Continual Learning For Visual Food Recognition](https://mlsanity.com/paper/2307.00183/) (2023)
- [Improving Dietary Assessment Via Integrated Hierarchy Food Classification](https://mlsanity.com/paper/2109.02736/) (2021)
- [A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment](https://mlsanity.com/paper/2106.11776/) (2021)
- [Learn More for Food Recognition via Progressive Self-Distillation](https://mlsanity.com/paper/2303.05073/) (2023)
- [Deep Learning based Food Instance Segmentation using Synthetic Data](https://mlsanity.com/paper/2107.07191/) (2021)
- [Object Detection in Indian Food Platters using Transfer Learning with YOLOv4](https://mlsanity.com/paper/2205.04841/) (2022)
- [NutritionVerse: Empirical Study of Various Dietary Intake Estimation Approaches](https://mlsanity.com/paper/2309.07704/) (2023)
- [NutritionVerse-Direct: Exploring Deep Neural Networks for Multitask Nutrition Prediction from Food Images](https://mlsanity.com/paper/2405.07814/) (2024)
- [Dish detection in food platters: A framework for automated diet logging and nutrition management](https://mlsanity.com/paper/2305.07552/) (2023)
- [Grad-CAM for Visualizing Attention Regions of PCA and SVM Layers in Convolutional Neural Networks](https://mlsanity.com/paper/2508.11880/) (2025)
- [Food-R1: A Unified Multi-Task Food Vision-Language Model with Reinforcement Learning](https://mlsanity.com/paper/2606.04986/) (2026)

## Abstract (arXiv)

The development of automatic nutrition diaries, which would allow to keep track objectively of everything we eat, could enable a whole new world of possibilities for people concerned about their nutrition patterns. With this purpose, in this paper we propose the first method for simultaneous food localization and recognition. Our method is based on two main steps, which consist in, first, produce a food activation map on the input image (i.e. heat map of probabilities) for generating bounding boxes proposals and, second, recognize each of the food types or food-related objects present in each bounding box. We demonstrate that our proposal, compared to the most similar problem nowadays - object localization, is able to obtain high precision and reasonable recall levels with only a few bounding boxes. Furthermore, we show that it is applicable to both conventional and egocentric images.