# Mining Discriminative Food Regions for Accurate Food Recognition

arXiv: https://arxiv.org/abs/2207.03692 · HTML: https://mlsanity.com/paper/2207.03692/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 8 Jul 2022.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Deep Layer Aggregation](https://mlsanity.com/paper/1707.06484/) (2017)
- [Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization](https://mlsanity.com/paper/1704.04232/) (2017)
- [Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach](https://mlsanity.com/paper/1703.08448/) (2017)
- [Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning](https://mlsanity.com/paper/1806.06193/) (2018)
- [Adversarial Complementary Learning for Weakly Supervised Object Localization](https://mlsanity.com/paper/1804.06962/) (2018)
- [Two-Phase Learning for Weakly Supervised Object Localization](https://mlsanity.com/paper/1708.02108/) (2017)
- [DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment](https://mlsanity.com/paper/1606.05675/) (2016)
- [Wide-Slice Residual Networks for Food Recognition](https://mlsanity.com/paper/1612.06543/) (2016)
- [Leveraging Context to Support Automated Food Recognition in Restaurants](https://mlsanity.com/paper/1510.02078/) (2015)

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

- [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)
- [Long-Tailed Continual Learning For Visual Food Recognition](https://mlsanity.com/paper/2307.00183/) (2023)
- [RoDE: Linear Rectified Mixture of Diverse Experts for Food Large Multi-Modal Models](https://mlsanity.com/paper/2407.12730/) (2024)
- [Food-500 Cap: A Fine-Grained Food Caption Benchmark for Evaluating Vision-Language Models](https://mlsanity.com/paper/2308.03151/) (2023)
- [Dietary Assessment with Multimodal ChatGPT: A Systematic Analysis](https://mlsanity.com/paper/2312.08592/) (2023)
- [Leveraging Automatic Personalised Nutrition: Food Image Recognition Benchmark and Dataset based on Nutrition Taxonomy](https://mlsanity.com/paper/2211.07440/) (2022)
- [Learn More for Food Recognition via Progressive Self-Distillation](https://mlsanity.com/paper/2303.05073/) (2023)
- [CookingDiffusion: Cooking Procedural Image Generation with Stable Diffusion](https://mlsanity.com/paper/2501.09042/) (2025)
- [Differential Evolution Integrated Hybrid Deep Learning Model for Object Detection in Pre-made Dishes](https://mlsanity.com/paper/2412.20370/) (2024)
- [Khana: A Comprehensive Indian Cuisine Dataset](https://mlsanity.com/paper/2509.06006/) (2025)
- [Fraesormer: Learning Adaptive Sparse Transformer for Efficient Food Recognition](https://mlsanity.com/paper/2503.11995/) (2025)
- [LLMs-based Augmentation for Domain Adaptation in Long-tailed Food Datasets](https://mlsanity.com/paper/2511.16037/) (2025)
- [FoodLogAthl-218: Constructing a Real-World Food Image Dataset Using Dietary Management Applications](https://mlsanity.com/paper/2512.14574/) (2025)

## Abstract (arXiv)

Automatic food recognition is the very first step towards passive dietary monitoring. In this paper, we address the problem of food recognition by mining discriminative food regions. Taking inspiration from Adversarial Erasing, a strategy that progressively discovers discriminative object regions for weakly supervised semantic segmentation, we propose a novel network architecture in which a primary network maintains the base accuracy of classifying an input image, an auxiliary network adversarially mines discriminative food regions, and a region network classifies the resulting mined regions. The global (the original input image) and the local (the mined regions) representations are then integrated for the final prediction. The proposed architecture denoted as PAR-Net is end-to-end trainable, and highlights discriminative regions in an online fashion. In addition, we introduce a new fine-grained food dataset named as Sushi-50, which consists of 50 different sushi categories. Extensive experiments have been conducted to evaluate the proposed approach. On three food datasets chosen (Food-101, Vireo-172, and Sushi-50), our approach performs consistently and achieves state-of-the-art results (top-1 testing accuracy of 90.4%, 90.2%, 92.0%, respectively) compared with other existing approaches. Dataset and code are available at https://github.com/Jianing-Qiu/PARNet