# Long-tailed Food Classification arXiv: https://arxiv.org/abs/2210.14748 ยท HTML: https://mlsanity.com/paper/2210.14748/ ## Key facts - Cited by 19 papers in the map (data through 14 Oct 2026). - Builds on 21 of its 44 references that are in the map. - First posted on arXiv 26 Oct 2022. ## Builds on (most cited first, 21 of 21) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015) - [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014) - [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019) - [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019) - [Large-Scale Long-Tailed Recognition in an Open World](https://mlsanity.com/paper/1904.05160/) (2019) - [Long-tail learning via logit adjustment](https://mlsanity.com/paper/2007.07314/) (2020) - [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017) - [Balanced Meta-Softmax for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2007.10740/) (2020) - [The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed Classification](https://mlsanity.com/paper/2112.00412/) (2021) - [Influence-Balanced Loss for Imbalanced Visual Classification](https://mlsanity.com/paper/2110.02444/) (2021) - [Large Scale Visual Food Recognition](https://mlsanity.com/paper/2103.16107/) (2021) - [Incremental Learning In Online Scenario](https://mlsanity.com/paper/2003.13191/) (2020) - [Online Continual Learning For Visual Food Classification](https://mlsanity.com/paper/2108.06781/) (2021) - [Visual Aware Hierarchy Based Food Recognition](https://mlsanity.com/paper/2012.03368/) (2020) - [Multi-Task Image-Based Dietary Assessment for Food Recognition and Portion Size Estimation](https://mlsanity.com/paper/2004.13188/) (2020) - [An End-to-End Food Image Analysis System](https://mlsanity.com/paper/2102.00645/) (2021) - [An Integrated System for Mobile Image-Based Dietary Assessment](https://mlsanity.com/paper/2110.01754/) (2021) - [Exemplar-free Online Continual Learning](https://mlsanity.com/paper/2202.05491/) (2022) - [Improving Dietary Assessment Via Integrated Hierarchy Food Classification](https://mlsanity.com/paper/2109.02736/) (2021) - [Towards the Creation of a Nutrition and Food Group Based Image Database](https://mlsanity.com/paper/2206.02086/) (2022) ## Built on by (most cited first, 19 of 19) - [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) - [Food Portion Estimation via 3D Object Scaling](https://mlsanity.com/paper/2404.12257/) (2024) - [Single-Stage Heavy-Tailed Food Classification](https://mlsanity.com/paper/2307.00182/) (2023) - [MetaFood CVPR 2024 Challenge on Physically Informed 3D Food Reconstruction: Methods and Results](https://mlsanity.com/paper/2407.09285/) (2024) - [MFP3D: Monocular Food Portion Estimation Leveraging 3D Point Clouds](https://mlsanity.com/paper/2411.10492/) (2024) - [DELTA: Decoupling Long-Tailed Online Continual Learning](https://mlsanity.com/paper/2404.04476/) (2024) - [An Improved Encoder-Decoder Framework for Food Energy Estimation](https://mlsanity.com/paper/2309.00468/) (2023) - [Diffusion Model with Clustering-based Conditioning for Food Image Generation](https://mlsanity.com/paper/2309.00199/) (2023) - [Muti-Stage Hierarchical Food Classification](https://mlsanity.com/paper/2309.01075/) (2023) - [Personalized Food Image Classification: Benchmark Datasets and New Baseline](https://mlsanity.com/paper/2309.08744/) (2023) - [Automatic Recognition of Food Ingestion Environment from the AIM-2 Wearable Sensor](https://mlsanity.com/paper/2405.07827/) (2024) - [Conditional Synthetic Food Image Generation](https://mlsanity.com/paper/2303.09005/) (2023) - [Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification](https://mlsanity.com/paper/2506.01368/) (2025) - [Comprehensive Evaluation of Large Multimodal Models for Nutrition Analysis: A New Benchmark Enriched with Contextual Metadata](https://mlsanity.com/paper/2507.07048/) (2025) - [Self-Supervised Visual Representation Learning on Food Images](https://mlsanity.com/paper/2303.09046/) (2023) - [Learning to Classify New Foods Incrementally Via Compressed Exemplars](https://mlsanity.com/paper/2404.07507/) (2024) - [FMiFood: Multi-modal Contrastive Learning for Food Image Classification](https://mlsanity.com/paper/2408.03922/) (2024) - [Dual-Imbalance Continual Learning for Real-World Food Recognition](https://mlsanity.com/paper/2603.29133/) (2026) ## Abstract (arXiv) Food classification serves as the basic step of image-based dietary assessment to predict the types of foods in each input image. However, food image predictions in a real world scenario are usually long-tail distributed among different food classes, which cause heavy class-imbalance problems and a restricted performance. In addition, none of the existing long-tailed classification methods focus on food data, which can be more challenging due to the lower inter-class and higher intra-class similarity among foods. In this work, we first introduce two new benchmark datasets for long-tailed food classification including Food101-LT and VFN-LT where the number of samples in VFN-LT exhibits the real world long-tailed food distribution. Then we propose a novel 2-Phase framework to address the problem of class-imbalance by (1) undersampling the head classes to remove redundant samples along with maintaining the learned information through knowledge distillation, and (2) oversampling the tail classes by performing visual-aware data augmentation. We show the effectiveness of our method by comparing with existing state-of-the-art long-tailed classification methods and show improved performance on both Food101-LT and VFN-LT benchmarks. The results demonstrate the potential to apply our method to related real life applications.