# Automatic Recognition of Food Ingestion Environment from the AIM-2 Wearable Sensor

arXiv: https://arxiv.org/abs/2405.07827 · HTML: https://mlsanity.com/paper/2405.07827/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 55 references that are in the map.
- First posted on arXiv 13 May 2024.

## 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)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [Equalization Loss for Long-Tailed Object Recognition](https://mlsanity.com/paper/2003.05176/) (2020)
- [Long-tailed Recognition by Routing Diverse Distribution-Aware Experts](https://mlsanity.com/paper/2010.01809/) (2020)
- [What makes ImageNet good for transfer learning?](https://mlsanity.com/paper/1608.08614/) (2016)
- [M2m: Imbalanced Classification via Major-to-minor Translation](https://mlsanity.com/paper/2004.00431/) (2020)
- [Learning Fast Sample Re-weighting Without Reward Data](https://mlsanity.com/paper/2109.03216/) (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)
- [Long-Tailed Continual Learning For Visual Food Recognition](https://mlsanity.com/paper/2307.00183/) (2023)
- [An Integrated System for Mobile Image-Based Dietary Assessment](https://mlsanity.com/paper/2110.01754/) (2021)
- [Long-tailed Food Classification](https://mlsanity.com/paper/2210.14748/) (2022)
- [An End-to-end Food Portion Estimation Framework Based on Shape Reconstruction from Monocular Image](https://mlsanity.com/paper/2308.01810/) (2023)
- [Single-Stage Heavy-Tailed Food Classification](https://mlsanity.com/paper/2307.00182/) (2023)
- [Towards the Creation of a Nutrition and Food Group Based Image Database](https://mlsanity.com/paper/2206.02086/) (2022)
- [Hierarchical approach to classify food scenes in egocentric photo-streams](https://mlsanity.com/paper/1905.04097/) (2019)

## Built on by (most cited first, 4 of 4)

- [Challenges and Trends in Egocentric Vision: A Survey](https://mlsanity.com/paper/2503.15275/) (2025)
- [MetaFood CVPR 2024 Challenge on Physically Informed 3D Food Reconstruction: Methods and Results](https://mlsanity.com/paper/2407.09285/) (2024)
- [Comprehensive Evaluation of Large Multimodal Models for Nutrition Analysis: A New Benchmark Enriched with Contextual Metadata](https://mlsanity.com/paper/2507.07048/) (2025)
- [Evaluating the Feasibility of Inferring Dietary Behavior Change Receptivity from Egocentric Images of Eating Environment](https://mlsanity.com/paper/2605.27950/) (2026)

## Abstract (arXiv)

Detecting an ingestion environment is an important aspect of monitoring dietary intake. It provides insightful information for dietary assessment. However, it is a challenging problem where human-based reviewing can be tedious, and algorithm-based review suffers from data imbalance and perceptual aliasing problems. To address these issues, we propose a neural network-based method with a two-stage training framework that tactfully combines fine-tuning and transfer learning techniques. Our method is evaluated on a newly collected dataset called ``UA Free Living Study", which uses an egocentric wearable camera, AIM-2 sensor, to simulate food consumption in free-living conditions. The proposed training framework is applied to common neural network backbones, combined with approaches in the general imbalanced classification field. Experimental results on the collected dataset show that our proposed method for automatic ingestion environment recognition successfully addresses the challenging data imbalance problem in the dataset and achieves a promising overall classification accuracy of 96.63%.