Long-Tailed Continual Learning For Visual Food Recognition

Clicks: 66
ID: 282985
2023
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Abstract
Deep learning-based food recognition has made significant progress in predicting food types from eating occasion images. However, two key challenges hinder real-world deployment: (1) continuously learning new food classes without forgetting previously learned ones, and (2) handling the long-tailed distribution of food images, where a few common classes and many more rare classes. To address these, food recognition methods should focus on long-tailed continual learning. In this work, We introduce a dataset that encompasses 186 American foods along with comprehensive annotations. We also introduce three new benchmark datasets, VFN186-LT, VFN186-INSULIN and VFN186-T2D, which reflect real-world food consumption for healthy populations, insulin takers and individuals with type 2 diabetes without taking insulin. We propose a novel end-to-end framework that improves the generalization ability for instance-rare food classes using a knowledge distillation-based predictor to avoid misalignment of representation during continual learning. Additionally, we introduce an augmentation technique by integrating class-activation-map (CAM) and CutMix to improve generalization on instance-rare food classes. Our method, evaluated on Food101-LT, VFN-LT, VFN186-LT, VFN186-INSULIN, and VFN186-T2DM, shows significant improvements over existing methods. An ablation study highlights further performance enhancements, demonstrating its potential for real-world food recognition applications.
Reference Key
zhu2023longtailed Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jiangpeng He, Xiaoyan Zhang, Luotao Lin, Jack Ma, Heather A. Eicher-Miller, Fengqing Zhu
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Journal arXiv
Year 2023
DOI
DOI not found
URL
http://arxiv.org/abs/2307.00183v2
Keywords
cs.cv

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