Long-Tailed Continual Learning For Visual Food Recognition
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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
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|---|---|
| Authors | Jiangpeng He; Xiaoyan Zhang; Luotao Lin; Jack Ma; Heather A. Eicher-Miller; Fengqing Zhu |
| Journal | arXiv |
| Year | 2023 |
| DOI |
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