让食物分类模型持续学习新菜系，不丢旧知识。
Continual Learning for Food Category Classification Dataset: Enhancing Model Adaptability and Performance
- 用增量更新替代重训练，支持动态添加新菜系
- 模型可从西餐扩展到多国菜肴，如咖喱、泡菜
- 适合饮食监测与个性化营养场景
传统机器学习模型难以识别训练集之外的食物类别，导致准确率下降。为解决此问题，本文提出一种文本引导的食物分类持续学习框架。相比需从头训练的方法，该框架支持增量更新，可在不损害已有知识的前提下引入新类别。例如，一个仅训练于西餐的模型，可后续学习识别印度炖菜（dosa）或韩式泡菜（kimchi）。尽管仍需进一步优化，该设计在自适应食物识别方面展现出潜力，适用于饮食监测与个性化营养规划。
原文摘要 · Abstract (English)
Conventional machine learning pipelines often struggle to recognize categories absent from the original trainingset. This gap typically reduces accuracy, as fixed datasets rarely capture the full diversity of a domain. To address this, we propose a continual learning framework for text-guided food classification. Unlike approaches that require retraining from scratch, our method enables incremental updates, allowing new categories to be integrated without degrading prior knowledge. For example, a model trained on Western cuisines could later learn to classify dishes such as dosa or kimchi. Although further refinements are needed, this design shows promise for adaptive food recognition, with applications in dietary monitoring and personalized nutrition planning.
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