连续学习用于食品类别分类数据集：增强模型适应性和性能
Continual Learning for Food Category Classification Dataset: Enhancing Model Adaptability and Performance
AI总结：
本文提出基于文本引导的连续学习框架，用于食品分类，使模型能逐步更新以适应新类别，提升适应性和性能，应用于饮食监控和个性化营养规划。
摘要：
传统机器学习流程常难以识别原始训练集未包含的类别，这通常会降低准确性，因为固定数据集难以捕捉领域完整多样性。为此，我们提出了一种基于文本引导的连续学习框架用于食品分类。与需要从头开始重新训练的方法不同，我们的方法允许逐步更新，使新类别能够被整合而不影响原有知识。例如，一个训练于西方美食的模型可后来学习分类如达克萨或 kimchi 等菜肴。尽管还需进一步改进，此设计在适应性食品识别中显示了前景，应用于饮食监控和个性化营养规划。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
