用元学习让小模型在少量数据下精准预测水果新鲜度，兼顾隐私与性能。
Privacy Preserving Ordinal-Meta Learning with VLMs for Fine-Grained Fruit Quality Prediction
- 基于元学习和标签顺序信息，提升小模型在少样本下的水果新鲜度判断能力。
- 零样本与少样本设置下平均准确率达92.71%，达行业标准水平。
- 适合关注隐私保护与低资源场景的农产品质量智能检测应用。
为有效减少易腐水果的损耗，需借助视觉数据非侵入式地精准预测其新鲜度或保质期。深度学习技术为此提供了可行方案，但专家标注精细的新鲜度标签成本高，导致数据稀缺。封闭式视觉语言模型（如Gemini）在零样本与少样本设置下表现优异，但食品零售机构因数据隐私顾虑无法使用；而现有开源VLM在该任务上表现欠佳，有限数据微调也难以达到封闭模型水平。本文提出一种无需依赖具体模型的序数元学习算法（MAOML），通过元学习缓解数据稀疏问题，并利用标签的序数特性，在零样本与少样本设置下均实现水果新鲜度分类任务的最先进性能。方法在所有水果类别上平均准确率达到92.71%。关键词：水果质量预测、视觉语言模型、元学习、序数回归。
原文摘要 · Abstract (English)
To effectively manage the wastage of perishable fruits, it is crucial to accurately predict their freshness or shelf life using non-invasive methods that rely on visual data. In this regard, deep learning techniques can offer a viable solution. However, obtaining fine-grained fruit freshness labels from experts is costly, leading to a scarcity of data. Closed proprietary Vision Language Models (VLMs), such as Gemini, have demonstrated strong performance in fruit freshness detection task in both zero-shot and few-shot settings. Nonetheless, food retail organizations are unable to utilize these proprietary models due to concerns related to data privacy, while existing open-source VLMs yield sub-optimal performance for the task. Fine-tuning these open-source models with limited data fails to achieve the performance levels of proprietary models. In this work, we introduce a Model-Agnostic Ordinal Meta-Learning (MAOML) algorithm, designed to train smaller VLMs. This approach utilizes meta-learning to address data sparsity and leverages label ordinality, thereby achieving state-of-the-art performance in the fruit freshness classification task under both zero-shot and few-shot settings. Our method achieves an industry-standard accuracy of 92.71%, averaged across all fruits. Keywords: Fruit Quality Prediction, Vision Language Models, Meta Learning, Ordinal Regression
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