用少量标注数据实现鱼肉新鲜度的精准天级估计。
Few-Shot Ordinal Learning for Day-Wise Freshness Estimation with Hyperspectral Fish Images
- 基于少样本学习框架，每片鱼肉视为独立任务
- 仅需每片3个标注日，平均误差1.58天，2天内准确率达72.3%
- 适合标注成本高、个体差异大的食品质量评估场景
非破坏性食品质量评估正受益于高光谱成像（HSI），该技术可捕捉存储过程中与生化变化相关的光谱特征。然而，由于鱼片间差异大且每种产品标注数据稀少，实现天级新鲜度估计仍具挑战。现有基于HSI的深度学习方法均依赖全监督训练，需密集标注数据集，而这类数据在单品级别获取成本高昂。本文首次提出面向HSI食品质量评估的少样本学习框架。每片鱼肉定义为一个独立的元任务，采用类似CORAL的序数预测头，通过累积阈值建模捕捉新鲜度演进的排序特性。引入基于生物学先验的单调性与嵌入平滑性约束，引导预测走向合理轨迹。在16天鲑鱼HSI数据集上，严格遵循未见鱼片协议，本方法仅用每片3个标注日，即达1.58天平均绝对误差和72.3%的2天内准确率，显著优于标量回归与标签分布基线。
原文摘要 · Abstract (English)
Non-destructive food quality assessment has increasingly benefited from hyperspectral imaging (HSI), which captures spectral signatures linked to biochemical changes during storage. Estimating day-wise freshness, however, remains challenging owing to strong inter-fillet variability and scarce labelled data per product. All existing deep learning approaches for HSI-based freshness prediction operate under full supervision, requiring densely annotated training sets that are costly to obtain at the individual-product level. We introduce, to the best of our knowledge, the first few-shot learning framework for HSI-based food quality estimation. Each fillet defines a distinct episodic task, and a CORAL-style ordinal prediction head captures the ranked nature of freshness progression through cumulative threshold modelling. Biologically grounded monotonicity and embedding smoothness constraints further guide predictions toward plausible trajectories. On a 16-day salmon HSI dataset under a strict unseen-fillet protocol, our method achieves a mean absolute error of 1.58 days and 2-day accuracy of 72.3% with only three labelled days per fillet, substantially outperforming scalar regression and label-distribution baselines under an identical unseen-fillet protocol.
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