发表机构
School of Built Environment, Engineering and Computing, Leeds Beckett University（利兹贝克特大学建筑环境、工程与计算学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高光谱鱼类图像的按天新鲜度估计，提出首个小样本序贯学习框架，在16天三文鱼HSI数据集上，仅用3个标注天的情况下，平均绝对误差1.58天、2天准确率72.3%，优于基线方法。
AI 中文摘要
无损食品质量评估越来越受益于高光谱成像（HSI），该技术可捕获与储存过程中生化变化相关的光谱特征。然而，由于鱼片间存在强烈的变异性且每个产品的标注数据稀缺，按天估计新鲜度仍具挑战性。所有现有的基于HSI的新鲜度预测深度学习方法均在全监督模式下运行，需要密集标注的训练集，而针对单个产品获取此类数据集成本高昂。据我们所知，我们提出了首个用于基于HSI的食品质量估计的小样本学习框架。每个鱼片定义一个独特的episode任务，CORAL风格的序贯预测头通过累积阈值建模捕捉新鲜度变化的排序特性。基于生物学的单调性和嵌入平滑性约束进一步引导预测趋向合理轨迹。在包含16天数据的三文鱼HSI数据集上，采用严格的未见过鱼片协议，我们的方法在每个鱼片仅用3个标注天的情况下，达到1.58天的平均绝对误差和72.3%的2天准确率，在相同的未见过鱼片协议下，显著优于标量回归和标签分布基线方法。
英文摘要
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.
CommentsAccepted at EUSIPCO'2026
