AI 中文总结
本研究通过农业基准数据集评估CNN分类中的上下文偏差，发现多数数据集存在高于随机概率的上下文依赖，并提出系统量化框架。
AI 中文摘要
卷积神经网络（CNN）通常使用留出集的分类准确率进行评估，这种方法假设预测主要基于目标对象本身，而非偶然的周围上下文。我们通过在基于CNN的农业图像分类中，比较模型在原始图像上的性能与从相同图像中提取的背景主导补丁上的性能，来检验这一假设，涉及八个公开可用的农业基准数据集和四种广泛使用的CNN架构。在八个数据集中，有六个数据集的背景主导补丁分类结果高于数据集特定的随机概率，其中四个数据集显著高于随机概率，这表明上下文信息对大多数评估数据集的模型预测有贡献。对于这四个数据集，我们进一步使用宏平均精确率、召回率和F1值以及类别平衡的测试子集，评估了这种行为是否反映了真实的类别判别信息，还是主要归因于类别不平衡。结果表明，上下文依赖不能用一个单一的解释来概括：对于某些数据集和架构，类别不平衡解释了观察到的信号的大部分，而在平衡后，其他数据集和架构仍存在高于随机概率的上下文分类。结合先前在精选物体识别和癌症病理成像中的证据，这些发现支持了日益增长的观点，即上下文偏差是CNN图像分类的一个反复出现的特征，而非仅限于单一应用领域的现象。更广泛地说，这项工作通过结合数据集特定的随机概率基线、上下文偏差分类、宏平均评估和类别平衡鲁棒性分析，提供了一个跨异构图像数据集量化上下文偏差的系统框架。
英文摘要
Convolutional neural networks (CNNs) are typically evaluated using held-out classification accuracy, an approach that presupposes predictions are based primarily on the intended object of interest rather than incidental surrounding context. We test this assumption in CNN-based agricultural image classification by comparing model performance on original images with performance on background-dominated patches extracted from the same images across eight publicly available agricultural benchmark datasets and four widely used CNN architectures. Background-dominated patches were classified above dataset-specific random chance for six of the eight datasets, and substantially above chance for four of them, indicating that contextual information contributes to model predictions for the majority of datasets evaluated. For these four datasets, we further evaluated whether this behavior reflected genuine class-discriminative information or was primarily attributable to class imbalance using macro-averaged precision, recall, and F1 together with class-balanced test subsets. The results show that contextual reliance does not admit a single explanation: class imbalance accounts for a substantial portion of the observed signal for some datasets and architectures, whereas above-chance contextual classification persists after balancing for others. Together with previous evidence from curated object recognition and cancer pathology imaging, these findings support the growing view that contextual bias is a recurring characteristic of CNN-based image classification rather than a phenomenon confined to a single application domain. More broadly, this work provides a systematic framework for quantifying contextual bias across heterogeneous image datasets by combining dataset-specific random-chance baselines, contextual bias categorization, macro-averaged evaluation, and class-balanced robustness analysis.
