文档图像解释需用结构感知分割，否则会掩盖模型作弊行为
When Superpixels Fail on Documents: A Study of Segmentation for LIME Explanations
- 用OCR框或规则网格做分割，比通用超像素更贴合文档结构
- 结构感知分割使解释更稳定，收敛更快，能暴露识别码依赖的模型偏见
- 适合关注模型可解释性与公平性的研究者和工程师
后置解释方法广泛用于检验图像分类器，但其可靠性依赖于常被当作实现细节的设计选择。本文研究了文档图像分类中LIME的分割步骤对解释可信度的影响。标准LIME通常使用自然图像超像素，难以对齐文本区域、版面块和识别码等文档结构。基于RVL-CDIP数据集，我们对比了Quickshift、SLIC与基于OCR边界框及规则网格的文档感知分割。结果表明，分割方式显著影响解释的一致性、正确性和局部保真度：文档感知分割产生的解释更稳定、更忠实，收敛所需扰动更少，并能揭示模型对文档识别码的捷径依赖——这是RVL-CDIP中的已知偏差，而超像素基的LIME往往掩盖此问题。研究说明，可靠的后置解释需要领域感知的可解释表示，分割应视为解释方法的一部分而非中性预处理。
原文摘要 · Abstract (English)
Post-hoc explanation methods are widely used to inspect image classifiers, but their reliability depends on design choices that are often treated as implementation details. We study this issue for LIME on document image classification, focusing on the segmentation step that defines the interpretable units being perturbed. Standard image-based LIME typically relies on natural-image superpixels, which are poorly aligned with document structure such as text regions, layout blocks, and identification codes. Using RVL-CDIP, we compare Quickshift and SLIC with document-aware segmentations based on OCR bounding boxes and regular grids. Our results show that segmentation strongly affects explanation consistency, correctness, and local fidelity. Document-aware segmentations produce more stable and faithful explanations, require fewer perturbations to converge, and expose shortcut behaviour based on document identification codes, a known RVL-CDIP bias that superpixel-based LIME often obscures. These findings show that reliable post-hoc explanation requires domain-aware interpretable representations, and that segmentation should be treated as part of the explanation method rather than as neutral preprocessing.
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