MorphoSHAP：重新思考深度视觉模型解释中的归因单元
MorphoSHAP: Rethinking the Unit of Attribution in Explanation for Deep Visual Models
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中文总结 AI 辅助
MorphoSHAP利用形态学形状作为Shapley归因单元，提供空间、文本和全局类级解释，在多个基准上优于现有方法，并通过用户研究验证其易用性。
中文摘要 AI 辅助
视觉归因方法通常使用像素、超像素或规则块来解释预测。这些表示可以定位重要区域，但对其结构提供的信息有限。我们引入了MorphoSHAP，一种模型无关的事后方法，它改用形态学形状作为Shapley归因博弈的参与者。利用形状树，每个形状由其尺度、几何形状和有符号贡献来描述，从而提供关于证据位于何处、何种类型的结构承载证据以及它如何强烈影响预测的解释。这种共享的形态学词汇表使得空间、文本和全局类级解释成为可能，超越了仅针对图像的特定热图。据我们所知，MorphoSHAP是首个基于SHAP的图像归因框架，它结合了这些不同形式的解释。在五个不同的数据集和三种架构上，MorphoSHAP实现了强大的插入/删除性能，并在多个基准上优于竞争性归因方法。最后，一项用户研究表明，MorphoSHAP提供的解释易于使用，并且比标准归因基线更受青睐。
英文摘要
Visual attribution methods typically explain predictions using pixels, superpixels, or regular patches. These representations can localize important regions, but provide limited information about their structure. We introduce MorphoSHAP, a model-agnostic post-hoc method that instead uses morphological shapes as the players of a Shapley attribution game. Using the Tree of Shapes, each shape is described by its scale, geometry, and signed contribution, providing explanations of where the evidence lies, what type of structure carries it, and how strongly it affects the prediction. This shared morphological vocabulary enables spatial, textual, and global class-level explanations beyond image-specific heatmaps. To the best of our knowledge, MorphoSHAP is the first SHAP-based image attribution framework to combine these different forms of explanation. Across five diverse datasets and three architectures, MorphoSHAP achieves strong insertion/deletion performance and outperforms competing attribution methods on several benchmarks. Finally, a user study shows that MorphoSHAP provides explanations that are easy to use and are preferred over standard attribution baselines.
发表机构
- AMIAD, Pôle Recherche, Palaiseau（AMIAD，研究部，帕莱索）
机构由 AI 辅助整理，请以论文原文为准。
