MorphoSHAP: Rethinking the Unit of Attribution in Explanation for Deep Visual Models

2026-09-22

AI Summary

本文提出MorphoSHAP方法，通过使用形态学形状作为Shapley归因游戏的参与者来改进深度视觉模型的解释，提供更丰富的结构信息，并在多个数据集上优于现有方法。

Abstract

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.

Problem

• Visual Attribution
• Structure Information
• Explanation

Innovation

• Morphological Shapes
• Shapley Attribution
• Tree of Shapes
• Model-Agnostic
• Post-Hoc Explanation