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arXiv · 2609.25815

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

Anirudh Prabhakaran·Alexandre Rocchi·Gianni Franchi
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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.

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Anirudh Prabhakaran, Alexandre Rocchi, Gianni Franchi. 2026-09-22. MorphoSHAP: Rethinking the Unit of Attribution in Explanation for Deep Visual Models. https://arxiv.org/abs/2609.25815

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