Pixel-level feature attributions are an important tool in eXplainable AI for Computer Vision (XCV), providing visual insights into how image features influence model predictions. The Owen formula for hierarchical Shapley values has been widely used to interpret machine learning (ML) models and their learned representations. However, existing hierarchical Shapley approaches do not exploit the multiscale structure of image data, leading to slow convergence and weak alignment with the actual morphological features. Moreover, no prior Shapley method has leveraged data-aware hierarchies for Computer Vision tasks, leaving a gap in model interpretability of structured visual data. To address this, this paper introduces ShapBPT, a novel data-aware XCV method based on the hierarchical Shapley formula. ShapBPT assigns Shapley coefficients to a multiscale hierarchical structure tailored for images, the Binary Partition Tree (BPT). By using this data-aware hierarchical partitioning, ShapBPT ensures that feature attributions align with intrinsic image morphology, effectively prioritizing relevant regions while reducing computational overhead. This advancement connects hierarchical Shapley methods with image data, providing a more efficient and semantically meaningful approach to visual interpretability. Experimental results confirm ShapBPT's effectiveness, demonstrating superior alignment with image structures and improved efficiency over existing XCV methods, and a 20-subject user study confirming that ShapBPT explanations are preferred by humans.

翻译：像素级特征归因是计算机视觉可解释人工智能（XCV）中的重要工具，能够直观揭示图像特征如何影响模型预测。基于分层沙普利值的欧文公式已被广泛用于解释机器学习模型及其习得表征。然而，现有分层沙普利方法未能利用图像数据的多尺度结构，导致收敛速度缓慢且与实际形态特征对齐性较弱。此外，先前所有沙普利方法均未在计算机视觉任务中采用数据感知的层次结构，导致结构化视觉数据的模型可解释性存在空白。为此，本文提出ShapBPT——一种基于分层沙普利公式的新型数据感知XCV方法。该方法将沙普利系数分配至专为图像定制的多尺度层次结构——二叉划分树。通过采用这种数据感知的层次划分机制，ShapBPT确保特征归因与图像内在形态对齐，在有效突出相关区域的同时降低计算开销。这一进展将分层沙普利方法与图像数据相结合，为视觉可解释性提供了更高效且更具语义意义的研究路径。实验结果证实了ShapBPT的有效性：相较于现有XCV方法，其展现出更优的图像结构对齐性与计算效率提升，一项20人参与的用户研究亦表明人类更倾向于选择ShapBPT生成的解释。