Towards Fine-Grained Interpretability: Counterfactual Explanations for Misclassification with Saliency Partition

• URL: http://arxiv.org/abs/2511.07974v1
• Date: Wed, 12 Nov 2025 01:31:56 GMT
• Title: Towards Fine-Grained Interpretability: Counterfactual Explanations for Misclassification with Saliency Partition
• Authors: Lintong Zhang, Kang Yin, Seong-Whan Lee,
• Abstract summary: We propose a fine-grained counterfactual explanation framework that generates both object-level and part-level interpretability.<n>Our approach yields explainable counterfactuals in a non-generative manner by quantifying similarity and weighting component contributions.
• Score: 50.68751788132789
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Attribution-based explanation techniques capture key patterns to enhance visual interpretability; however, these patterns often lack the granularity needed for insight in fine-grained tasks, particularly in cases of model misclassification, where explanations may be insufficiently detailed. To address this limitation, we propose a fine-grained counterfactual explanation framework that generates both object-level and part-level interpretability, addressing two fundamental questions: (1) which fine-grained features contribute to model misclassification, and (2) where dominant local features influence counterfactual adjustments. Our approach yields explainable counterfactuals in a non-generative manner by quantifying similarity and weighting component contributions within regions of interest between correctly classified and misclassified samples. Furthermore, we introduce a saliency partition module grounded in Shapley value contributions, isolating features with region-specific relevance. Extensive experiments demonstrate the superiority of our approach in capturing more granular, intuitively meaningful regions, surpassing fine-grained methods.

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