Visual counterfactual explanations aim to reveal the minimal semantic modifications that can alter a model's prediction, providing causal and interpretable insights into deep neural networks. However, existing diffusion-based counterfactual generation methods are often computationally expensive, slow to sample, and imprecise in localizing the modified regions. To address these limitations, we propose MaskDiME, a simple, fast, yet effective diffusion framework that unifies semantic consistency and spatial precision through localized sampling. Our approach adaptively focuses on decision-relevant regions to achieve localized and semantically consistent counterfactual generation while preserving high image fidelity. Our training-free framework, MaskDiME, performs inference over 30x faster than the baseline and achieves comparable or state-of-the-art performance across five benchmark datasets spanning diverse visual domains, establishing a practical and generalizable solution for efficient counterfactual explanation.
@article{arxiv.2602.18792,
title = {MaskDiME: Adaptive Masked Diffusion for Precise and Efficient Visual Counterfactual Explanations},
author = {Changlu Guo and Anders Nymark Christensen and Anders Bjorholm Dahl and Morten Rieger Hannemose},
journal= {arXiv preprint arXiv:2602.18792},
year = {2026}
}