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The Vision Transformer (ViT) demonstrates exceptional performance in variouscomputer vision tasks. Attention is crucial for ViT to capture complexwide-ranging relationships among image patches, allowing the model to weigh theimportance of image patches and aiding our understanding of the decision-makingprocess. However, when utilizing the attention of ViT as evidence inhigh-stakes decision-making tasks such as medical diagnostics, a challengearises due to the potential of attention mechanisms erroneously focusing onirrelevant regions. In this study, we propose a statistical test for ViT'sattentions, enabling us to use the attentions as reliable quantitative evidenceindicators for ViT's decision-making with a rigorously controlled error rate.Using the framework called selective inference, we quantify the statisticalsignificance of attentions in the form of p-values, which enables thetheoretically grounded quantification of the false positive detectionprobability of attentions. We demonstrate the validity and the effectiveness ofthe proposed method through numerical experiments and applications to brainimage diagnoses.

Statistical Test for Attention Map in Vision Transformer