ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder

Sangwon Kim · Jaeyeal Nam · Byoung Chul Ko

Hall E #924

Keywords: [ APP: Computer Vision ] [ DL: Attention Mechanisms ] [ SA: Accountability, Transparency and Interpretability ]


Vision transformers (ViTs), which have demonstrated a state-of-the-art performance in image classification, can also visualize global interpretations through attention-based contributions. However, the complexity of the model makes it difficult to interpret the decision-making process, and the ambiguity of the attention maps can cause incorrect correlations between image patches. In this study, we propose a new ViT neural tree decoder (ViT-NeT). A ViT acts as a backbone, and to solve its limitations, the output contextual image patches are applied to the proposed NeT. The NeT aims to accurately classify fine-grained objects with similar inter-class correlations and different intra-class correlations. In addition, it describes the decision-making process through a tree structure and prototype and enables a visual interpretation of the results. The proposed ViT-NeT is designed to not only improve the classification performance but also provide a human-friendly interpretation, which is effective in resolving the trade-off between performance and interpretability. We compared the performance of ViT-NeT with other state-of-art methods using widely used fine-grained visual categorization benchmark datasets and experimentally proved that the proposed method is superior in terms of the classification performance and interpretability. The code and models are publicly available at

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