Case-Enhanced Vision Transformer: Improving Explanations of Image Similarity with a ViT-based Similarity Metric

Ziwei Zhao; David Leake; Xiaomeng Ye; David Crandall

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arXiv · 2407.16981

Case-Enhanced Vision Transformer: Improving Explanations of Image Similarity with a ViT-based Similarity Metric

Ziwei Zhao·David Leake·Xiaomeng Ye·David Crandall

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Abstract

This short paper presents preliminary research on the Case-Enhanced Vision Transformer (CEViT), a similarity measurement method aimed at improving the explainability of similarity assessments for image data. Initial experimental results suggest that integrating CEViT into k-Nearest Neighbor (k-NN) classification yields classification accuracy comparable to state-of-the-art computer vision models, while adding capabilities for illustrating differences between classes. CEViT explanations can be influenced by prior cases, to illustrate aspects of similarity relevant to those cases.

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Ziwei Zhao, David Leake, Xiaomeng Ye, David Crandall. 2024-07-24. Case-Enhanced Vision Transformer: Improving Explanations of Image Similarity with a ViT-based Similarity Metric. https://arxiv.org/abs/2407.16981

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