Case-Enhanced Vision Transformer: Improving Explanations of Image Similarity with a ViT-based Similarity Metric
<|reference_start|>Case-Enhanced Vision Transformer: Improving Explanations of Image Similarity with a ViT-based Similarity Metric: 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.<|reference_end|>
@article{zhao2024case-enhanced,
    title={Case-Enhanced Vision Transformer: Improving Explanations of Image
  Similarity with a ViT-based Similarity Metric},
    author={Ziwei Zhao, David Leake, Xiaomeng Ye, David Crandall},
    journal={arXiv preprint arXiv:2407.16981},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16981},
    primaryClass={cs.CV cs.AI}
}