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

arXiv: https://arxiv.org/abs/2407.16981 · HTML: https://mlsanity.com/paper/2407.16981/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 24 Jul 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Attention Mechanisms in Computer Vision: A Survey](https://mlsanity.com/paper/2111.07624/) (2021)
- [Learning by tracking: Siamese CNN for robust target association](https://mlsanity.com/paper/1604.07866/) (2016)
- [On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning](https://mlsanity.com/paper/2009.06399/) (2020)
- [Learning similarity measures from data](https://mlsanity.com/paper/2001.05312/) (2020)
- [Applying the Case Difference Heuristic to Learn Adaptations from Deep Network Features](https://mlsanity.com/paper/2107.07095/) (2021)

## Built on by (most cited first, 1 of 1)

- [Synthetic Similarity Search in Automotive Production](https://mlsanity.com/paper/2505.07256/) (2025)

## Abstract (arXiv)

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.