# ViT-Explainer: An Interactive Walkthrough of the Vision Transformer Pipeline

arXiv: https://arxiv.org/abs/2604.02182 · HTML: https://mlsanity.com/paper/2604.02182/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 2 Apr 2026.

## Builds on (most cited first, 15 of 15)

- [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)
- [Transformer Feed-Forward Layers Are Key-Value Memories](https://mlsanity.com/paper/2012.14913/) (2020)
- [What Does BERT Look At? An Analysis of BERT's Attention](https://mlsanity.com/paper/1906.04341/) (2019)
- [Eliciting Latent Predictions from Transformers with the Tuned Lens](https://mlsanity.com/paper/2303.08112/) (2023)
- [A Multiscale Visualization of Attention in the Transformer Model](https://mlsanity.com/paper/1906.05714/) (2019)
- [A Survey on Neural Network Interpretability](https://mlsanity.com/paper/2012.14261/) (2020)
- [CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization](https://mlsanity.com/paper/2004.15004/) (2020)
- [VL-InterpreT: An Interactive Visualization Tool for Interpreting Vision-Language Transformers](https://mlsanity.com/paper/2203.17247/) (2022)
- [How Does Attention Work in Vision Transformers? A Visual Analytics Attempt](https://mlsanity.com/paper/2303.13731/) (2023)
- [Dodrio: Exploring Transformer Models with Interactive Visualization](https://mlsanity.com/paper/2103.14625/) (2021)
- [Visualizing and Understanding Patch Interactions in Vision Transformer](https://mlsanity.com/paper/2203.05922/) (2022)
- [Transformer Explainer: Learning LLM Transformers with Interactive Visual Explanation and Experimentation](https://mlsanity.com/paper/2408.04619/) (2024)
- [EL-VIT: Probing Vision Transformer with Interactive Visualization](https://mlsanity.com/paper/2401.12666/) (2024)
- [CafGa: Customizing Feature Attributions to Explain Language Models](https://mlsanity.com/paper/2509.20901/) (2025)

## Abstract (arXiv)

Transformer-based architectures have become the shared backbone of natural language processing and computer vision. However, understanding how these models operate remains challenging, particularly in vision settings, where images are processed as sequences of patch tokens. Existing interpretability tools often focus on isolated components or expert-oriented analysis, leaving a gap in guided, end-to-end understanding of the full inference pipeline. To bridge this gap, we present ViT-Explainer, a web-based interactive system that provides an integrated visualization of Vision Transformer inference, from patch tokenization to final classification. The system combines animated walkthroughs, patch-level attention overlays, and a vision-adapted Logit Lens within both guided and free exploration modes. A user study with six participants suggests that ViT-Explainer is easy to learn and use, helping users interpret and understand Vision Transformer behavior.