@inproceedings{
zeng2024peeling,
title={Peeling Back the Layers: Interpreting the Storytelling of ViT},
author={Jingjie Zeng and Zhihao Yang and Qi Yang and Liang Yang and Hongfei Lin},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=yN2zvWf9a0}
}
By integrating various modules with the Visual Transformer (ViT), we facilitate a interpretation of image processing across each layer and attention head. This method allows us to explore the connections both within and across the layers, enabling a analysis of how images are processed at different layers. Conducting a analysis of the contributions from each layer and attention head, shedding light on the intricate interactions and functionalities within the model's layers. This in-depth exploration not only highlights the visual cues between layers but also examines their capacity to navigate the transition from abstract concepts to tangible objects. It unveils the model's mechanism to building an understanding of images, providing a strategy for adjusting attention heads between layers, thus enabling targeted pruning and enhancement of performance for specific tasks. Our research indicates that achieving a scalable understanding of transformer models is within reach, offering ways for the refinement and enhancement of such models.
Peeling Back the Layers: Interpreting the Storytelling of ViT
Jingjie Zeng Zhihao Yang Qi Yang Liang Yang Hongfei Lin