Vision Transformer: Insights into Functionality Through Neural Paths

Vision Transformers (ViTs) have established themselves as powerful models in the field of computer vision. Their ability to process complex image information surpasses that of traditional Convolutional Neural Networks (CNNs) in many applications. Despite their impressive performance, however, the workings of ViTs remain largely opaque. This lack of understanding presents both challenges and risks for practical application, especially regarding trustworthiness, interpretability, and robustness.

Previous research approaches to deciphering ViTs have focused primarily on input attribution and the analysis of individual neurons' roles. However, the information flow between layers and the holistic path of information processing has often been neglected. A new research approach, presented in the paper "Discovering Influential Neuron Path in Vision Transformers," addresses the importance of influential neural paths within ViTs.

Neural Paths and their Influence

A neural path in a ViT is a chain of neurons extending from the input to the output of the model. The research suggests that certain neural paths have a significant influence on the model's conclusions. To quantify this influence, a joint influence measure has been developed, which assesses the contribution of a group of neurons to the model's output.

A layer-wise procedure is used to identify the most influential neural paths. In each layer, the neuron that has the greatest influence on the result is selected. By progressively selecting the most influential neurons in each layer, the crucial neural path is reconstructed from the input to the output of the model.

Experimental Results and Insights

Experimental results show that this approach identifies the most influential neural paths, along which information flows, more successfully than existing baseline solutions. The analysis of these paths suggests that ViTs have specific internal mechanisms for processing visual information within the same image category.

Further investigations show that the identified neural paths preserve the model's capabilities for downstream tasks, such as image classification. These findings could be relevant for applications like model pruning, as they offer the potential to reduce model complexity while maintaining performance.

Outlook

The exploration of influential neural paths in ViTs is a promising approach to better understand the workings of these complex models. The insights gained could lead to improved interpretation possibilities, more efficient training methods, and more robust models. Future research could focus on investigating the generalizability of these results to other ViT architectures and datasets, as well as on developing methods for targeted manipulation of neural paths to improve model performance.

Bibliography: - Wang, Yifan, et al. "Discovering Influential Neuron Path in Vision Transformers." *arXiv preprint arXiv:2503.09046* (2025). - Chang, Hanting, et al. "Revisiting Vision Transformer from the View of Path Ensemble." *Proceedings of the IEEE/CVF International Conference on Computer Vision*. 2023. - Khan, Salman, et al. "A survey of the vision transformers and their CNN-transformer based variants." *arXiv preprint arXiv:2310.19247* (2023). - Lee, Youngwan, et al. "MPViT: Multi-Path Vision Transformer for Dense Prediction." *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*. 2022.