  Despite the growing use of transformer models in computer vision, a
mechanistic understanding of these networks is still needed. This work
introduces a method to reverse-engineer Vision Transformers trained to solve
image classification tasks. Inspired by previous research in NLP, we
demonstrate how the inner representations at any level of the hierarchy can be
projected onto the learned class embedding space to uncover how these networks
build categorical representations for their predictions. We use our framework
to show how image tokens develop class-specific representations that depend on
attention mechanisms and contextual information, and give insights on how
self-attention and MLP layers differentially contribute to this categorical
composition. We additionally demonstrate that this method (1) can be used to
determine the parts of an image that would be important for detecting the class
of interest, and (2) exhibits significant advantages over traditional linear
probing approaches. Taken together, our results position our proposed framework
as a powerful tool for mechanistic interpretability and explainability
research.

Computer Vision and Pattern Recognition;Artificial Intelligence