Understanding Geometric Representations in Self-Supervised Vision Transformers via Subspace Intervention

Researchers have developed a subspace intervention framework to analyze how self-supervised Vision Transformers store geometric data. This method moves beyond traditional linear probing by isolating specific feature dimensions to better understand how these models process spatial information.