Adaptive Connectivity Is Not Correspondence: A Cross-Pose Study of Vision Graphs

Vision graph networks dynamically connect image regions through input-dependent edges, yet it remains unclear whether these learned connections capture true cross-view correspondence. We investigate this distinction using cross-pose face recognition as a controlled testbed. While achieving high frontal accuracy, the tested vision-graph models underperform a convolutional baseline on frontal-profile verification. Through controlled interventions, we find that grid anchoring emerges at an intermediate stage, while features at supplied correspondences retain a measurable advantage over identity-substituted controls. A geometric bound on cell-constrained sampling reach, however, does not imply a bound on recognition capacity. Under matched data and training budgets, pose augmentation alone outperforms adding the tested explicit correspondence alignment. Under a full 20-epoch schedule, pose augmentation improves extreme-pose genuine acceptance from 86.05% to 92.02 ± 0.99%. These recognition gains replicate across training datasets, whereas connectivity patterns are dataset-dependent, and the augmented models become less robust to pixel-noise corruptions. Together, our results show that adaptive connectivity, cross-view correspondence, and downstream recognition require distinct evidence and should be evaluated separately.