Omni-Flex GraphFormer: Parallel Fusion of Local Graph Aggregation and Global Attention Across Image Resolutions

Graph neural networks (GNNs) capture local image structure with few parameters, whereas Vision Transformers (ViTs) model global context at a higher computational cost. We propose the Omni-Flex GraphFormer, which applies a graph branch (OMNIVIG: max-relative aggregation over a fixed 3×3 pixel neighbourhood) and a Transformer branch (FLEXIVIT: global multi-head self-attention) in parallel and sums their outputs. Because each baseline is the hybrid with one branch removed, comparing the three models on eight datasets from 8×8 to 96×96 pixels, under equal training budgets and over three seeds, is a controlled branch ablation. Global attention is decisive: the hybrid exceeds OMNIVIG in test macro-F1 on every dataset and in all 24 runs, by 3.7 to 15.1 points (Wilcoxon test, p=0.0078). Against FLEXIVIT, the hybrid has the best mean F1 on six datasets and a 40–62% smaller seed-to-seed spread on CIFAR-10 and CIFAR-100, but the difference is not significant (p=0.38). The graph branch adds only 0.5–3.1% to the number of multiply–accumulate operations, and under warmed-up timing, both models run at the same speed, whereas unwarmed timing overstates latency by up to 15×. Under Gaussian noise, the summed hybrid degrades like the graph branch, which motivates gated or attention-guiding fusion.