  Vision graph neural networks (ViG) offer a new avenue for exploration in
computer vision. A major bottleneck in ViGs is the inefficient k-nearest
neighbor (KNN) operation used for graph construction. To solve this issue, we
propose a new method for designing ViGs, Dynamic Axial Graph Construction
(DAGC), which is more efficient than KNN as it limits the number of considered
graph connections made within an image. Additionally, we propose a novel
CNN-GNN architecture, GreedyViG, which uses DAGC. Extensive experiments show
that GreedyViG beats existing ViG, CNN, and ViT architectures in terms of
accuracy, GMACs, and parameters on image classification, object detection,
instance segmentation, and semantic segmentation tasks. Our smallest model,
GreedyViG-S, achieves 81.1% top-1 accuracy on ImageNet-1K, 2.9% higher than
Vision GNN and 2.2% higher than Vision HyperGraph Neural Network (ViHGNN), with
less GMACs and a similar number of parameters. Our largest model, GreedyViG-B
obtains 83.9% top-1 accuracy, 0.2% higher than Vision GNN, with a 66.6%
decrease in parameters and a 69% decrease in GMACs. GreedyViG-B also obtains
the same accuracy as ViHGNN with a 67.3% decrease in parameters and a 71.3%
decrease in GMACs. Our work shows that hybrid CNN-GNN architectures not only
provide a new avenue for designing efficient models, but that they can also
exceed the performance of current state-of-the-art models.
