  Graph Neural Networks (GNNs) with attention have been successfully applied
for learning visual feature matching. However, current methods learn with
complete graphs, resulting in a quadratic complexity in the number of features.
Motivated by a prior observation that self- and cross- attention matrices
converge to a sparse representation, we propose ClusterGNN, an attentional GNN
architecture which operates on clusters for learning the feature matching task.
Using a progressive clustering module we adaptively divide keypoints into
different subgraphs to reduce redundant connectivity, and employ a
coarse-to-fine paradigm for mitigating miss-classification within images. Our
approach yields a 59.7% reduction in runtime and 58.4% reduction in memory
consumption for dense detection, compared to current state-of-the-art GNN-based
matching, while achieving a competitive performance on various computer vision
tasks.
