  Matching local features across images is a fundamental problem in computer
vision. Targeting towards high accuracy and efficiency, we propose Seeded Graph
Matching Network, a graph neural network with sparse structure to reduce
redundant connectivity and learn compact representation. The network consists
of 1) Seeding Module, which initializes the matching by generating a small set
of reliable matches as seeds. 2) Seeded Graph Neural Network, which utilizes
seed matches to pass messages within/across images and predicts assignment
costs. Three novel operations are proposed as basic elements for message
passing: 1) Attentional Pooling, which aggregates keypoint features within the
image to seed matches. 2) Seed Filtering, which enhances seed features and
exchanges messages across images. 3) Attentional Unpooling, which propagates
seed features back to original keypoints. Experiments show that our method
reduces computational and memory complexity significantly compared with typical
attention-based networks while competitive or higher performance is achieved.
