Papers › Graph-Context Attention Networks for Size-Varied Deep Graph Matching
Graph-Context Attention Networks for Size-Varied Deep Graph Matching
Zheheng Jiang, Hossein Rahmani, Plamen Angelov, Sue Black, Bryan M. Williams
Deep learning for graph matching has received growing interest and developed rapidly in the past decade. Although recent deep graph matching methods have shown excellent performance on matching between graphs of equal size in the computer vision area, the size-varied graph matching problem, where the number of keypoints in the images of the same category may vary due to occlusion, is still an open and challenging problem. To tackle this, we firstly propose to formulate the combinatorial problem of graph matching as an Integer Linear Programming (ILP) problem, which is more flexible and efficient to facilitate comparing graphs of varied sizes. A novel Graph-context Attention Network (GCAN), which jointly capture intrinsic graph structure and cross-graph information for improving the discrimination of node features, is then proposed and trained to resolve this ILP problem with node correspondence supervision. We further show that the proposed GCAN model is efficient to resolve the graph-level matching problem and is able to automatically learn node-to-node similarity via graph-level matching. The proposed approach is evaluated on three public keypoint-matching datasets and one graph-matching dataset for blood vessel patterns, with experimental results showing its superior performance over existing state-of-the-art algorithms on the keypoint and graph-level matching tasks.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Graph Matching | PASCAL VOC | GCAN | matching accuracy | 0.8223 | #16 of 31 | Archive leaderboard | report |
| Graph Matching | SPair-71k | GCAN | matching accuracy | 0.8210 | #6 of 8 | Archive leaderboard | report |
| Graph Matching | Willow Object Class | GCAN | matching accuracy | 0.9700 | #11 of 23 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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