Papers › Deep Graph Matching under Quadratic Constraint

Deep Graph Matching under Quadratic Constraint

11 Mar 2021CVPR 2021 1arXiv:2103.06643archive 2025-07-28

Quankai Gao, Fudong Wang, Nan Xue, Jin-Gang Yu, Gui-Song Xia

Recently, deep learning based methods have demonstrated promising results on the graph matching problem, by relying on the descriptive capability of deep features extracted on graph nodes. However, one main limitation with existing deep graph matching (DGM) methods lies in their ignorance of explicit constraint of graph structures, which may lead the model to be trapped into local minimum in training. In this paper, we propose to explicitly formulate pairwise graph structures as a \textbf{quadratic constraint} incorporated into the DGM framework. The quadratic constraint minimizes the pairwise structural discrepancy between graphs, which can reduce the ambiguities brought by only using the extracted CNN features. Moreover, we present a differentiable implementation to the quadratic constrained-optimization such that it is compatible with the unconstrained deep learning optimizer. To give more precise and proper supervision, a well-designed false matching loss against class imbalance is proposed, which can better penalize the false negatives and false positives with less overfitting. Exhaustive experiments demonstrate that our method competitive performance on real-world datasets.

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Tasks

DescriptiveGraph Matching

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Graph Matching PASCAL VOC qc-DGM2 matching accuracy 0.703 #22 of 31 Archive leaderboard report
Graph Matching PASCAL VOC qc-DGM1 matching accuracy 0.693 #23 of 31 Archive leaderboard report
Graph Matching Willow Object Class qc-DGM2 matching accuracy 0.977 #8 of 23 Archive leaderboard report
Graph Matching Willow Object Class qc-DGM1 matching accuracy 0.960 #15 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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