Papers › GAMnet: Robust Feature Matching via Graph Adversarial-Matching Network
GAMnet: Robust Feature Matching via Graph Adversarial-Matching Network
Bo Jiang, Pengfei Sun, Ziyan Zhang, Jin Tang, Bin Luo
Recently, deep graph matching (GM) methods have gained increasing attention. These methods integrate graph nodes¡¯s embedding, node/edges¡¯s affinity learning and final correspondence solver together in an end-to-end manner. For deep graph matching problem, one main issue is how to generate consensus node's embeddings for both source and target graphs that best serve graph matching tasks. In addition, it is also challenging to incorporate the discrete one-to-one matching constraints into the differentiable correspondence solver in deep matching network. To address these issues, we propose a novel Graph Adversarial Matching Network (GAMnet) for graph matching problem. GAMnet integrates graph adversarial embedding and graph matching simultaneously in a unified end-to-end network which aims to adaptively learn distribution consistent and domain invariant embeddings for GM tasks. Also, GAMnet exploits sparse GM optimization as correspondence solver which is differentiable and can also incorporate discrete one-to-one matching constraints approximately in natural in the final matching prediction. Experimental results on three public benchmarks demonstrate the effectiveness and benefits of the proposed GAMnet.
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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 | GAMnet | matching accuracy | 0.807 | #19 of 31 | Archive leaderboard | report |
| Graph Matching | Willow Object Class | GAMnet | matching accuracy | 0.9662 | #13 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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