Research map / Graph learning / Graph algorithms and clustering
Learning Combinatorial Solver for Graph Matching
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Jointly learn assignment-graph node selection and the combinatorial solver rather than learning affinities alone | Graph matching · Willow Object | matching accuracy (%): PCA 90.2; ours 94.9 | Helped |
| Jointly learn assignment-graph node selection and the combinatorial solver rather than relying on learned affinities with a simpler solver | Graph matching · Pascal VOC Keypoints | matching accuracy (%): PCA 63.8; ours 68.5 | Helped |
| Use the learned graph-matching framework under noise and outliers | 2D point-set matching · Synthetic 2D Points | matching accuracy | Helped |
| Use the learned graph-matching framework across deformation and outlier settings | Graph matching · CMU House | matching accuracy | Helped |
Closest papers by meaning
- Deep Learning of Graph Matching (CVPR 2018 · similarity 0.92)
- Learning Partial Graph Matching via Optimal Partial Transport (ICLR 2025 · similarity 0.90)
- Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning (NeurIPS 2020 · similarity 0.90)
- Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search (NeurIPS 2018 · similarity 0.89)
- COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial Problems (AAAI 2024 · similarity 0.89)
- M3C: A Framework towards Convergent, Flexible, and Unsupervised Learning of Mixture Graph Matching and Clustering (ICLR 2024 · similarity 0.87)
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