Research map / Graph learning / Graph algorithms and clustering
Universe Points Representation Learning for Partial Multi-Graph Matching
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use learned universe representations for object-to-universe matching (ablation) | URL · Pascal VOC | F1: 55.4±0.6 -> 71.7±0.5 | Helped |
| Add graph neural network processing with 2D and 3D SplineCNN blocks (ablation) | URL · Pascal VOC | F1: 55.1±0.6 -> 71.7±0.5 | Helped |
| Add 3DSplineCNN with a predicted virtual coordinate (ablation) | URL with graph feature processing · Pascal VOC | F1: 67.6±1.0 -> 71.7±0.5 | Helped |
| Use object-to-universe matching instead of pairwise matching | Pascal VOC partial multi-graph matching · Pascal VOC | F1: 61.4 -> 71.7 | Helped |
| Transfer the Pascal VOC-trained URL encoder without fine-tuning and use HiPPI | Willow full matching · Willow | accuracy: 94.5 -> 98.9 | Helped |
| Use LAP rather than HiPPI with the transferred URL features | URL on Willow · Willow | accuracy: 98.9 -> 95.0 | Hurt |
| Add URL universe representation learning | partial multi-graph matching · Pascal VOC and CUB-200-2011 | F1: 62.8 -> 71.7; 82.6 -> 95.1 | Helped |
Closest papers by meaning
- Discrete Cycle-Consistency Based Unsupervised Deep Graph Matching (AAAI 2024 · similarity 0.88)
- Deep Latent Graph Matching (ICML 2021 · similarity 0.88)
- Learning Partial Graph Matching via Optimal Partial Transport (ICLR 2025 · similarity 0.88)
- Deep Learning of Graph Matching (CVPR 2018 · similarity 0.87)
- IA-GM: A Deep Bidirectional Learning Method for Graph Matching (AAAI 2021 · similarity 0.87)
- SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream Tasks (CVPR 2024 · similarity 0.85)
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