ZhehengJiangLancaster / GCAN

Graph-context Attention Networks for Size-varied Deep Graph Matching

GCAN

This is a PyTorch implementation of our paper: Graph-context Attention Networks for Size-varied Deep Graph Matching

Requirements

• PyTorch (>=1.6.0)
• PyTorch Geometric (>=1.6.0)
• Gurobi Python (>=9.1.2)
• networkx (>=2.6.3)

Datasets:

1. Pascal VOC Keypoint: * Download and tar VOC2011 keypoints, and the path looks like: ./data/PascalVOC/VOC2011. * Download and tar Berkeley annotation, and the path looks like: ./data/PascalVOC/annotations. * The train/test split of Pascal VOC Keypoint is available in: ./data/PascalVOC/voc2011_pairs.npz.
2. Willow Object Class dataset: * Download and unzip Willow ObjectClass dataset, and the path looks like: ./data/WILLOW-ObjectClass.

Experiment:

Run training and evaluation on Pascal VOC Keypoint for Size-equal graph matching problem:

python train_eval.py --cfg ./experiments/GCAN_voc.yaml

Run training and evaluation on Pascal VOC Keypoint for Size-varied graph matching problem:

python train_eval.py --cfg ./experiments/GCAN_voc_varied_size.yaml

Unsupervised training code is coming soon

Citation

@InProceedings{Jiang_2022_CVPR,
    author    = {Jiang, Zheheng and Rahmani, Hossein and Angelov, Plamen and Black, Sue and Williams, Bryan M.},
    title     = {Graph-Context Attention Networks for Size-Varied Deep Graph Matching},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {2343-2352}
}