GCAN

GCAN

Our implementation of the following paper:

• Zheheng Jiang, Hossein Rahmani, Plamen Angelov, Sue Black, Bryan M. Williams. "Graph-Context Attention Networks for Size-Varied Deep Graph Matching." CVPR 2022. [paper]

GCAN proposes a deep graph matching pipeline to solve size-varied GM problem. It learns both node-level and graph-level matching. It proposes to combine the following components to formulate the GM pipeline:

• VGG16 CNN to extract image features
• Positional encoding and Graph-context attention (GCA) module to extract node features
• Solving node correspondence via Integer Linear Programming (ILP) attention loss
• Learning graph-level matching via margin-based pairwise loss

Benchmark Results

IMC-PT-SparseGM-50 - 2GM (unfiltered)

experiment config: experiments/vgg16_gcan_imcpt_50.yaml

pretrained model: google drive

model	year	reichstag	sacre coeur	st peters square	mean
GCAN	2022	0.872	0.551	0.630	0.684

IMC-PT-SparseGM-100 - 2GM (unfiltered)

experiment config: experiments/vgg16_gcan_imcpt_100.yaml

pretrained model: google drive

model	year	reichstag	sacre coeur	st peters square	mean
GCAN	2022	0.804	0.557	0.728	0.696

File Organization

├── cross_attention_layer.py
|   the implementation of cross-attention layer used in GCA module
├── GCA_module.py
|   the implementation of GCA module
├── GCAN_model.py
|   the implementation of training/evaluation procedures of GCAN 
├── GCAN_model_topk.py
|   the implementation of training/evaluation procedures of GCAN combined with topk-GM algorithm and partial matching handling AFA modules
├── model_config.py
|   the declaration of model hyperparameters
├── positional_encoding_layer.py
|   the implementation of positional encoding
└── self_attention_layer.py
    the implementation of self-attention layer used in GCA module

Credits and Citation

Please cite the following paper if you use this model in your research:

@InProceedings{JiangCVPR22,
    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)},
    year={2022},
}