GANN

GANN

Our implementation of the following paper:

• Runzhong Wang, Junchi Yan and Xiaokang Yang. "Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning." NeurIPS 2020. [paper]
• Runzhong Wang, Junchi Yan and Xiaokang Yang. "Unsupervised Learning of Graph Matching with Mixture of Modes via Discrepancy Minimization." TPAMI 2023. [paper][project page]

GANN proposes a self-supervised learning framework by leveraging graph matching solvers to provide pseudo labels to train the neural network module in deep graph matching pipeline. We propose a general graph matching solver for various graph matching settings based on the classic Graduated Assignment (GA) algorithm.

The variants on three different graph matching settings are denoted by different suffixes:

• GANN-2GM: self-supervised learning graduated assignment neural network for two-grpah matching
• GANN-MGM: self-supervised learning graduated assignment neural network for multi-grpah matching
• GANN-MGM3: self-supervised learning graduated assignment neural network for multi-graph matching with a mixture of modes (this setting is also known as multi-graph matching and clustering in the NeurIPS paper)

GANN-MGM notably surpass supervised learning methods on the relatively small dataset Willow Object Class.

Benchmark Results

Willow Object Class - MGM

experiment config: experiments/vgg16_gann-mgm_willow.yaml

pretrained model: google drive

model	year	remark	Car	Duck	Face	Motorbike	Winebottle	mean
GANN-MGM	2020	self-supervised	0.9600	0.9642	1.0000	1.0000	0.9879	0.9906

File Organization

├── graduated_assignment.py
|   the implementation of the graduated assignment algorithm covering all scenarios
├── model.py
|   the implementation of training/evaluation procedures of GANN-GM/MGM/MGM3
└── model_config.py
    the declaration of model hyperparameters

Credits and Citation

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

@inproceedings{WangNeurIPS20,
  author = {Runzhong Wang and Junchi Yan and Xiaokang Yang},
  title = {Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning},
  booktitle = {Neural Information Processing Systems},
  year = {2020}
}

@article{WangPAMI23,
  title={Unsupervised Learning of Graph Matching With Mixture of Modes Via Discrepancy Minimization},
  author={Wang, Runzhong and Yan, Junchi and Yang, Xiaokang},
  journal={IEEE Transactions of Pattern Analysis and Machine Intelligence},
  year={2023}
}