README.md

CIE-H

Our implementation of the following paper:

• Tianshu Yu, Runzhong Wang, Junchi Yan, Baoxin Li. "Learning deep graph matching with channel-independent embedding and Hungarian attention." ICLR 2020. [paper]

CIE-H follows the CNN-GNN-metric-Sinkhorn pipeline proposed by PCA-GM, and it improves PCA-GM from two aspects:

1. A channel-independent edge embedding module for better graph feature extraction;
2. A Hungarian Attention module that dynamically constructs a structured and sparsely connected layer, taking into account the most contributing matching pairs as hard attention during training.

Benchmark Results

PascalVOC - 2GM

experiment config: experiments/vgg16_cie_voc.yaml

pretrained model: google drive

model	year	aero	bike	bird	boat	bottle	bus	car	cat	chair	cow	table	dog	horse	mbkie	person	plant	sheep	sofa	train	tv	mean
CIE-H	2020	0.5250	0.6858	0.7015	0.5706	0.8207	0.7700	0.7073	0.7313	0.4383	0.6994	0.6237	0.7018	0.7031	0.6641	0.4763	0.8525	0.7172	0.6400	0.8385	0.9168	0.6892

Willow Object Class - 2GM

experiment config: experiments/vgg16_cie_willow.yaml

pretrained model: google drive

model	year	remark	Car	Duck	Face	Motorbike	Winebottle	mean
CIE-H	2020	-	0.8581	0.8206	0.9994	0.8836	0.8871	0.8898

SPair-71k - 2GM

experiment config: experiments/vgg16_cie_spair71k.yaml

pretrained model: google drive

model	year	aero	bike	bird	boat	bottle	bus	car	cat	chair	cow	dog	horse	mtbike	person	plant	sheep	train	tv	mean
CIE-H	2020	0.7146	0.5710	0.8168	0.5672	0.6794	0.8246	0.7339	0.7449	0.6259	0.7804	0.6872	0.6626	0.7374	0.6604	0.9246	0.6717	0.8228	0.9751	0.7334

File Organization

├── model.py
|   the implementation of training/evaluation procedures of BBGM
└── model_config.py
    the declaration of model hyperparameters

some files are borrowed from models/PCA

Credits and Citation

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

@inproceedings{YuICLR20,
  title={Learning deep graph matching with channel-independent embedding and Hungarian attention},
  author={Yu, Tianshu and Wang, Runzhong and Yan, Junchi and Li, Baoxin},
  booktitle={International Conference on Learning Representations},
  year={2020}
}