QC-DGM

This is the official PyTorch implementation and models for our CVPR 2021 paper: Deep Graph Matching under Quadratic Constraint.

It also contains the configuration files to reproduce the results of qc-DGM_1 reported in the paper on Pascal VOC Keypoint and Willow Object Class dataset.

Get started

1. pytorch (GPU version) >= 1.1
2. ninja-build: apt-get install ninja-build
3. python packages: pip install tensorboardX scipy easydict pyyaml
4. Download dataset:
   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.

Training

1. Run training and evaluation on Pascal VOC Keypoint:

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

   or you could replace the default ./experiments/QCDGM_voc.yaml with path to your own configuration file.

2. Run training and evaluation on Willow Object Class dataset:

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

   or you could replace the default ./experiments/QCDGM_willow.yaml with path to your own configuration file.

Evaluation

1. Run evaluation on Pascal VOC Keypoint on epoch k:

   python eval.py --cfg ./experiments/QCDGM_voc.yaml --epoch k

   or you could replace the default ./experiments/QCDGM_voc.yaml with path to your own configuration file.

2. Run evaluation on Willow Object Class dataset on epoch k:

   python eval.py --cfg ./experiments/QCDGM_willow.yaml --epoch k

   or you could replace the default ./experiments/QCDGM_voc.yaml with path to your own configuration file.

Results

We report the performance on Pascal VOC Keypoint and Willow Object Class datasets.

Pascal VOC Keypoint

Willow Object Class

Citation

@InProceedings{Gao_2021_CVPR,
author = {Gao, Quankai and Wang, Fudong and Xue, Nan and Yu, Jin-Gang and Xia, Gui-Song},
title = {Deep Graph Matching under Quadratic Constraint},
booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)},
year = {2021}
}