Correlation Verification for Image Retrieval

Official Pytorch Implementation of the paper "Correlation Verification for Image Retrieval"
accept to CVPR 2022 as an oral presentation
by Seongwon Lee, Hongje Seong, Suhyeon Lee, and Euntai Kim
Yonsei University

Overall architecture

➡️ Guide to Our Code

Data preparation

Download ROxford5k and RParis6k. Unzip the files and make the directory structures as follows.

revisitiop
 └ data
   └ datasets
     └ roxford5k
       └ gnd_roxford5k.pkl
       └ jpg
         └ ...
     └ rparis6k
       └ gnd_rparis6k.pkl
       └ jpg
         └ ...

Pretrained models

You can download our pretrained models from Google Drive.

Testing

For ResNet-50 model, run the command

python test.py MODEL.DEPTH 50 TEST.WEIGHTS <path-to-R50-pretrained-model> TEST.DATA_DIR <path_to_revisitop>/data/datasets

and for ResNet-101 model, run the command

python test.py MODEL.DEPTH 101 TEST.WEIGHTS <path-to-R101-pretrained-model> TEST.DATA_DIR <path_to_revisitop>/data/datasets

🙏 Acknowledgments

Our pytorch implementation is derived from HSNet, Revisiting Oxford and Paris and DELG-pytorch. We thank for these great works and repos.

✏️ Citation

If you find our paper useful in your research, please cite us using the following entry:

@InProceedings{lee2022cvnet, 
    author    = {Lee, Seongwon and Seong, Hongje and Lee, Suhyeon and Kim, Euntai},
    title     = {Correlation Verification for Image Retrieval},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {5374-5384}
}