Awesome
Correlation Verification for Image Retrieval
Official Pytorch Implementation of the paper "Correlation Verification for Image Retrieval"<br> accept to CVPR 2022 as an oral presentation <br> by Seongwon Lee, Hongje Seong, Suhyeon Lee, and Euntai Kim<br> Yonsei University
Overall architecture
<p align="middle"> <img src="assets/CVNet_rerank_architecture.jpg"> </p>➡️ 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}
}