Awesome
CATs++: Boosting Cost Aggregation with Convolutions and Transformers (TPAMI'22)
For more information, check out the paper on [arXiv]. Also check out project page here [Project Page]
Network
Our model is illustrated below:
Environment Settings
git clone https://github.com/KU-CVLAB/CATs-PlusPlus.git
cd CATs-PlusPlus
conda env create -f environment.yml
Evaluation
- Download pre-trained weights on Link
- All datasets are automatically downloaded into directory specified by argument
datapath
Result on SPair-71k:
python test.py --pretrained "/path_to_pretrained_model/spair" --benchmark spair
Results on PF-PASCAL:
python test.py --pretrained "/path_to_pretrained_model/pfpascal" --benchmark pfpascal
Results on PF-WILLOW:
python test.py --pretrained "/path_to_pretrained_model/pfpascal" --benchmark pfwillow --thres {bbox|bbox-kp}
Acknowledgement <a name="Acknowledgement"></a>
We borrow code from public projects (huge thanks to all the projects). We mainly borrow code from DHPF, GLU-Net, and CATs.
BibTeX
If you find this research useful, please consider citing:
@article{cho2022cats++,
title={CATs++: Boosting Cost Aggregation with Convolutions and Transformers},
author={Cho, Seokju and Hong, Sunghwan and Kim, Seungryong},
journal={arXiv preprint arXiv:2202.06817},
year={2022}
}