Towards Open-World Co-Salient Object Detection with Generative Uncertainty-aware Group Selective Exchange-Masking
Thank you very much for your interest in our work.
The OWCoSOD datasets (OWCoSal, OWCoSOD, OWCoCA) can be downloaded from the link
https://pan.baidu.com/s/11MKqPIRP58p8lvz7x9AF2Q, and the password is 1310.
The results of our method on OWCoSal, OWCoSOD, OWCoCA can be downloaded from
https://pan.baidu.com/s/1Yw3jN_cxkRgR47PSiIclPw, and the password is 1310.
The contour can be generated by draw_contour.py.
The results of our method on CoSal2015, CoSOD3k, CoCA, MSRC, and iCoseg are available, and they can be downloaded from the link
https://pan.baidu.com/s/1uRwH5Y1HgDvxWd9gwRoR9g, and the password is 1310.
The pretrained_model and weights can be downloaded from the link
https://pan.baidu.com/s/1_FVoR6QP6FeQCZEGxgyZog and the password is 1310.
Putting the pretrained_model into ./pretrained_model and weights files into ./result/models and run coseg_test.py can get the results.
The link of the eval toolbox is: https://github.com/zzhanghub/eval-co-sod, we are very grateful for their contributions.
- Python 3.8
- Pytorch 2.1.1(CUDA 12.1 build).
Please see requirements.txt
for all the other requirements.
When you initially train the method, you need firstly train vqvae and pixelcnn
Train vqvae
python train_VQVAE.py
Train pixelcnn
python train_pixelcnn.py
Train our Method
python main.py --data_root /home/dell/Codes/IJCV/data/ --trainset coco-seg --n_embedding 128 --n_dim 384 --color_level 128 --linear_dim 128 --save_vqvae ./checkpoints/vqvae --save_gen_model ./checkpoints/vqvae
Test on CoCA
python coseg_test.py --n_embedding 128 --n_dim 384 --color_level 128 --linear_dim 128
If you find our work helpful, you can cite our paper
@inproceedings{wu2023co,
title={Co-Salient Object Detection With Uncertainty-Aware Group Exchange-Masking},
author={Wu, Yang and Song, Huihui and Liu, Bo and Zhang, Kaihua and Liu, Dong},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={19639--19648},
year={2023}
}
@article{wu2023towards,
title={Towards Open-World Co-Salient Object Detection with Generative Uncertainty-aware Group Selective Exchange-Masking},
author={Wu, Yang and Hu, Shenglong and Song, Huihui and Zhang, Kaihua and Liu, Bo and Liu, Dong},
journal={arXiv preprint arXiv:2310.10264},
year={2023}
}