This is the source code of ICMR 2020 paper "Deep Adversarial Discrete Hashing for Cross-Modal Retrieval".
- Python 3.7.2
- Pytorch 1.6.0
- torchvision 0.7.0
- CUDA 10.1 and cuDNN 7.6.4
##Install required libraries##
pip install -r requirements.txt##before train##
python -m visdom.server
##custom train##
python main.py train [--flag DATASET] [--batch_size BATCH_SIZE] [--lr LR]
[--valid_freq FRQ] [--bit CODE_LENGTH] [--lamb LAMBDA]
[--alpha ALPHA] [--gamma GAMMA] [--beta BETA] [--mu MU]
[--margin MARGIN] [--max_epoch MAX_ITER] [--vis_env VIS_ENV]
[--device DEVICE] [--dropout DROPOUT]
optional arguments:
--flag DATASET Dataset name.('mir' or 'nus')
--batch_size BATCH_SIZE
Batch size.(default: 128)
--lr LR Learning rate.(default: 5e-5)
--valid_freq FRQ Valid frequency.(default: 1)
--bit CODE_LENGTH
Binary hash code length.(default: 16,32,64)
--lamb LAMBDA Hyper-parameter.(default: 1)
--alpha ALPHA Hyper-parameter.(default: 10)
--gamma GAMMA Hyper-parameter.(default: 1)
--beta BETA Hyper-parameter.(default: 1)
--mu MU Hyper-parameter.(default: 1e-5)
--margin MARGIN Hyper-parameter.(default: 0.4)
--max_epoch MAX_ITER Number of iterations.(default: 300)
--vis_env VIS_ENV Visdom environment name.(default: 'main')
--device DEVICE If use gpu.(default: 'cuda:0')
--dropout DROPOUT If use dropout.(default: False)
##use our settings##
bash run_mir.sh CODE_LENGTH
bash run_nus.sh CODE_LENGTH##test##
python main.py test [--flag DATASET] [--bit CODE_LENGTH]- CNN-F:
-Google: https://drive.google.com/file/d/1rkjOPzcyXFj_fpqPKqt-R0Vuvd9WPadM/view?usp=sharing
-Baidu: https://pan.baidu.com/s/17R7t1qKNskIDWzLhALYFPg 提取码：cou4 - NUS-WIDE:
-Google: https://drive.google.com/file/d/125G-B7sIQPVIcRk4W7qR-tkc6gcBP-mX/view?usp=sharing
-Baidu: https://pan.baidu.com/s/1Hb4FzOOxqJjR4tDAP5C8rw 提取码：v60x - MIRFlickr25K:
-Google: https://drive.google.com/file/d/1Eca2meBpmhnfezkUVqQJo0tJnEpwQwi2/view?usp=sharing
-Baidu: https://pan.baidu.com/s/14JrUH2AdnvDV1ezs0Qxc5w 提取码：22fs
Note: We extracted 4096-d features of the images in the datasets using CNN-F pre-trained on ImageNet.
Our codes were modified from the implementation of "Adversary Guided Asymmetric Hashing for Cross-Modal Retrieval", written by Wen Gu. Please cite the two papers (AGAH and DADH) when you use the codes.
@inproceedings{Bai2020,
author={Cong Bai, Chao Zeng, Qing Ma, Jinglin Zhang and Shengyong Chen.},
booktitle={Proceedings of the 2020 on International Conference on Multimedia Retrieval},
pages={525-531},
title={Deep Adversarial Discrete Hashing for Cross-Modal Retrieval},
year={2020},
}
@inproceedings{Gu2019,
author = {Gu, Wen and Gu, Xiaoyan and Gu, Jingzi and Li, Bo and Xiong, Zhi and Wang, Weiping},
booktitle = {Proceedings of the ACM International Conference on Multimedia Retrieval},
pages = {159--167},
title = {{Adversary guided asymmetric hashing for cross-modal retrieval}},
year = {2019}
}
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