Source code for TPAMI'24 paper "Cross-Modal Hashing Method with Properties of Hamming Space: A New Perspective"
Please refer to the provided link to download the dataset, create a data folder and update data path in settings.py.
You can directly run the file
python train.py --Bit 16 --GID 0 --DS 0
to get the results.
Modify the settings.py line 7
EVAL = True
You can downlod the trained models via following links (have been updated):
| Dataset | Hash Bit | Downlod |
|---|---|---|
| MIR | 16 | link |
| MIR | 32 | link |
| MIR | 64 | link |
| MIR | 128 | link |
| NUS | 16 | link |
| NUS | 32 | link |
| NUS | 64 | link |
| NUS | 128 | link |
If you find SCH useful in your research, please consider citing:
@article{hu2024cross,
title={Cross-Modal Hashing Method with Properties of Hamming Space: A New Perspective},
author={Hu, Zhikai and Cheung, Yiu-ming and Li, Mengke and Lan, Weichao},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2024},
volume={46},
number={12},
pages={7636-7650},
doi={10.1109/TPAMI.2024.3392763}}
}