Adversary Guided Asymmetric Hashing for Cross-Modal Retrieval
Code for the paper Adversary Guided Asymmetric Hashing for Cross-Modal Retrieval (ICMR 2019 Best Student Paper).
Requirements
- Python >= 3.5
- PyTorch >= 1.0.0
Usage
Help
python main.py helpYou will get the following help information:
========================::HELP::=========================
usage : python file.py <function> [--args=value]
<function> := train | test | help
example:
python main.py train --lr=0.01
python main.py help
avaiable args (default value):
load_model_path: None
pretrain_model_path: ./data/imagenet-vgg-f.mat
vis_env: None
vis_port: 8097
dataset: flickr25k
data_path: ./data/FLICKR-25K.mat
db_size: 18015
num_label: 24
tag_dim: 1386
query_size: 2000
training_size: 10000
batch_size: 128
emb_dim: 512
valid: True
valid_freq: 2
max_epoch: 300
bit: 64
lr: 0.0001
device: cuda:1
alpha: 1
beta: 0
gamma: 0.001
eta: 1
mu: 1
delta: 0.5
lambd: 0.8
margin: 0.3
debug: False
data_enhance: False
========================::HELP::=========================
Train & Test
Train and test:
python main.py trainFor test only:
python main.py testDatasets
Coming soon...
Framework
Result
Citing AGAH
@inproceedings{gu2019adversary,
title={Adversary Guided Asymmetric Hashing for Cross-Modal Retrieval},
author={Gu, Wen and Gu, Xiaoyan and Gu, Jingzi and Li, Bo and Xiong, Zhi and Wang, Weiping},
booktitle={Proceedings of the 2019 on International Conference on Multimedia Retrieval},
pages={159--167},
year={2019},
organization={ACM}
}