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Cross_Modal_GCN

Semi-supervised Graph Convolutional Hashing Network For Large-Scale Cross-Modal Retrieval (Shen, Zhai et al., ICIP 2020) framework

Overview

Here we provide the implementation of our model in TensorFlow, along with all experimental datasets. The repository is organised as follows:

  • data/ contains the necessary dataset files. Here, we have three datasets including MIRFLICKR-25K, NUS-WIDE-10K and Wiki, which can be downloaded from pan.baidu.com: link: https://pan.baidu.com/s/1DlIxCvT_3vRKphMydnljVQ code: b6be.
  • train_semi_nuswide.py execute a full training run on NUS-WIDE-10K dataset.
  • train_semi_wiki.py execute a full training run on Wiki dataset.

Dependencies

The script has been tested running under Python 2.7, with the following packages installed (along with their dependencies):

  • numpy==1.15.4
  • tensorflow-gpu==1.12.0

In addition, CUDA 9.0 and cuDNN 7 have been used.