This is the source code of our TOMM 2019 paper "CM-GANs: Cross-modal Generative Adversarial Networks for Common Representation Learning", Please cite the following paper if you find our code useful.
Yuxin Peng, Jinwei Qi, "CM-GANs: Cross-modal Generative Adversarial Networks for Common Representation Learning", ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Vol.15, No.1, pp.22:1-22:24, 2019. [PDF]
Our code is based on torch, and tested on Ubuntu 14.04.5 LTS, Lua 5.1.
Data Preparation: we use pascal dataset as example, and the data should be put in ./data/.
The data files can be download from the link and unzipped to the above path.
run sh run.sh to train models, extract features and calculate mAP.
If you are interested in cross-media retrieval, you can check our recently published overview paper on IEEE TCSVT:
Yuxin Peng, Xin Huang, and Yunzhen Zhao, "An Overview of Cross-media Retrieval: Concepts, Methodologies, Benchmarks and Challenges", IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), Vol.28, No.9, pp.2372-2385, 2018. [PDF]
Welcome to our Benchmark Website and Laboratory Homepage for more information about our papers, source codes, and datasets.