aaai17-cdq

This is the Tensorflow (Version 0.11) implementation of AAAI-17 paper "Collective Deep Quantization for Efficient Cross-modal Retrieval". The descriptions of files in this directory are listed below:

• cdq.py: contains the main implementation of the proposed approach cdq.
• train_script.py: gives an example to show how to train cdq model.
• validation_script.py: gives an example to show how to evaluate the trained quantization model.
• run_cdq.sh: gives an example to show the full procedure of training and evaluating the proposed approach cdq.

Data Preparation

In data/nuswide/train.txt and data/nuswide/text_train.txt, we give an example to show how to prepare image/text training data. In data/nuswide/test.txt, data/nuswide/text_test.txt, data/nuswide/database.txt and data/nuswide/text_database.txt, the list of testing and database images/texts could be processed during predicting procedure.

Training Model and Predicting

The AlexNet is used as the pre-trained model. If the NUS_WIDE dataset and pre-trained caffemodel is prepared, the example can be run with the following command:

"./run_cdq.sh"

Citation

@inproceedings{DBLP:conf/aaai/CaoL0L17,
  author    = {Yue Cao and
               Mingsheng Long and
               Jianmin Wang and
               Shichen Liu},
  title     = {Collective Deep Quantization for Efficient Cross-Modal Retrieval},
  booktitle = {Proceedings of the Thirty-First {AAAI} Conference on Artificial Intelligence,
               February 4-9, 2017, San Francisco, California, {USA.}},
  pages     = {3974--3980},
  year      = {2017},
  crossref  = {DBLP:conf/aaai/2017},
  url       = {http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14499},
  timestamp = {Mon, 06 Mar 2017 11:36:24 +0100},
  biburl    = {http://dblp2.uni-trier.de/rec/bib/conf/aaai/CaoL0L17},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}