Modeling Inter and Intra-Class Relations in the Triplet Loss for Zero-Shot Learning

This code allows to reprodure the results published in the ICCV'19 article and its supplementary material.

All the material is contained in a unique jupyter notebook that you run with:

jupyter notebook Results.ipynb

Dependencies are:

• python 2.7.9
• torch 1.1.0
• sklearn 0.19.2
• numpy 1.14.3
• pandas 0.23.0
• matplotlib 2.2.2
• scipy 1.0.0
• jupyter

Get the CUB dataset

To visualize the images, you need to download the CUB 200 2011 dataset. The corresponding project page gives the reference to cite if you use it in your work.

You need to decompress the dataset into CUB_200_2011/ then change the path in data/df.cvs accordingly, e.g:

perl -i.old -p -e  's#/scratch_global/yannick#'$PWD'#' data/df.csv

Citation

Please cite the following article if you use this code in your work:

Y. Le Cacheux, H. Le Borgne and M. Crucianu. Modeling Inter and Intra-Class Relations in the Triplet Loss for Zero-Shot Learning. In Proceedings of the IEEE International Conference on Computer Vision, ICCV, Seoul, Korea, Oct. 27 - Nov. 2, 2019

@inproceedings{lecacheux2019zsl,
     title  = {Modeling Inter and Intra-Class Relations in the Triplet Loss for Zero-Shot Learning},
     author = {Le Cacheux, Yannick and Le Borgne, Herv{\'e} and Crucianu, Michel},
  booktitle = {the IEEE International Conference on Computer Vision (ICCV)},
      month = {October},
     series = {ICCV},
       year = {2019}
 }