Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval

This project is our implementation of Semantic-Aware Knowledge prEservation (SAKE) for zero-shot sketch-based image retrieval. More detailed descriptions and experimental results could be found in the paper.

If you find this project helpful, please consider citing our paper.

@article{liu2019semantic,
  author    = {Liu, Qing and Xie, Lingxi and Wang, Huiyu and Yuille, Alan},
  title     = {Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval},
  journal   = {arXiv preprint arXiv:1904.03208},
  year      = {2019},
}

Dataset

Download the resized TUBerlin Ext and Sketchy Ext dataset and our zeroshot train/test split files from here. Put the unzipped folder to the same directory of this project.

Training

CSE-ResNet50 model with 64-d features on TUBerlin Ext:

python train_cse_resnet_tuberlin_ext.py

CSE-ResNet50 model with 64-d features on Sketchy Ext:

python train_cse_resnet_sketchy_ext.py

Testing