This repository contains the public release of the Python implementation of

Attribute Attention for Semantic Disambiguation in Zero-Shot Learning

Yang Liu, Jishun Guo, Deng Cai, Xiaofei He.

If you use this code or find this work useful for your research, please cite:

@inproceedings{Liu_2019_ICCV,
  title={Attribute Attention for Semantic Disambiguation in Zero-Shot Learning},
  author={Liu, Yang and Guo, Jishun and Cai, Deng and He, Xiaofei},
  booktitle={The IEEE International Conference on Computer Vision (ICCV)},
  month={Oct},
  year={2019}
}

Implemented and tested on Ubuntu 16.04 with Python 3.6 and Pytorch 1.0.1. Experiments are conducted on AwA2, CUB and SUN datasets.

We use AwA2 file format as default detailed in ./data/ folder and images should be downloaded and renamed as ./data/*/JPEGImages. It is important to note that several cusomization work should be done for SUN dataset to maintain the same file format.

Use experiments/run_trainer.py to train the network. Run help to view all the possible parameters. We provide several config files under ./configs/ folder. Example usage:

python experiments/run_trainer.py --cfg ./configs/self_adaptation/VGG19_AwA2_PS_C.yaml

Feel free to download the reported checkpoints.

Use experiments/run_evaluator.py to evaluate the network with self_adaptation and experiments/run_evaluator_hybrid.py with hybrid method.