Awesome
FREE
This repository contains the reference code for the paper "FREE: Feature Refinement for Generalized Zero-Shot Learning" accepted to ICCV 2021. [arXiv][Paper]
1. Preparing Dataset and Model
Datasets can be download from Xian et al. (CVPR2017) and take them into dir data
.
Requirements
The code implementation of FREE mainly based on PyTorch. All of our experiments run in Python 3.8.8.
2. Runing
Before running commands, you can set the hyperparameters in config.py. Please run the following commands and testing FREE on different datasets:
$ python ./image-scripts/run-cub.py #CUB
$ python ./image-scripts/run-sun.py #SUN
$ python ./image-scripts/run-flo.py #FLO
$ python ./image-scripts/run-awa1.py #AWA1
$ python ./image-scripts/run-awa2.py #AWA2
Note: All of above results are run on a server with one GPU (Nvidia 1080Ti).
3. Citation
If this work is helpful for you, please cite our paper.
@InProceedings{Chen_2021_ICCV,
author = {Chen, Shiming and Wang, Wenjie and Xia, Beihao and Peng, Qinmu and You, Xinge and Zheng, Feng and Shao, Ling},
title = {FREE: Feature Refinement for Generalized Zero-Shot Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
year = {2021},
pages = {122-131}
}
4. Ackowledgement
We thank the following repos providing helpful components in our work.