https://github.com/vision-cair/cizslv2
CIZSL++: Creativity Inspired Generative Zero-Shot Learning. T-PAMI under review.
https://github.com/vision-cair/cizslv2
attributes creativity generative-models unseen-classes-understanding vision-and-language zero-shot-learning
Last synced: 8 months ago
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CIZSL++: Creativity Inspired Generative Zero-Shot Learning. T-PAMI under review.
- Host: GitHub
- URL: https://github.com/vision-cair/cizslv2
- Owner: Vision-CAIR
- Created: 2020-09-24T09:19:16.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2021-02-08T07:10:01.000Z (almost 5 years ago)
- Last Synced: 2025-03-26T06:11:40.574Z (8 months ago)
- Topics: attributes, creativity, generative-models, unseen-classes-understanding, vision-and-language, zero-shot-learning
- Language: Python
- Homepage:
- Size: 13.8 MB
- Stars: 6
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# CIZSLv2
CIZSL++: Creativity Inspired Generative Zero-shot Learning, Mohamed Elhoseiny, Kai Yi, Mohamed Elfeki, Arxiv, 2020
# Requirements
Python 3.6
Pytorch 1.6
sklearn, scipy, matplotlib, numpy, random, copy
# Processed Feature Data
You can download the text-based dataset at [dataset CUBird and NABird](https://www.dropbox.com/s/9qovr86kgogkl6r/CUB_NAB_Data.zip). For attribute-based data, you can access to [here](https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/research/zero-shot-learning/zero-shot-learning-the-good-the-bad-and-the-ugly).
Please put the uncompressed data to the folder "data".
# Reproduce CIZSLv2 Best Model
```
python train_cizslv2.py --dataset 'CUB' --splitmode 'easy' --creativity_weight 1 --exp_name 'cizslv2'
python train_cizslv2.py --dataset 'CUB' --splitmode 'hard' --creativity_weight 0.1 --exp_name 'cizslv2'
python train_cizslv2.py --dataset 'NAB' --splitmode 'easy' --creativity_weight 0.001 --exp_name 'cizslv2'
python train_cizslv2.py --dataset 'NAB' --splitmode 'hard' --creativity_weight 1 --exp_name 'cizslv2'
```
# Reference
- Yizhe Zhu, Mohamed Elhoseiny, Bingchen Liu, Xi Peng and Ahmed Elgammal "A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts", CVPR, 2018
- Mohamed Elhoseiny, Mohamed Elfeki, Creativity Inspired Zero Shot Learning, Thirty-sixth International Conference on Computer Vision (ICCV), 2019
If you find this code is useful, please cite:
```
@article{elhoseiny2021cizsl++,
title={CIZSL++: Creativity Inspired Generative Zero-Shot Learning},
author={Elhoseiny, Mohamed and Yi, Kai and Elfeki, Mohamed},
journal={arXiv preprint arXiv:2101.00173},
year={2021}
}
```