Codes for the ICCV'21 paper "FREE: Feature Refinement for Generalized Zero-Shot Learning"

Related tags

Deep Learning zero-shot-learning feature-refinement cross-dataset-bias
Overview

FREE

This repository contains the reference code for the paper "FREE: Feature Refinement for Generalized Zero-Shot Learning". [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.

  1. TF-VAEGAN
  2. cycle-CLSWGAN
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Comments
  • FREE VS tfvaegan

    FREE VS tfvaegan

    In the process of reading your paper, I always feel like tfvaegan. I can confirm from your code that you refer to tfvaegan. Except you removed the feedback module of tfvaegan and proposed SAMC-loss. Or from your code, it can be said that the feedback module is replaced with SAMC-loss. Could the author point out other differences between your paper and eccv2020: tfvaegan . The FR you proposed is basically the same as tfvaegan's decoder?

    opened by in-my-heart 1
  • HELP: I run the code, but got the following error,can you help me?

    HELP: I run the code, but got the following error,can you help me?

    Traceback (most recent call last): File "C:/Users/Desktop/FREE-main/train_free.py", line 237, in fake = netG(z, c=input_attv) File "D:\PyCharm\Anaconda\envs\Python 3_8\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl return forward_call(*input, **kwargs) File "C:\Users\14367\Desktop\FREE-main\model.py", line 59, in forward x1 = self.lrelu(self.fc1(z)) File "D:\PyCharm\Anaconda\envs\Python 3_8\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl return forward_call(*input, **kwargs) File "D:\PyCharm\Anaconda\envs\Python 3_8\lib\site-packages\torch\nn\modules\linear.py", line 114, in forward return F.linear(input, self.weight, self.bias) RuntimeError: mat1 and mat2 shapes cannot be multiplied (64x1336 and 2048x4096)

    opened by ChocalateWZLH 0
Owner
Shiming Chen
Interest: Generative modeling and learning, zero-shot learning, image retrieval, domain adaptation
Shiming Chen
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