TransZero++

This repository contains the training and testing code for the paper "TransZero++: Cross Attribute-guided Transformer for Zero-Shot Learning" accepted to TPAMI.

Running Environment

The implementation of TransZero++ is mainly based on Python 3.8.8 and PyTorch 1.8.0. To install all required dependencies:

$ pip install -r requirements.txt

We use Weights & Biases (W&B) to keep track and organize the results of experiments. You may need to follow the online documentation of W&B to quickstart.

Download Dataset

We trained the model on three popular ZSL benchmarks: CUB, SUN and AWA2 following the data split of xlsa17. In order to train the TransZero++, you should firstly download these datasets as well as the xlsa17. Then decompress and organize them as follows:

.
├── data
│   ├── CUB/CUB_200_2011/...
│   ├── SUN/images/...
│   ├── AWA2/Animals_with_Attributes2/...
│   └── xlsa17/data/...
└── ···

Visual Features Preprocessing

In this step, you should run the following commands to extract the visual features of three datasets:

$ python preprocessing.py --dataset CUB --compression --device cuda:0
$ python preprocessing.py --dataset SUN --compression --device cuda:0
$ python preprocessing.py --dataset AWA2 --compression --device cuda:0

Training TransZero++ from Scratch

In ./wandb_config, we provide our parameters setting of conventional ZSL (CZSL) and generalized ZSL (GZSL) tasks for CUB, SUN, and AWA2. Please run the following commands to train the TransZero++ from scratch:

$ python train_cub.py   # CUB
$ python train_sun.py   # SUN
$ python train_awa2.py  # AWA2

Note: Please load the corresponding setting when aiming at the CZSL task.

Results

We also provide trained models (Google Drive) on CUB/SUN/AWA2. You can download these .pth files and validate the results in our paper. Please refer to the here for testing codes and usage. Following table shows the results of our released models using various evaluation protocols on three datasets, both in the CZSL and GZSL settings:

Dataset	Acc(CZSL)	U(GZSL)	S(GZSL)	H(GZSL)
CUB	78.3	67.5	73.6	70.4
SUN	67.6	48.6	37.8	42.5
AWA2	72.6	64.6	82.7	72.5

Note: The training of our models and all of the above results are run on a server with an AMD Ryzen 7 5800X CPU, 128GB memory, and an NVIDIA RTX A6000 GPU (48GB).

Citation

If this work is helpful for you, please cite our paper.

@article{Chen2022TransZeropp,
    author    = {Chen, Shiming and Hong, Ziming and Hou, Wenjin and Xie, Guo-Sen and Song, Yibing and Zhao, Jian and You, Xinge and Yan, Shuicheng and Shao, Ling},
    title     = {TransZero++: Cross Attribute-guided Transformer for Zero-Shot Learning},
    booktitle = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
    year      = {2022}
}

References

Parts of our codes based on:

• hbdat/cvpr20_DAZLE
• zhangxuying1004/RSTNet
• shiming-chen/TransZero

Contact

If you have any questions about codes, please don't hesitate to contact us by gchenshiming@gmail.com or hoongzm@gmail.com.