TransZero [arXiv]
This repository contains the training and testing code for the paper "TransZero: Attribute-guided Transformer for Zero-Shot Learning" accepted to AAAI 2022.
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
Additionally, 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. To run these codes, sign up an online account to track experiments or create a local wandb server using docker (recommended).
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/...
└── ···
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
In ./wandb_config, we provide our parameters setting of conventional ZSL (CZSL) and generalized ZSL (GZSL) tasks for CUB, SUN, and AWA2. You can 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.
We also provide trained models (Google Drive) on three datasets. You can download these .pth files and validate the results in our paper. Please refer to the test branch 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 | 76.8 | 69.3 | 68.3 | 68.8 |
| SUN | 65.6 | 52.6 | 33.4 | 40.8 |
| AWA2 | 70.1 | 61.3 | 82.3 | 70.2 |
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).
If this work is helpful for you, please cite our paper.
@InProceedings{Chen2022TransZero,
author = {Chen, Shiming and Hong, Ziming and Liu, Yang and Xie, Guo-Sen and Sun, Baigui and Li, Hao and Peng, Qinmu and Lu, Ke and You, Xinge},
title = {TransZero: Attribute-guided Transformer for Zero-Shot Learning},
booktitle = {Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI)},
year = {2022}
}
Parts of our codes based on:
If you have any questions about codes, please don't hesitate to contact us by gchenshiming@gmail.com or hoongzm@gmail.com.
