[arXiv]
TransZeroThis repository contains the testing code for the paper "TransZero: Attribute-guided Transformer for Zero-Shot Learning" accepted to AAAI 2022. We will release all codes of this work later.
Preparing Dataset and Model
We provide trained models (Google Drive) on three different datasets: CUB, SUN, AWA2 in the CZSL/GZSL setting. You can download model files as well as corresponding datasets, and organize them as follows:
.
├── saved_model
│ ├── TransZero_CUB_CZSL.pth
│ ├── TransZero_CUB_GZSL.pth
│ ├── TransZero_SUN_CZSL.pth
│ ├── TransZero_SUN_GZSL.pth
│ ├── TransZero_AWA2_CZSL.pth
│ └── TransZero_AWA2_GZSL.pth
├── data
│ ├── CUB/
│ ├── SUN/
│ └── AWA2/
└── ···
Requirements
The code implementation of TransZero mainly based on PyTorch. All of our experiments run and test in Python 3.8.8. To install all required dependencies:
$ pip install -r requirements.txt
Runing
Runing following commands and testing TransZero on different dataset:
CUB Dataset:
$ python test.py --config config/CUB_CZSL.json # CZSL Setting
$ python test.py --config config/CUB_GZSL.json # GZSL Setting
SUN Dataset:
$ python test.py --config config/SUN_CZSL.json # CZSL Setting
$ python test.py --config config/SUN_GZSL.json # GZSL Setting
AWA2 Dataset:
$ python test.py --config config/AWA2_CZSL.json # CZSL Setting
$ python test.py --config config/AWA2_GZSL.json # GZSL Setting
Results
Results of our released models using various evaluation protocols on three datasets, both in the conventional ZSL (CZSL) and generalized ZSL (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: All of above results are run on a server with an AMD Ryzen 7 5800X CPU and a NVIDIA RTX A6000 GPU.
Citation
If this work is helpful for you, please cite our paper.
@InProceedings{Chen2021TransZero,
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}
}
References
Parts of our codes based on:
Contact
If you have any questions about codes, please don't hesitate to contact us by [email protected] or [email protected].