Awesome
PSVMA
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Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning This repository contains the reference code for the paper "Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning" accepted to CVPR 2023.
🌈 Model Architecture
📚 Dependencies
Python 3.6.7
PyTorch = 1.7.0
- All experiments are performed with one RTX 3090Ti GPU.
⚡ Prerequisites
- Dataset: please download the dataset, i.e., CUB, AWA2, SUN to the dataset root path on your machine
- Data split: Datasets can be download from Xian et al. (CVPR2017) and take them into dir
../../datasets/
. - Attribute w2v:
extract_attribute_w2v_CUB.py
extract_attribute_w2v_SUN.py
extract_attribute_w2v_AWA2.py
should generate and place it inw2v/
. - Download pretranined vision Transformer as the vision encoder.
🚀 Train & Eval
Before running commands, you can set the hyperparameters in config on different datasets:
config/cub.yaml #CUB
config/sun.yaml #SUN
config/awa2.yaml #AWA2
T rain:
python train.py
Eval:
python test.py
You can test our trained model: CUB, AwA2, SUN.
❗ Cite:
If this work is helpful for you, please cite our paper.
@InProceedings{Liu_2023_CVPR,
author = {Liu, Man and Li, Feng and Zhang, Chunjie and Wei, Yunchao and Bai, Huihui and Zhao, Yao},
title = {Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2023},
pages = {15337-15346}
}
📕 Ackowledgement
We thank the following repos providing helpful components in our work. GEM-ZSL