The official implementation of the AAAI2023 paper:

Deconstructed Generation-Based Zero-Shot Model

Dubing Chen, Yuming Shen, Haofeng Zhang, Philip H.S. Torr

Dependencies

Codes released in this work are trained and tested on:

• Ubuntu Linux
• Python 3.8.15
• Pytorch 1.13.0
• NVIDIA CUDA 11.6
• 1x NVIDIA GeForce rtx 2080 ti GPU

Prerequisites

• Dataset: Please download the dataset, and change --dataroot in config.py to your local path. Please refer to SDGZSL for the finetuned features.
• Attribute: The attributes for AWA2, SUN, and APY are available in the datasets. Please download the 1024-D CUB semantic and save it to the data path.

Train and Test

Please run the scripts in ./scripts to reproduce the results in the paper, e.g.,

sh ./scripts/AWA2.sh

Citation

If you recognize our work, please cite:

@inproceedings{chen2023deconstructed,
            title={Deconstructed Generation-Based Zero-Shot Model},
            author={Chen, Dubing and Shen, Yuming and Zhang, Haofeng and Torr, Philip H.S.},
            booktitle={Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-23)},
            year={2023}
          }

Acknowledgment

Our implementation is inspired by f-CLSWGAN. We appreciate the authors for sharing it as an open source.