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Systematical Investigation for the Openness of CLIP: We design the evaluation protocol and two indicators of extensibility and stability.
CLIP Feature Space Dissecting: We define inter-modal alignment and intra-modal uniformity, two metrics to measure the quality of representations in contrastive learning for the vision-and-language domain.
Retrieval-enhanced prompt engineering (REPE): A simple yet effective method to improve the extensibility and stability of CLIP without fine-tuning.
Installation
For installation and other package requirements, please follow the instructions detailed in INSTALL.md.
Data preparation
Please follow the instructions at DATASETS.md to prepare all datasets.
Pre-trained Models
Please follow the instructions at MODELS.md to prepare all pre-trained models.
Training and Evaluation
Please refer to the RUN.md for detailed instructions on training, evaluating and reproducing the results.
Citation
If you use our work, please consider citing:
@article{Ren2022DelvingIT,
title={Delving into the Openness of {CLIP}},
author={Shuhuai Ren and Lei Li and Xuancheng Ren and Guangxiang Zhao and Xu Sun},
journal={ArXiv},
year={2022},
volume={abs/2206.01986}
}
Contact
If you have any questions, please create an issue on this repository or contact at renshuhuai007@gmail.com.
Acknowledgements
Our code is based on CoOp, clip-retrieval, and DeCLIP repositories. We thank the authors for releasing their code. If you use our model and code, please consider citing these works as well.