Tuning Multi-mode Token-level Prompt Alignment across Modalities [NeurIPS 2023]

This is the official implementation of our paper Tuning Multi-mode Token-level Prompt Alignment across Modalities in NeurIPS 2023.

The proposed ALIGN algorithm aims to learn multiple prompts in both textual and visual domains. Given the M visual prompts and N textual prompts, ALIGN first views the label/image as discrete distributions over the the M and N supporting, and each distribution itself can further be modeled as a discrete distribution over its model-specific token-level space. ALIGN applies the Prompt-level OT and Token-level OT to align those two domains.

TODO

Due to some ddls, we will add more details about the training scripts and results soon.

Getting Started

Install

• Clone this repo:

git clone https://github.com/wds2014/ALIGN.git
cd ALIGN

• Please follow the INSTALL.md to build the python environment.

Dataset

• Datasets in our paper

The datasets we used is as the same as previous works (CoOp and MAPLE). Please follow the DATASETS.md to prepare all datasets.

Training

• Easy to train:

cd scripts/mmp
bash base_to_new_train.sh

Change the DATASET and SEED in the .sh file to train our model in different datasets and seeds.

Citation

If you find this repo useful to your project, please consider to cite it with following bib:

@article{wang2023tuning,
  title={Tuning Multi-mode Token-level Prompt Alignment across Modalities},
  author={Wang, Dongsheng and Li, Miaoge and Liu, Xinyang and Xu, MingSheng and Chen, Bo and Zhang, Hanwang},
  journal={arXiv preprint arXiv:2309.13847},
  year={2023}
}

Acknowledgements

Our code is modified based on CoOp and MAPLE repository. We thank the authors for releasing their code.