Achieving Cross Modal Generalization with Multimodal Unified Representation, NeurIPS 2023

This is the Pytorch implementation of our paper:

Achieving Cross Modal Generalization with Multimodal Unified Representation

Yan Xia, Hai Huang, Jieming Zhu, Zhou Zhao

In NeurIPS 2023

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📝Requirements and Installation

• Getting Started

Due to the version conflict between bert_embedding's dependency on NumPy and other libraries, directly installing according to requirements.txt may cause issues. For more details, you can refer to this issue."

git clone https://github.com/haihuangcode/CMG
cd CMG
# You don't actually have to install all the libraries in the txt file, you can choose to install them as needed.
# It is recommended to use Python 3.7, as some libraries used do not support higher versions of Python.
conda create -n your_env_name python=3.7
pip install -r requirements.txt

• Pretrain

cd CMG/code/src
./pretrain.sh

• AVE_downstream

cd CMG/code/src
./ave.sh

• AVVP_downstream

cd CMG/code/src
./avvp.sh

• AVE_AVVP_downstream

cd CMG/code/src
./ave_avvp.sh

• UCF_VGGSOUND_downstream

cd CMG/code/src
./ucf_vggsound.sh

• AVS_downstream

cd CMG/code/AVSBench_downstream/avs_scripts/avs_s4
./train.sh
./test.sh

🎓Cite

If you find this work useful, please consider citing it.

@article{xia2024achieving,
  title={Achieving Cross Modal Generalization with Multimodal Unified Representation},
  author={Xia, Yan and Huang, Hai and Zhu, Jieming and Zhao, Zhou},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}

✏Model Checkpoints And Date Feature

Baidu Disk (pwd: 1234)

• 2023.11.07 Update #1

✏Directory

CMG
├── checkpoint
├── cnt.pkl
├── code
├── data
├── figs
├── paper
├── README.md
└── requirements.txt

✏Note

• For the video and audio feature extraction method, please refer to AVE, text is based on the label to generate a description-focused statement of approximately 10 words in length.
• There is no validation set for the pre-training process, in this paper it is done by testing the performance of each model on the downstream of the AVE, and the model with the best performance tests the rest of the downstream tasks, so the AVE can be regarded as a validation set and the model with the best pre-training appears in the first 5 epochs.
• Pretraining can be performed using just one GPU, such as 4090 or A100. The experimental results in the paper were obtained by running on 4090 or A100. Multi-GPU parallel training yielded poorer model performance, possibly due to issues between the mutual information minimization design in DCID and Pytorch (but this was an early experimental observation, and was not re-verified after the code was finalized, since single GPU pretraining was sufficient).

👍Acknowledgments

Our code is based on AVE, AVVP, PSP, CPSP, VGGSOUND, AVS.