🔥🔥 BBFN has won the best paper award honourable mention at ICMI 2021!

This repository contains official implementation of the paper: Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis (ICMI 2021)

💎 If you would be interested in other multimodal works in our DeCLaRe Lab, please visit the clustered repository

Overview of our Bi-Bimodal Fusion Network (BBFN). It learns two text-related pairs of representations, text-acoustic and text-visual by enforcing each pair of modalities to complement mutually. Finally, the four (two pairs) head representations are concatenated to generate the final prediction.

A single complementation layer: two identical pipelines (left and right) propagate the main modality and fuse that with complementary modality with regularization and gated control.

Results on the test set of CMU-MOSI and CMU-MOSEI dataset. Notation: △ indicates results in the corresponding line are excerpted from previous papers; † means the results are reproduced with publicly visible source code and applicable hyperparameter setting; ‡ shows the results have experienced paired t-test with 𝑝 < 0.05 and demonstrate significant improvement over MISA, the state-of-the-art model.

1. Set up conda environemnt

conda env create -f environment.yml
conda activate BBFN

2. Install CMU Multimodal SDK

3. Set sdk_dir in src/config.py to the path of CMU-MultimodalSDK

4. Train the model

cd src
python main.py --dataset <dataset_name> --data_path <path_to_dataset>

We provide a script scripts/run.sh for your reference.

Please cite our paper if you find our work useful for your research:

@article{han2021bi,
  title={Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis},
  author={Han, Wei and Chen, Hui and Gelbukh, Alexander and Zadeh, Amir and Morency, Louis-philippe and Poria, Soujanya},
  journal={ICMI 2021},
  year={2021}
}

Should you have any question, feel free to contact me through [email protected]