🔥🔥 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.
- Set up conda environemnt
conda env create -f environment.yml
conda activate BBFN
-
Install CMU Multimodal SDK
-
Set
sdk_dir
insrc/config.py
to the path of CMU-MultimodalSDK -
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 henryhan88888@gmail.com