| # DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis, AAAI 2025. | |
| ### [Arxiv Paper](https://arxiv.org/abs/2412.12225) | |
| ## Main Contributions | |
| Our main contributions can be summarized as follows: | |
| - **Proposed Framework:** In this study, we propose a Disentangled-Language-Focused (DLF) multimodal representation learning framework to promote MSA tasks. The framework follows a structured pipeline: feature extraction, disentanglement, enhancement, fusion, and prediction. | |
| - **Language-Focused Attractor (LFA):** We develop the LFA to fully harness the potential of the dominant language modality within the modality-specific space. The LFA exploits the language-guided multimodal cross-attention mechanisms to achieve a targeted feature enhancement ($X$->Language). | |
| - **Hierarchical Predictions:** We devise hierarchical predictions to leverage the pre-fused and post-fused features, improving the total MSA accuracy. | |
| ## The Framework | |
|  | |
| The framework of DLF. Please refer to [Paper Link](https://arxiv.org/abs/2412.12225) for details. | |
| ## Usage | |
| ### Prerequisites | |
| - Python 3.9.13 | |
| - PyTorch 1.13.0 | |
| - CUDA 11.7 | |
| ### Installation | |
| - Create a conda environment. Please make sure you have installed conda before. | |
| ``` | |
| conda create -n DLF python==3.9.13 | |
| ``` | |
| - Activate the built DLF environment. | |
| ``` | |
| conda activate DLF | |
| ``` | |
| - Install Pytorch with CUDA | |
| ``` | |
| pip install torch==1.13.0+cu117 torchvision==0.14.0+cu117 torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cu117 | |
| ``` | |
| - Clone this repo. | |
| ``` | |
| git clone https://github.com/pwang322/DLF.git | |
| ``` | |
| - Install the necessary packages. | |
| ``` | |
| cd DLF | |
| pip install -r requirements.txt | |
| ``` | |
| ### Datasets | |
| Data files (containing processed MOSI, MOSEI datasets) can be downloaded from [here](https://drive.google.com/drive/folders/1BBadVSptOe4h8TWchkhWZRLJw8YG_aEi?usp=sharing). | |
| You can first build and then put the downloaded datasets into `./dataset` directory and revise the path in `./config/config.json`. For example, if the processed the MOSI dataset is located in `./dataset/MOSI/aligned_50.pkl`. Please make sure "dataset_root_dir": "./dataset" and "featurePath": "MOSI/aligned_50.pkl". | |
| Please note that the meta information and the raw data are not available due to the privacy of YouTube content creators. For more details, please follow the [official website](https://github.com/ecfm/CMU-MultimodalSDK) of these datasets. | |
| ### Run the Codes | |
| - Training | |
| You can first set the training dataset name in `./train.py` as "mosei" or "mosi", and then run: | |
| ``` | |
| python3 train.py | |
| ``` | |
| By default, the trained model will be saved in `./pt` directory. You can change this in `train.py`. | |
| - Testing | |
| You can first set the testing dataset name in `./test.py` as "mosei" or "mosi", and then test the trained model: | |
| ``` | |
| python3 test.py | |
| ``` | |
| We also provide pre-trained models for testing. ([Google drive](https://drive.google.com/drive/folders/1GgCfC1ITAnRRw6RScGc7c2YUg5Ccbdba?usp=sharing)) | |
| ### Citation | |
| If you find the code and our idea helpful in your research or work, please cite the following paper. | |
| ``` | |
| @article{wang2024dlf, | |
| title={DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis}, | |
| author={Wang, Pan and Zhou, Qiang and Wu, Yawen and Chen, Tianlong and Hu, Jingtong}, | |
| journal={arXiv preprint arXiv:2412.12225}, | |
| year={2024} | |
| } | |
| ``` | |