| # DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis, AAAI 2025. |
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| ### [Arxiv Paper](https://arxiv.org/abs/2412.12225) |
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|
| ## Main Contributions |
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| Our main contributions can be summarized as follows: |
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| - **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. |
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|
| ## Usage |
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|
| ### Prerequisites |
| - Python 3.9.13 |
| - PyTorch 1.13.0 |
| - CUDA 11.7 |
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|
| ### 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} |
| } |
| ``` |
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