Update README.md

README.md

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-# DLF
-Code is coming!
+# DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis, AAAI 2025.
+
+## 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$$\rightarrow$Language).
+- **Hierarchical Predictions:** We devise hierarchical predictions to leverage the pre-fused and post-fused features, improving the total MSA accuracy. 
+
+
+## The Framework.
+![](./imgs/Framework.png)
+The framework of DLF. Please refer to [Paper Link](arxiv) for details.
+
+
+## Usage
+
+### Prerequisites
+- Python 3.9.13
+- PyTorch 1.13.0
+- CUDA 11.7
+
+### Installation
+- Create conda environment. Please make sure you have installed conda before.
+```
+conda create -n DLF python==3.9.13
+```
+```
+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
+```
+```
+pip instal 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 MOSEI dataset is located in `./dataset/MOSEI/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 privacy of Youtube content creators. For more details, please follow the [official website](https://github.com/A2Zadeh/CMU-MultimodalSDK) of these datasets.
+
+### Run the Codes
+- Training
+
+You can first set the traning 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 pretrained 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 resarch or work, please cite the following paper.
+
+```
+@article{wang2025dlf,
+  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},
+  year={2024}
+}
+```
+
+
+
+