Emotion-Enhanced Audiovisual Perception

# Emotion-Enhanced Audiovisual Perception ## Project Description This project aims to integrate emotion recognition into multimodal correspondence learning to enhance the accuracy of audiovisual perception tasks. By extending the PE-AV model with an emotion recognition module, we aim to improve tasks such as emotion-based speech retrieval and sentiment analysis. ## Research Hypothesis Integrating emotion recognition into multimodal correspondence learning can enhance the accuracy of audiovisual perception tasks. ## Implementation Approach We will develop an extended version of the PE-AV model that includes an emotion recognition module. This model will be trained using datasets annotated with emotional labels, alongside existing audiovisual data. ## Setup Instructions 1. Clone the repository. 2. Install the required Python libraries using `pip install -r requirements.txt`. 3. Download and place the required datasets in the `data/` directory. ## Usage Examples Run the training script: ```bash python src/train.py ``` Evaluate the model: ```bash python src/evaluate.py ``` ## Expected Results We expect the integrated model to outperform the baseline PE-AV model in tasks involving emotion-based speech retrieval and sentiment analysis. ## References - [Pushing the Frontier of Audiovisual Perception with Large-Scale Multimodal Correspondence Learning](http://arxiv.org/abs/2512.19687v1)