Multimodal Fusion Model

Multimodal fusion models integrate data from diverse sources, such as images, text, and sensor readings, to improve the accuracy and robustness of machine learning models. Current research focuses on developing effective fusion strategies, often employing deep learning architectures like transformers and convolutional neural networks, to combine these modalities and address challenges like missing data and adversarial attacks. This approach holds significant promise for various applications, including medical diagnosis (e.g., pulmonary embolism, Alzheimer's disease), autonomous driving, and mineral prospectivity mapping, by leveraging the complementary information provided by different data types. The field is also actively investigating fairness and robustness issues within these models.