Description: Neural multimodal frameworks provide a structured approach to developing and evaluating multimodal models, which are systems capable of processing and merging information from different modalities, such as text, images, and audio. These frameworks enable researchers and developers to integrate various data sources into a single model, facilitating the creation of more robust and versatile applications. One of the main features of these frameworks is their ability to learn joint representations of different types of data, enhancing understanding and information generation. Additionally, they often include tools and libraries that simplify the training and evaluation process of models, allowing users to experiment with different architectures and fusion techniques. The relevance of neural multimodal frameworks lies in their potential to tackle complex problems across various fields such as computer vision, natural language processing, and robotics, where information from multiple modalities is crucial for system performance. In summary, these frameworks are essential for advancing research and development in artificial intelligence that can interact more effectively with the real world by coherently combining and understanding different forms of data.
