Description: Labeling in multimodal systems refers to the process of assigning labels to data coming from different modalities, such as text, images, audio, and video, within a system that integrates multiple forms of information. This process is fundamental for understanding and analyzing complex data, as it allows multimodal models to interpret and relate information from various sources. The main characteristics of labeling in this context include the need for a coherent and unified representation of the data, as well as the ability to capture interactions between different modalities. The relevance of this process lies in its application in various fields, including artificial intelligence, where the goal is to improve model accuracy by providing a richer and more varied context. By properly labeling the data, it facilitates the training of algorithms that can learn to recognize patterns and make inferences based on the combination of different types of information, resulting in more robust and efficient systems.
