Description: Quantitative fusion in multimodal systems refers to the process of integrating and analyzing data from different modalities or sources, such as text, images, audio, and video, to obtain a more complete and accurate representation of information. This approach allows for the combination of the strengths of each modality, overcoming limitations that may arise when analyzing each type of data in isolation. Quantitative fusion relies on statistical techniques and machine learning algorithms that evaluate and combine the relevant features of each modality, thus generating a more robust and effective model. This process is essential in the development of intelligent systems that require a deep and contextual understanding of information, such as interpreting emotions through the combination of text analysis and facial recognition. The relevance of quantitative fusion lies in its ability to improve accuracy and efficiency in complex tasks, such as data classification, anomaly detection, and behavior prediction, making it a fundamental tool in fields such as artificial intelligence, computer vision, and natural language processing.
