Description: Multimodal Evaluation Models are approaches that allow for analyzing and measuring the performance of systems using data from multiple modalities, such as text, images, audio, and video. These models are fundamental in the field of artificial intelligence and machine learning, as they integrate different types of information to provide a more comprehensive and accurate evaluation. Multimodality refers to a system’s ability to process and understand data from various sources, enriching interpretation and decision-making. The main characteristics of these models include data fusion, where different modalities are combined to enhance the accuracy of the analysis, and the ability to learn representations that capture the interrelationship between different modalities. The relevance of Multimodal Evaluation Models lies in their application in various domains, such as computer vision, natural language processing, and robotics, where a holistic understanding of information is crucial for developing smarter and more efficient systems.
