Problem Formalization and Benchmarking of Multimodal Learning for Text, Image, Audio, and Video Understanding to Promote Inclusive and Sustainable Knowledge Access

The Multimodal learning, being a combination of text, image, audio and video information has come up as the essential paradigm of creating intelligent and context sensitive information system. They are also being used more in education, healthcare, governance, and media, where the successful combination of various types of data can be used to improve knowledge, decision-making, and accessibility. Although there has been much progress, there have been a number of challenges such as the absence of standard definitions of the problem, disparities between benchmarking protocols and the problem of equitable and inclusive knowledge access. The current paper will provide a detailed system toward formalizing and benchmarking of multimodal learning models, especially in terms of interpretability, fairness, and resource efficiency. Representative architectures are analyzed in a systematic manner, in a variety of modalities, to measure performance, robustness as well as computation requirements trade-offs. The results offer viable lessons to researchers and practitioners in order to create not only accurate and scalable multimodal systems but also socially accountable and able to promote inclusive knowledge sharing. Besides, the research points out the most influential areas where research should focus in the future to develop effective, accessible, and sustainable multimodal models.