Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Dsouza, Salu; Panen, Jos Title A name given to the resource Future trends in multimodal learning: from theory to practical applications Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Multimodal Learning Using Heterogeneous Data;pp.247-263 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-27528-9.00009-7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-27528-9.00009-7</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032985842?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032985842?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Dsouza S., School of Law, Christ University, Karnataka, Bengaluru, India; Panen J., Department of Commercial Sciences, Business Management &amp; Informatics, Vives University of Applied Sciences, Bruges, Belgium Description An account of the resource The human ability to seamlessly integrate information from various sensory channelssight, sound, touchhas long inspired researchers in artificial intelligence. This ability to learn from and reason with multimodal datatext, speech, vision, and moreforms the core of multimodal learning. This abstract delves into the theoretical foundations of multimodal learning, explores its cutting-edge advancements, and critically examines the path toward practical applications in diverse fields. At its heart, multimodal learning seeks to exploit the inherent complementarity between different data modalities. Text, for instance, provides rich semantic meaning, while visual data offers valuable context. Speech captures the nuances of emotion and prosody often absent in text. By learning from these combined modalities, models can achieve a more comprehensive understanding of the world around them. Recent years have witnessed significant progress in multimodal learning architectures. Deep learning approaches, particularly convolutional neural networks and recurrent neural networks, have proven adept at capturing complex relationships within individual modalities. New architectures like multimodal transformers further bridge the gap by allowing models to learn joint representations across different modalities. These advancements pave the way for a paradigm shift in areas like computer vision, natural language processing, and robotics. In computer vision, multimodal learning allows models to not only recognize objects in images but also understand the context and actions depicted. By incorporating textual descriptions or speech narratives alongside visual data, models can achieve better scene understanding, image captioning, and action recognition. This has applications in autonomous vehicles, where understanding traffic signs, pedestrians, and road conditions is crucial, and in video surveillance systems, where interpreting visual cues alongside spoken dialogue improves anomaly detection. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource artificial intelligence; cognitive process; computer in society; Computer vision, robotics, explainability, fairness, deep learning; human- interaction; information management; information systems; machine learning Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044327528-9; 978-044327529-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online