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 Gnanamonickam, A. Arul Selvan; M., Savithri Title A name given to the resource Visual-audio fusion in multimedia content analysis 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.177-191 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-27528-9.00008-5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-27528-9.00008-5</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032931832?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032931832?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 Gnanamonickam A.A.S., Department of Software System and Computer Science (PG), KG College of Arts and Science, Tamil Nadu, Coimbatore, India; M. S., Department of Data Science, Christ University, Karnataka, Bengaluru, India Description An account of the resource The analysis of multimedia material has grown in importance due to the rapid expansion of digital media. Exploiting the combination of auditory and visual modalities for improved comprehension and interpretation of multimedia material has gained popularity in recent years. An extensive review of the methods, strategies, and uses of visual-audio fusion in multimedia content analysis is provided in this chapter. Combining auditory and visual modalities provides a number of benefits over single-modal analysis, such as increased robustness, deeper semantic comprehension, and better user experience. This chapter investigates a variety of fusion approaches, from late combination at the decision near to early synthesis at the feature nearby. Besides, it studies sophisticated fusion methods that allow for efficient integraton of data across modalities, such cross-modal attention processes and multimodal deep learning. Also looks at several other areas, such as multimedia retrieval, event detection, sentiment analysis, and emotional computing, where visual-audio fusion has been used successfully. It dialogs about the difficulties and potential paths ahead for the area, including how to deal with modality inconsistencies, manage massive multimedia information, and create fusion models that are understandable. To sum up, visual-audio fusion presents new possibilities for comprehending and analyzing complicated multimedia data and has the potential to significantly advance multimedia content analysis. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource cross-model; Fusion; multimedia; multimedia analysis; sophisticated fusion; visual-audio 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