An introduction to multimodal data representation
subject: audio recognition; convolutional neural networks (CNNs); data complexity; data fusion; data integration; deep learning; early fusion; feature extraction; hybrid fusion; image processing; late fusion; Multimodal data representation; natural language processing (NLP); recurrent neural networks (RNNs); sensor data; structured data; text analysis; unimodal techniques; unstructured data; video analysis
description: The contemporary digital epoch is characterized by a radical transformation of data representation methodologies that imply increased intricacy as well as an enlarged bulk of data. An unimodal approach focusing on judicious data types, considered in isolation, was the earlier norm. The emphasis was on structured data, which had the advantage of being arranged systematically within relational databases and entity-relationship frameworks. This facilitated efficient data management. With the introduction of the internet and digital communication, such unstructured data as textual content, images, and audio began to be placed up front. But unimodal techniques were not adequately equipped to manage the intricate and interconnected nature of real-world phenomena. The welcome result was the development of multimodal data representation methodologies, which constitute a sophisticated paradigm that integrates data from such varied sources as text, images, audio, video, and sensor data. This results in a more holistic comprehension of complex scenarios. Distinct attributes and inherent challenges characterize each modality. To exemplify, text data need advanced natural language processing strategies to comprehend context and semantics; Image data necessitate methodologies well versed in managing spatial features and elevated dimensionality; audio data requires concentration on temporal patterns and noise; video data, on the contrary, integrates these complexities, leading to efficient processing techniques to accommodate its substantial volume and dynamic characteristics. The unsynchronous and heterogeneous sensor data complicate the integration of diverse data streams. Sophisticated fusion techniques, that is, early fusion, late fusion, and hybrid fusion, capable of integrating features from various modalities, are employed to mitigate the challenges faced by multimodal data representation. It increases interpretative insights and precision. The deep learning technologies, s…
creator: K.V., Thomas; Rattan, Punam; Nair, Raji Ramakrishnan
source: Multimodal Learning Using Heterogeneous Data;pp.17-30
publisher: Elsevier
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 978-044327528-9; 978-044327529-6;
language: English
type: Book chapter
coverage: K.V. T., Department of Commerce, CHRIST (Deemed to be University), Lavasa Campus, Maharashtra, Pune, India; Rattan P., School of Computer Application, Lovely Professional University, Punjab, Phagwara, India; Nair R.R., School of Business and Management (MBA), CHRIST (Deemed to be University), Lavasa Campus, Maharashtra, Pune, India