Multimodal data generation and synthesis
artificial intelligence; autonomous systems; bias mitigation; cross-modal generation; data representation; data synthesis; deep learning; edge computing; ethical AI; extended reality (XR); generative adversarial networks (GANs); healthcare applications; immersive experiences; Multimodal data; multimodal translation; privacy concerns; quantum computing; text-to-image synthesis; variational autoencoders (VAEs)
Multimodal data generation and synthesis have become new promising directions in artificial intelligence research, making possible the combination and transformation of the different data modalities: text, images, audio, and video. In this chapter a look will be made about the principles, methodologies, applications, and challenges linked with multimodal data, bringing attention to the current trends and needs regarding multimodal systems and systems approaches to tackle complex real-world challenges across the medical and health care, autonomous systems, entertainment, and extended reality (XR) fields. The chapter introduces multimodal data and discusses how the approach differs from unimodal methods, considering the merits of working with multiple data forms. Multimodal systems present richer and more comprehensive representations that lead to better decision-making and provide a better interaction with users. The complexity due to alignment, synchronization, and representation of diverse modes is inherently difficult. This section further discusses state-of-the-art techniques in multimodal synthesis, especially focusing on generative approaches like generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models. These methods are shown to facilitate cross-modal transformations, such as text-to-image or audio-to-video synthesis, driving innovation in artificial intelligence and beyond. Applications of multimodal data synthesis are discussed in detail, underscoring its transformative impact. In health care, for instance, synthesizing medical images paired with textual annotations enhances diagnostic accuracy and medical training. Autonomous vehicles benefit from the integration of LiDAR, visual, and auditory data, enabling robust decision-making in real-time environments. Similarly, in entertainment and XR, multimodal synthesis is redefining content creation, making immersive experiences more personalized and dynamic. The chapter also delves into novel applications such as multimodal translation, exemplified by systems that translate sign language into spoken text, fostering inclusivity and accessibility. Despite its potential, multimodal synthesis faces critical challenges, including bias in data and models, privacy concerns, and the ethical implications of creating hyperrealistic synthetic data, such as deepfakes. All these raise pressing concerns, and addressing these requires robust privacy-preserving techniques, bias-mitigation strategies, and stringent ethical guidelines. 2026 Elsevier Inc. All rights reserved.
K.V., Thomas; Birari, Abhijeet; Rattan, Punam
Multimodal Learning Using Heterogeneous Data;pp.117-137
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1016/B978-0-443-27528-9.00018-8 https://www.scopus.com/pages/publications/105032986083?origin=resultslist
K.V. T., Department of Commerce, CHRIST (Deemed to be University), Lavasa Campus, Maharashtra, Pune, India; Birari A., 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