Artificial Intelligence-Driven Multimedia and Multimodal Intelligence: From Algorithms to Real-World Deployment

The text begins with data fusion from multiple modalities and neural network-based architectures, through to real-time applications and security considerations. It introduces readers to the versatility and further possibilities of multimedia and multimodal intelligent applications in different fields. The text begins with data fusion from multiple modalities and neural network-based architectures, through to real-time applications and security considerations. It introduces readers to the versatility and further possibilities of multimedia and multimodal intelligent applications in different fields. It discusses topics such as edge artificial intelligence for lightweight multimodal systems and deep learning architectures for multimodal fusion. Presents a comprehensive coverage of the latest advancements in the field of multimedia and multimodal intelligence. Focuses on interdisciplinary approaches combining artificial intelligence, machine learning, and deep learning. Covers problems and drawbacks of multimodal intelligence, including data heterogeneity, requirements for real time, and privacy aspects. Discusses topics such as edge artificial intelligence for lightweight multimodal systems and deep learning architectures for multimodal fusion. Explains natural language processing-vision integration, real-time multimodal processing, and cross-modal learning. This book is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, artificial intelligence, and machine learning.