Advances in Multimodal Information Retrieval and Generation
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Beschreibung
This book provides an extensive examination of state-of-the-art methods in multimodal retrieval, generation, and the pioneering field of retrieval-augmented generation. The work is rooted in the domain of Transformer-based models, exploring the complexities of blending and interpreting the intricate connections between text and images. The authors present cutting-edge theories, methodologies, and frameworks dedicated to multimodal retrieval and generation, aiming to furnish readers with a comprehensive understanding of the current state and future prospects of multimodal AI. As such, the book is a crucial resource for anyone interested in delving into the intricacies of multimodal retrieval and generation. Serving as a bridge to mastering and leveraging advanced AI technologies in this field, the book is designed for students, researchers, practitioners, and AI aficionados alike, offering the tools needed to expand the horizons of what can be achieved in multimodal artificial intelligence.
This book provides an extensive examination of state-of-the-art methods in multimodal retrieval, generation, and the pioneering field of retrieval-augmented generation. The work is rooted in the domain of Transformer-based models, exploring the complexities of blending and interpreting the intricate connections between text and images. The authors present cutting-edge theories, methodologies, and frameworks dedicated to multimodal retrieval and generation, aiming to furnish readers with a comprehensive understanding of the current state and future prospects of multimodal AI. As such, the book is a crucial resource for anyone interested in delving into the intricacies of multimodal retrieval and generation. Serving as a bridge to mastering and leveraging advanced AI technologies in this field, the book is designed for students, researchers, practitioners, and AI aficionados alike, offering the tools needed to expand the horizons of what can be achieved in multimodal artificial intelligence.
Provides a comprehensive overview of the state-of-the-art in multi-modal architectures and representation learning Presents state-of-the-art techniques including neural models based on transformers and multi-modal learning techniques Explores the foundations and algorithms that power MMIR and how information can be retrieved using multimodal queries
Autor*in
Man Luo
Themen in »Advances in Multimodal Information Retrieval and Generation«
Multi-modal Information Retrieval Vision and Language Information Retrieval Image and Text Retrieval Vision and Language Language Grounding Natural Language Processing Computer Vision Machine Learning Artificial Intelligence