A Comprehensive Survey and Guide to Multimodal Large Language Models in Vision-Language Tasks

• URL: http://arxiv.org/abs/2411.06284v2
• Date: Sun, 08 Dec 2024 06:48:30 GMT
• Title: A Comprehensive Survey and Guide to Multimodal Large Language Models in Vision-Language Tasks
• Authors: Chia Xin Liang, Pu Tian, Caitlyn Heqi Yin, Yao Yua, Wei An-Hou, Li Ming, Tianyang Wang, Ziqian Bi, Ming Liu,
• Abstract summary: Large language models (MLLMs) integrate text, images, video and audio to enable AI systems for cross-modal understanding and generation. Book examines prominent MLLM implementations while addressing key challenges in scalability, robustness, and cross-modal learning. Concluding with a discussion of ethical considerations, responsible AI development, and future directions, this authoritative resource provides both theoretical frameworks and practical insights.
• Score: 5.0453036768975075
• License:
• Abstract: This survey and application guide to multimodal large language models(MLLMs) explores the rapidly developing field of MLLMs, examining their architectures, applications, and impact on AI and Generative Models. Starting with foundational concepts, we delve into how MLLMs integrate various data types, including text, images, video and audio, to enable complex AI systems for cross-modal understanding and generation. It covers essential topics such as training methods, architectural components, and practical applications in various fields, from visual storytelling to enhanced accessibility. Through detailed case studies and technical analysis, the text examines prominent MLLM implementations while addressing key challenges in scalability, robustness, and cross-modal learning. Concluding with a discussion of ethical considerations, responsible AI development, and future directions, this authoritative resource provides both theoretical frameworks and practical insights. It offers a balanced perspective on the opportunities and challenges in the development and deployment of MLLMs, and is highly valuable for researchers, practitioners, and students interested in the intersection of natural language processing and computer vision.

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