全面解析多模态大模型在视觉语言任务中的发展与应用
A Comprehensive Survey and Guide to Multimodal Large Language Models in Vision-Language Tasks
- 系统梳理多模态大模型的架构与训练方法
- 涵盖图文视频音频融合的跨模态理解与生成能力
- 适合关注AI交叉领域研究者与实践者阅读
本文全面综述多模态大语言模型（MLLMs）在视觉-语言任务中的进展，涵盖其架构设计、训练方法、跨模态理解与生成能力。从基础概念出发，探讨文本、图像、视频、音频等多源数据的融合机制，分析其在视觉叙事、无障碍访问等实际场景中的应用。通过典型案例和技术剖析，揭示当前在可扩展性、鲁棒性及跨模态学习方面的主要挑战。同时讨论伦理责任与可持续发展路径，为研究人员、从业者和学生提供理论框架与实践指导。
原文摘要 · Abstract (English)
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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