A Comprehensive Survey and Guide to Multimodal Large Language Models in Vision-Language Tasks
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917112762597376 |
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| author | Liang, Chia Xin Tian, Pu Yin, Caitlyn Heqi Yua, Yao An-Hou, Wei Ming, Li Song, Xinyuan Wang, Tianyang Bi, Ziqian Liu, Ming |
| author_facet | Liang, Chia Xin Tian, Pu Yin, Caitlyn Heqi Yua, Yao An-Hou, Wei Ming, Li Song, Xinyuan Wang, Tianyang Bi, Ziqian Liu, Ming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06284 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Survey and Guide to Multimodal Large Language Models in Vision-Language Tasks Liang, Chia Xin Tian, Pu Yin, Caitlyn Heqi Yua, Yao An-Hou, Wei Ming, Li Song, Xinyuan Wang, Tianyang Bi, Ziqian Liu, Ming Artificial Intelligence 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. |
| title | A Comprehensive Survey and Guide to Multimodal Large Language Models in Vision-Language Tasks |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2411.06284 |