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

2024-11-09

arXiv.org

AI Summary

This paper addresses key challenges in multimodal large language models (MLLMs)—namely limited scalability, insufficient robustness, and difficulties in cross-modal alignment—within vision-language tasks. To tackle these, it proposes a comprehensive technical taxonomy covering the full MLLM stack and introduces a tripartite evaluation framework centered on scalability, robustness, and ethical alignment. Methodologically, the work conducts a systematic comparative analysis of core techniques, including Transformer-based fusion architectures, visual encoders (e.g., ViT, CLIP), cross-modal alignment learning, instruction tuning, and multi-stage training. It further synthesizes structured best practices for modeling across text, image, video, and audio modalities. The study delivers 12 representative application cases, empirically validating its framework and guidelines. Collectively, this work advances trustworthy deployment and cross-domain adoption of MLLMs through rigorous architectural analysis, principled evaluation, and actionable implementation insights.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Surveying multimodal large language models' architectures and applications
• Exploring integration of text, images, video, and audio for cross-modal understanding
• Addressing challenges in scalability, robustness, and cross-modal learning

Innovation

• Integrates text, images, video, audio for cross-modal understanding
• Covers training methods and architectural components for MLLMs
• Addresses scalability, robustness, and cross-modal learning challenges