Connecting text and visual modalities plays an essential role in generative
intelligence. For this reason, inspired by the success of large language
models, significant research efforts are being devoted to the development of
Multimodal Large Language Models (MLLMs). These models can seamlessly integrate
visual and textual modalities, both as input and output, while providing a
dialogue-based interface and instruction-following capabilities. In this paper,
we provide a comprehensive review of recent visual-based MLLMs, analyzing their
architectural choices, multimodal alignment strategies, and training
techniques. We also conduct a detailed analysis of these models across a wide
range of tasks, including visual grounding, image generation and editing,
visual understanding, and domain-specific applications. Additionally, we
compile and describe training datasets and evaluation benchmarks, conducting
comparisons among existing models in terms of performance and computational
requirements. Overall, this survey offers a comprehensive overview of the
current state of the art, laying the groundwork for future MLLMs.

连接文本和视觉模态在生成智能中起着关键作用。受大型语言模型成功的启发，目前正在大量研究开发多模态大型语言模型 (MLLMs)。本文全面回顾了最近的面向视觉的 MLLMs，分析了它们的体系结构选择、多模态对齐策略和训练技术。同时，还对这些模型在包括视觉定位、图像生成和编辑、视觉理解以及领域特定应用等一系列任务上进行了详细分析。此外，我们还编译和描述了训练数据集和评估基准，并在性能和计算要求方面对现有模型进行了比较。总体而言，本调查提供了当前最新技术的全面概述，为未来的 MLLMs 奠定了基础。