  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, 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.
