  The advent of Large Language Models (LLMs) has significantly reshaped the
trajectory of the AI revolution. Nevertheless, these LLMs exhibit a notable
limitation, as they are primarily adept at processing textual information. To
address this constraint, researchers have endeavored to integrate visual
capabilities with LLMs, resulting in the emergence of Vision-Language Models
(VLMs). These advanced models are instrumental in tackling more intricate tasks
such as image captioning and visual question answering. In our comprehensive
survey paper, we delve into the key advancements within the realm of VLMs. Our
classification organizes VLMs into three distinct categories: models dedicated
to vision-language understanding, models that process multimodal inputs to
generate unimodal (textual) outputs and models that both accept and produce
multimodal inputs and outputs.This classification is based on their respective
capabilities and functionalities in processing and generating various
modalities of data.We meticulously dissect each model, offering an extensive
analysis of its foundational architecture, training data sources, as well as
its strengths and limitations wherever possible, providing readers with a
comprehensive understanding of its essential components. We also analyzed the
performance of VLMs in various benchmark datasets. By doing so, we aim to offer
a nuanced understanding of the diverse landscape of VLMs. Additionally, we
underscore potential avenues for future research in this dynamic domain,
anticipating further breakthroughs and advancements.
