Amidst the evolving landscape of artificial intelligence, the convergence of
visual and textual information has surfaced as a crucial frontier, leading to
the advent of image-text multimodal models. This paper provides a comprehensive
review of the evolution and current state of image-text multimodal models,
exploring their application value, challenges, and potential research
trajectories. Initially, we revisit the basic concepts and developmental
milestones of these models, introducing a novel classification that segments
their evolution into three distinct phases, based on their time of introduction
and subsequent impact on the discipline. Furthermore, based on the tasks'
significance and prevalence in the academic landscape, we propose a
categorization of the tasks associated with image-text multimodal models into
five major types, elucidating the recent progress and key technologies within
each category. Despite the remarkable accomplishments of these models, numerous
challenges and issues persist. This paper delves into the inherent challenges
and limitations of image-text multimodal models, fostering the exploration of
prospective research directions. Our objective is to offer an exhaustive
overview of the present research landscape of image-text multimodal models and
to serve as a valuable reference for future scholarly endeavors. We extend an
invitation to the broader community to collaborate in enhancing the image-text
multimodal model community, accessible at:
\href{https://github.com/i2vec/A-survey-on-image-text-multimodal-models}{this https URL}.

在人工智能领域的演变环境中，图像与文本信息的融合已成为一个关键的领域，引发了图像 - 文本多模态模型的出现。本文全面回顾了图像 - 文本多模态模型的发展和现状，探讨了其应用价值、挑战和潜在研究方向。通过细分演化阶段，提出了三个不同阶段的分类，根据其引入时间和对学科的影响。此外，根据任务在学术领域的重要性和普及性，对图像 - 文本多模态模型相关任务进行了五个主要类型的分类，阐明了每个类别内的最新进展和关键技术。尽管这些模型取得了显著成就，但仍存在许多挑战和问题。本文深入探讨了图像 - 文本多模态模型固有的挑战和限制，促进了未来研究方向的探索。我们的目标是提供对图像 - 文本多模态模型研究现状的全面概述，并为未来学术工作提供有价值的参考。我们邀请广大学术社区共同合作，推进图像 - 文本多模态模型社区的发展。