Image captioning is a research area of immense importance, aiming to generate natural language descriptions for visual content in the form of still images. The advent of deep learning and more recently vision-language pre-training techniques has revolutionized the field, leading to more sophisticated methods and improved performance. In this survey paper, we provide a structured review of deep learning methods in image captioning by presenting a comprehensive taxonomy and discussing each method category in detail. Additionally, we examine the datasets commonly employed in image captioning research, as well as the evaluation metrics used to assess the performance of different captioning models. We address the challenges faced in this field by emphasizing issues such as object hallucination, missing context, illumination conditions, contextual understanding, and referring expressions. We rank different deep learning methods' performance according to widely used evaluation metrics, giving insight into the current state of the art. Furthermore, we identify several potential future directions for research in this area, which include tackling the information misalignment problem between image and text modalities, mitigating dataset bias, incorporating vision-language pre-training methods to enhance caption generation, and developing improved evaluation tools to accurately measure the quality of image captions.

翻译：图像描述是一个极为重要的研究领域，旨在为静态图像形式的视觉内容生成自然语言描述。深度学习的兴起，尤其是近期视觉-语言预训练技术的发展，彻底革新了这一领域，催生了更复杂的方法并提升了性能。本综述论文通过对深度学习方法进行系统分类，详细讨论每类方法，提供了图像描述中深度学习方法的结构化回顾。此外，我们审视了图像描述研究中常用的数据集，以及用于评估不同描述模型性能的评价指标。我们通过强调对象幻觉、上下文缺失、光照条件、语境理解和指代表达等问题，探讨了这一领域面临的挑战。我们根据广泛使用的评价指标对不同深度学习方法的性能进行排序，揭示了当前最先进技术的发展现状。此外，我们指出了该领域研究的若干潜在未来方向，包括解决图像与文本模态之间的信息不对齐问题、缓解数据集偏差、整合视觉-语言预训练方法以增强描述生成，以及开发改进的评价工具以准确衡量图像描述的质量。