图像字幕生成的深度学习方法：综述
Deep Learning Approaches on Image Captioning: A Review
AI总结：
该综述系统梳理了图像字幕生成的深度学习方法，给出分类体系、常用数据集与评价指标，分析现有挑战与性能排名，并指明未来研究方向。
摘要：
图像字幕生成是一个极具重要性的研究领域，旨在为静态图像形式的视觉内容生成自然语言描述。深度学习的出现以及近年来视觉-语言预训练技术的发展彻底革新了该领域，催生了更复杂的方法并提升了性能。在这篇综述论文中，我们通过提出全面的分类体系并详细讨论每类方法，对图像字幕生成领域的深度学习方法进行了结构化梳理。此外，我们考察了图像字幕生成研究中常用的数据集，以及用于评估不同字幕模型性能的评价指标。我们重点阐述了该领域面临的挑战，包括对象幻觉、上下文缺失、光照条件、上下文理解以及指代表达等问题。我们依据广泛使用的评价指标对不同深度学习方法的性能进行了排序，以呈现当前的最优技术水平。此外，我们还指出了该领域若干潜在的未来研究方向，包括解决图像与文本模态间的信息错位问题、缓解数据集偏差、引入视觉-语言预训练方法优化字幕生成，以及开发更完善的评估工具以准确衡量图像字幕的质量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
