  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.
