Exploring deep learning approaches for video captioning: A comprehensive review

Abstract

While humans can easily describe visual data at varying levels of detail, the same task presents a significant challenge for machines. This challenge becomes even more complex when dealing with video data. The process of understanding a video and generating descriptive text for it is known as video captioning. Video captioning requires not only understanding the visual content but also producing human-like descriptions that accurately capture its semantics. Achieving this level of understanding requires the collaborative efforts of both the computer vision and natural language processing research communities. The captions produced through video captioning serve as valuable resources that can be further leveraged for various applications such as video search, accessibility for visually impaired people, and human-robot interaction. Deep learning strategies have emerged as powerful tools in addressing the complexities of video captioning. By leveraging large scale annotated video caption datasets and sophisticated neural network architectures, deep learning approaches have made significant advances in this challenging task. In the existing literature, numerous techniques, benchmark datasets, and evaluation metrics have been developed, emphasizing the necessity for a comprehensive examination to concentrate research efforts in this rapidly evolving field. This paper provides a survey of deep learning based methods for video captioning, highlighting their key components, challenges, and recent advancements.