Abstract
Video captioning is the process of creating a natural language sentence that summarises the video's contents automatically. Modeling the video's effective temporal composition and effectively integrating that information into a plain language description are both required. It has a variety of applications, including assisting the visually impaired, video subtitling, and video surveillance, among others. Due to the advancement of deep learning in computer vision and natural language processing, there has been a surge in study in this area in recent years. Video captioning is the result of combining these two worlds of computer vision and natural language processing. In this study, we examine and analyse various strategies for addressing this issue, as well as benchmark datasets in terms of domains, repository size, and number of classes; and identify the benefits and drawbacks of various evaluation metrics such as BLEU, METEOR, CIDEr, SPICE, and ROUGE.
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