  Video captioning (VC) is a fast-moving, cross-disciplinary area of research
that bridges work in the fields of computer vision, natural language processing
(NLP), linguistics, and human-computer interaction. In essence, VC involves
understanding a video and describing it with language. Captioning is used in a
host of applications from creating more accessible interfaces (e.g., low-vision
navigation) to video question answering (V-QA), video retrieval and content
generation. This survey covers deep learning-based VC, including but, not
limited to, attention-based architectures, graph networks, reinforcement
learning, adversarial networks, dense video captioning (DVC), and more. We
discuss the datasets and evaluation metrics used in the field, and limitations,
applications, challenges, and future directions for VC.
