A Review of Deep Learning for Video Captioning

Moloud Abdar, Meenakshi Kollati, Swaraja Kuraparthi, Farhad Pourpanah, Daniel McDuff +6 more
4/22/2023
cs.CV

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Cite this paper

@article{abdar2023review,
  title  = {A Review of Deep Learning for Video Captioning},
  author = {Moloud Abdar and Meenakshi Kollati and Swaraja Kuraparthi and Farhad Pourpanah and Daniel McDuff and Mohammad Ghavamzadeh and Shuicheng Yan and Abduallah Mohamed and Abbas Khosravi and Erik Cambria and Fatih Porikli},
  year   = {2023},
  eprint = {2304.11431},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.11431v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2304.11431.svg)](https://codeofpaper.com/paper/2304.11431)

Discussion