Humans use multiple senses to comprehend the environment. Vision and language are two of the most vital senses since they allow us to easily communicate our thoughts and perceive the world around us. There has been a lot of interest in creating video-language understanding systems with human-like senses since a video-language pair can mimic both our linguistic medium and visual environment with temporal dynamics. In this survey, we review the key tasks of these systems and highlight the associated challenges. Based on the challenges, we summarize their methods from model architecture, model training, and data perspectives. We also conduct performance comparison among the methods, and discuss promising directions for future research.
@article{arxiv.2406.05615,
title = {Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives},
author = {Thong Nguyen and Yi Bin and Junbin Xiao and Leigang Qu and Yicong Li and Jay Zhangjie Wu and Cong-Duy Nguyen and See-Kiong Ng and Luu Anh Tuan},
journal= {arXiv preprint arXiv:2406.05615},
year = {2026}
}
Comments
Accepted at ACL 2024 (Findings). Code is available at https://github.com/nguyentthong/video-language-understanding