Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions
Jing Gu, Eliana Stefani, Qi Wu, Jesse Thomason, Xin Wang
Abstract
A long-term goal of AI research is to build intelligent agents that can communicate with humans in natural language, perceive the environment, and perform real-world tasks. Vision-and-Language Navigation (VLN) is a fundamental and interdisciplinary research topic towards this goal, and receives increasing attention from natural language processing, computer vision, robotics, and machine learning communities. In this paper, we review contemporary studies in the emerging field of VLN, covering tasks, evaluation metrics, methods, etc. Through structured analysis of current progress and challenges, we also highlight the limitations of current VLN and opportunities for future work. This paper serves as a thorough reference for the VLN research community.
BibTeX
@inproceedings{gu-etal-2022-vision,
title = "Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions",
author = "Gu, Jing and
Stefani, Eliana and
Wu, Qi and
Thomason, Jesse and
Wang, Xin",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.524/",
doi = "10.18653/v1/2022.acl-long.524",
pages = "7606--7623"
}