Yicong Hong - VLN BERT | Talking Papers Podcast - Listen or read transcript on Metacast

‌

Episode description

‌

Episode description

PAPER TITLE:
"VLN BERT: A Recurrent Vision-and-Language BERT for Navigation"

AUTHORS:
Yicong Hong, Qi Wu, Yuankai Qi, Cristian Rodriguez-Opazo, Stephen Gould

ABSTRACT:
Accuracy of many visiolinguistic tasks has benefited significantly from the application of vision-and-language (V&L) BERT. However, its application for the task of vision and-language navigation (VLN) remains limited. One reason for this is the difficulty adapting the BERT architecture to the partially observable Markov decision process present in VLN, requiring history-dependent attention and decision making. In this paper we propose a recurrent BERT model that is time-aware for use in VLN. Specifically, we equip the BERT model with a recurrent function that maintains cross-modal state information for the agent. Through extensive experiments on R2R and REVERIE we demonstrate that our model can replace more complex encoder-decoder models to achieve state-of-the-art results. Moreover, our approach can be generalised to other transformer-based architectures, supports pre-training, and is capable of solving navigation and referring expression tasks simultaneously.

CODE:
💻 https://github.com/YicongHong/Recurrent-VLN-BERT

LINKS AND RESOURCES
👱Yicong's page

RELATED PAPERS:
📚 Attention is All You Need
📚 Towards learning a generic agent for vision-and-language navigation via pre-training

CONTACT:
-----------------
If you would like to be a guest, sponsor or just share your thoughts, feel free to reach out via email: talking.papers.podcast@gmail.com

This episode was recorded on April, 16th 2021.

SUBSCRIBE AND FOLLOW:
🎧Subscribe on your favourite podcast app: https://talking.papers.podcast.itzikbs.com
📧Subscribe to our mailing list: http://eepurl.com/hRznqb
🐦Follow us on Twitter: https://twitter.com/talking_papers
🎥YouTube Channel: https://bit.ly/3eQOgwP

#talkingpapers #CVPR2021 #VLNBERT
#VLN #VisionAndLanguageNavigation #VisionAndLanguage #machinelearning #deeplearning #AI #neuralnetworks #research #computervision #artificialintelligence