Paper Info
Title
Vision Powered Conversational AI for Easy Human Dialogue Systems
Abstract
In this paper, we propose an end to end goal-oriented conversational AI agent that can provide contextual information from a potential hazard site. We posit the conversational agent as a FloodBot capable of seeing, sensing, assessing hazard condition, and ultimately conversing about them. We present our domain-specific FloodBot design-solution and learning-experience from the real-time deployment in a flash flood devastated city that uses state-of-the-art deep learning models. We specifically used computer vision and pertinent natural language processing technologies to empower the conversation power of the FloodBot. To deliver such practical and usable AI, we chain multiple deep learning frameworks and create a human-friendly question-answer based dialogue system. We present our deployment details from the last five months and validate the results using ongoing COVID19's impact on the area as well.
Year
DOI
Venue
2020
10.1109/MASS50613.2020.00088
2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)
Keywords
DocType
ISSN
Chatbot,Deep Learning,Natural Language Processing,Computer Vision,Mobile computing
Conference
2155-6806
ISBN
Citations 
PageRank 
978-1-7281-9867-5
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Bipendra Basnyat143.57
Neha Singh281.94
Nirmalya Roy3106.01
Aryya Gangopadhyay4391112.49