Vision powered conversational AI for easy human dialogue systems

  • Bipendra Basnyat
  • , Neha Singh
  • , Nirmalya Roy
  • , Aryya Gangopadhyay

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 17th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages684-692
Number of pages9
ISBN (Electronic)9781728198668
DOIs
StatePublished - Dec 2020
Event17th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2020 - Virtual, Delhi, India
Duration: Dec 10 2020Dec 13 2020

Publication series

NameProceedings - 2020 IEEE 17th International Conference on Mobile Ad Hoc and Smart Systems, MASS 2020

Conference

Conference17th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2020
Country/TerritoryIndia
CityVirtual, Delhi
Period12/10/2012/13/20

Keywords

  • Chatbot
  • Computer Vision
  • Deep Learning
  • Mobile computing
  • Natural Language Processing

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