SuperClass: A Deep Duo-Task Learning Approach to Improving QoS in Image-driven Smart Urban Sensing Applications

Yang Zhang, Ruohan Zong, Lanyu Shang, Md Tahmid Rashid, Dong Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Image-driven smart urban sensing (ISUS) has emerged as a powerful sensing paradigm to capture abundant visual information about the urban environment for intelligent city monitoring, planning, and management. In this paper, we focus on a Classification and Super-resolution Coupling (CSC) problem in ISUS applications, where the goal is to explore the interdependence between two critical tasks (i.e., classification and super-resolution) to concurrently boost the Quality of Service (QoS) of both tasks. Previous efforts often focus on solving the two tasks individually and ignore the opportunity to explore the interdependence between them. In this paper, we develop SuperClass, a deep duo-task learning framework, to effectively integrate the classification and super-resolution tasks into a holistic network design that jointly optimizes the QoS of both tasks. The evaluation results on a real-world ISUS application show that SuperClass consistently outperforms state-of-the-art baselines by simultaneously achieving better land usage classification accuracy and higher reconstructed image quality.

Original languageEnglish (US)
Title of host publication2021 IEEE/ACM 29th International Symposium on Quality of Service, IWQOS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781665414944
ISBN (Print)978-1-6654-3054-8
DOIs
StatePublished - Jun 25 2021
Externally publishedYes
Event29th IEEE/ACM International Symposium on Quality of Service, IWQOS 2021 - Virtual, Tokyo, Japan
Duration: Jun 25 2021Jun 28 2021

Publication series

Name2021 IEEE/ACM 29th International Symposium on Quality of Service, IWQOS 2021

Conference

Conference29th IEEE/ACM International Symposium on Quality of Service, IWQOS 2021
Country/TerritoryJapan
CityVirtual, Tokyo
Period6/25/216/28/21

Keywords

  • Image quality
  • Visualization
  • Superresolution
  • Urban areas
  • Quality of service
  • Sensors
  • Planning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

Fingerprint

Dive into the research topics of 'SuperClass: A Deep Duo-Task Learning Approach to Improving QoS in Image-driven Smart Urban Sensing Applications'. Together they form a unique fingerprint.

Cite this