Weather Attribute-Aware Multi-Scale Image Generation with Residual Learning

Wei Ta Chu, Li Wei Huang

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

Abstract

We present image generation networks to generate images conforming to specified weather attributes. Taking weather attributes as the conditions, the proposed networks generate scene images with the help of a guided reference image. To generate higher-resolution images, we construct a multi-scale generation framework consisting of a global generator and a local enhancer. Furthermore, we integrate the idea of residual learning into the proposed framework, and aim at generating fine-grained texture. The evaluation shows performance comparison both from quantitative and qualitative perspectives. A comprehensive study including the impact of different attributes and extension of the proposed models is also provided. This work is kind of hybrid approach among various image generation studies.

Original languageEnglish
Title of host publicationIndo - Taiwan 2nd International Conference on Computing, Analytics and Networks, Indo-Taiwan ICAN 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages238-243
Number of pages6
ISBN (Electronic)9781728149998
DOIs
Publication statusPublished - 2020 Feb
EventIndo - Taiwan 2nd International Conference on Computing, Analytics and Networks, Indo-Taiwan ICAN 2020 - Rajpura, Punjab, India
Duration: 2020 Feb 72020 Feb 15

Publication series

NameIndo - Taiwan 2nd International Conference on Computing, Analytics and Networks, Indo-Taiwan ICAN 2020 - Proceedings

Conference

ConferenceIndo - Taiwan 2nd International Conference on Computing, Analytics and Networks, Indo-Taiwan ICAN 2020
CountryIndia
CityRajpura, Punjab
Period20-02-0720-02-15

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management

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