TransUNet for Cross-Domain Semantic Segmentation of Urban Scenery

Wei Yuen Teh, Ian K. T. Tan

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


TransUNet is a hybrid architecture that combines a transformer-based encoder with a CNN-based UNet. Originally introduced for semantic segmentation of medical images, we show in our work that TransUNet can be successfully applied to urban scenery datasets commonly used for developing autonomous driving systems. We also explore the performance characteristics of training on multi-domain data from the real world and a simulator, and show that using simulated images to augment a live dataset can improve segmentation performance. Code will be made available at
Original languageEnglish
Title of host publication2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)
ISBN (Electronic)9798350332421
Publication statusPublished - 31 Mar 2023


  • Autonomous Driving
  • Domain Adaptation
  • Semantic Segmentation
  • Urban Scenery
  • Vision Transformer

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing


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