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Domain Adaptation through Photorealistic Enhanced Images for Semantic Segmentation

Author

Listed:
  • Takafumi Katayama
  • Tian Song
  • Xiantao Jiang
  • Jenq-Shiou Leu
  • Takashi Shimamoto
  • Adriana del Carmen Téllez-Anguiano

Abstract

In this paper, three types of domain adaptation which are defined as image-level domain adaptation, interdomain adaptation, and intradomain adaptation are efficiently combined to construct a high efficiency framework for semantic segmentation. The proposed domain adaptation platform can achieve a high reduction of time-consuming to generate exhausted supervised data in the real world using photorealistic images. The proposed framework achieved a mean Intersection-over-Union (mIoU) of 45.0%. Furthermore, by combining the proposed method with intradomain adaptation, the improvement of 1.2% mIoU is achieved compared to previous work.

Suggested Citation

  • Takafumi Katayama & Tian Song & Xiantao Jiang & Jenq-Shiou Leu & Takashi Shimamoto & Adriana del Carmen Téllez-Anguiano, 2022. "Domain Adaptation through Photorealistic Enhanced Images for Semantic Segmentation," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-8, July.
    • Handle: RePEc:hin:jnlmpe:1848857
    DOI: 10.1155/2022/1848857
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