Multilevel Self-Training Approach for Cross-Domain Semantic Segmentation in Intelligent Vehicles-Reference-Cited by-同舟云学术

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Multilevel Self-Training Approach for Cross-Domain Semantic Segmentation in Intelligent Vehicles

Published:2024-01 Issue:1 Volume:16 Page:148-161
ISSN:1939-1390
Container-title:IEEE Intelligent Transportation Systems Magazine
language:
Short-container-title:IEEE Intell. Transport. Syst. Mag.

Author:

Chen Yung-Yao¹^ORCID,Jhong Sin-Ye²^ORCID

Affiliation:

1. Department of Electronic and Computer Engineering and the Smart Electric Vehicle Research Center, National Taiwan University of Science and Technology, Taipei, Taiwan

2. Department of Engineering Science, National Cheng Kung University, Tainan City, Taiwan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/5117645/10381489/10250815.pdf?arnumber=10250815

Reference60 articles.

1. Gated-Residual Block for Semantic Segmentation Using RGB-D Data

2. A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes

3. ACDC: The Adverse Conditions Dataset with Correspondences for Semantic Driving Scene Understanding

4. Playing for Data: Ground Truth from Computer Games

5. The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes

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