SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation-Reference-Cited by-同舟云学术

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SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation

Published:2022-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
language:
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Author:

Sun Tao¹,Segu Mattia¹,Postels Janis¹,Wang Yuxuan¹,Van Gool Luc¹,Schiele Bernt²,Tombari Federico³,Yu Fisher¹

Affiliation:

1. ETH Zürich

2. MPI Informatics

3. Google

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9878378/9878366/09880320.pdf?arnumber=9880320

Reference96 articles.

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

2. Semantic Foggy Scene Understanding with Synthetic Data

3. Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding

4. Semi-Supervised Domain Adaptation via Minimax Entropy

5. Scalability in Perception for Autonomous Driving: Waymo Open Dataset

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