Self-Mining the Confident Prototypes for Source-Free Unsupervised Domain Adaptation in Image Segmentation-Reference-Cited by-同舟云学术

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Self-Mining the Confident Prototypes for Source-Free Unsupervised Domain Adaptation in Image Segmentation

Published:2024 Issue: Volume:26 Page:7709-7720
ISSN:1520-9210
Container-title:IEEE Transactions on Multimedia
language:
Short-container-title:IEEE Trans. Multimedia

Author:

Tian Yuntong¹^ORCID,Li Jiaxi²^ORCID,Fu Huazhu³^ORCID,Zhu Lei⁴^ORCID,Yu Lequan⁵^ORCID,Wan Liang¹^ORCID

Affiliation:

1. College of Intelligence and Computing, Tianjin University, Tianjin, China

2. Medical School, Tianjin University, Tianjin, China

3. Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore

4. The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

5. Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China

Funder

Natural Science Foundation of Tianjin Municipality

Guangzhou Municipal Science and Technology Project

National Natural Science Fund

Innovation and Technology Commission

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6046/10384483/10452765.pdf?arnumber=10452765

Reference55 articles.

1. Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation

2. Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training

3. Adversarial Mixup Ratio Confusion for Unsupervised Domain Adaptation

4. Model Adaptation: Unsupervised Domain Adaptation Without Source Data

5. Do we really need to access the source data? Source hypothesis transfer for unsupervised domain adaptation;Liang,2020

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