Towards Better Stability and Adaptability: Improve Online Self-Training for Model Adaptation in Semantic Segmentation-Reference-Cited by-同舟云学术

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Towards Better Stability and Adaptability: Improve Online Self-Training for Model Adaptation in Semantic Segmentation

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
language:
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Author:

Zhao Dong¹,Wang Shuang¹,Zang Qi¹,Quan Dou¹,Ye Xiutiao¹,Jiao Licheng¹

Affiliation:

1. School of Artificial Intelligence, Xidian University,Shaanxi,China

Funder

National Natural Science Foundation of China

National Key R&D Program of China

China Postdoctoral Science Foundation

111 Project

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10203037/10203050/10203421.pdf?arnumber=10203421

Reference64 articles.

1. Semi-supervised learning by entropy minimization;grandvalet;CAP,2005

2. Source Data-free Unsupervised Domain Adaptation for Semantic Segmentation

3. Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation

4. FDA: Fourier Domain Adaptation for Semantic Segmentation

5. Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation

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