Learn From Segment Anything Model: Local Region Homogenizing for Cross-Domain Remote Sensing Image Segmentation-Reference-Cited by-同舟云学术

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Learn From Segment Anything Model: Local Region Homogenizing for Cross-Domain Remote Sensing Image Segmentation

Published:2024-07-07 Issue: Volume: Page:8351-8354
ISSN:
Container-title:IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Liu Wang¹,Duan Puhong²,Xie Zhuojun¹,Kang Xudong²,Li Shutao¹

Affiliation:

1. Hunan University,College of Electrical and Information Engineering,China

2. Hunan University,School of Robotics,China

Funder

Science Fund for Creative Research Groups

Research and Development

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

China Postdoctoral Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10640349/10640352/10642007.pdf?arnumber=10642007

Reference9 articles.

1. Learning to Adapt Structured Output Space for Semantic Segmentation

2. Classes Matter: A Fine-Grained Adversarial Approach to Cross-Domain Semantic Segmentation

3. DACS: Domain Adaptation via Cross-domain Mixed Sampling

4. DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation

5. Prototypical contrast adaptation fordomain adaptive semantic segmentation;Jiang

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