Train One, Generalize to All: Generalizable Semantic Segmentation from Single-Scene to All Adverse Scenes-Reference-Cited by-同舟云学术

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Train One, Generalize to All: Generalizable Semantic Segmentation from Single-Scene to All Adverse Scenes

Published:2023-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Multimedia
language:
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Author:

Gong Ziyang¹^ORCID,Li Fuhao²^ORCID,Deng Yupeng³^ORCID,Shen Wenjun⁴^ORCID,Ma Xianzheng⁵^ORCID,Ji Zhenming¹^ORCID,Xia Nan⁶^ORCID

Affiliation:

1. Sun Yat-sen University, Zhuhai, China

2. Wuhan University of Science and Technology, Wuhan, China

3. National University of Singapore, Singapore, Singapore

4. Wuhan College, Wuhan, China

5. Shanghai AI Laboratory, Shanghai, China

6. South China University of Technology, Guangzhou, China

Funder

Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3612387

Reference58 articles.

1. Self-supervised Augmentation Consistency for Adapting Semantic Segmentation

2. Martin Arjovsky , Léon Bottou , Ishaan Gulrajani , and David Lopez-Paz . 2019. Invariant risk minimization. arXiv preprint arXiv:1907.02893 ( 2019 ). Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019. Invariant risk minimization. arXiv preprint arXiv:1907.02893 (2019).

3. Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions

4. All About Structure: Adapting Structural Information Across Domains for Boosting Semantic Segmentation

5. Learning Semantic Segmentation From Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach

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