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ORCIDBin Song 0010
Person information
- affiliation: Xi'an University of Technology, College of Automation and Information Engineering, China
- affiliation: China Aerospace Science and Industry Corporation, Defense Technology Second Academy, Missile Control Division, Beijing, China
Other persons with the same name
- Bin Song — disambiguation page
- Bin Song 0001
![[0000-0002-8096-3370]](https://dblp.uni-trier.de./img/orcid-mark.12x12.png "0000-0002-8096-3370")
— Xidian University, State Key Laboratory of Integrated Services Networks, Xi'an, China - Bin Song 0002 — Sichuan University West China Hospital, Department of Radiology, Chengdu, China
- Bin Song 0003 — The University of Utah, Salt Lake City, UT, USA
- Bin Song 0004 — Pingdingshan University, Ceramics College, Pingdingshan, China (and 1 more)
- Bin Song 0005 — Fudan University, Zhongshan Hospital, Shanghai, China
- Bin Song 0006 — Ilmenau University of Technology, Ilmenau, Germany
- Bin Song 0007 — Henan University of Science and Technology, Luoyang, China
- Bin Song 0008 — Beihang University, School of Mechanical Engineering & Automation, Beijing, China
- Bin Song 0009 — University of Florida, Department of Computer and Information Science and Engineering, Gainesville, FL, USA (and 1 more)
- Bin Song 0011 — Jiangxi Normal University, School of Geography and Environment, Nanchang, China
- Bin Song 0012 — Singapore Institute of Manufacturing Technology, Product Lifecycle Management Program, SIMTech, Singapore
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2020 – today
- 2025
[j4]Yuandong Bi, Hong Zhu, Jing Shi, Bin Song:
TsCANet: Three-stream contrastive adaptive network for cross-domain few-shot learning. J. Supercomput. 81(1): 139 (2025)- 2024
- [j3]Bin Song, Hong Zhu, Yuandong Bi:
A conditioned feature reconstruction network for few-shot classification. Appl. Intell. 54(8): 6592-6605 (2024) - [j2]Bin Song, Hong Zhu, Bingxin Wang, Yuandong Bi:
Class feature Sub-space for few-shot classification. Appl. Intell. 54(19): 9177-9194 (2024) - [j1]Yuandong Bi, Hong Zhu, Jing Shi, Bin Song:
R2Net: relative relation network with intra-class local augmentation for few-shot learning. Signal Image Video Process. 18(6-7): 5061-5071 (2024)
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