Classification Matters More: Global Instance Contrast for Fine-Grained SAR Aircraft Detection-Reference-Cited by-同舟云学术

Classification Matters More: Global Instance Contrast for Fine-Grained SAR Aircraft Detection

Author:

Zhao Danpei¹^ORCID,Chen Ziqiang¹^ORCID,Gao Yue¹^ORCID,Shi Zhenwei¹^ORCID

Affiliation:

1. Image Processing Center, School of Astronautics, Beihang University, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

Reference79 articles.

1. A simple framework for contrastive learning of visual representations;chen;Proc Int Conf Mach Learn,2020

2. Barlow twins: Self-supervised learning via redundancy reduction;zbontar;Proc Int Conf Mach Learn,2021

4. Bootstrap your own latent—A new approach to self-supervised learning;grill;Proc Adv Neural Inf Process Syst,2020

5. Improved baselines with momentum contrastive learning;chen;arXiv 2003 04297,2020

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Accurate SAR Aircraft Detection Algorithm Based on Feature Enhancement;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

2. Hyperspectral Image-Text Coupling Network For Hyperspectral Image Classification;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07