A Bi-Prototype BDC Metric Network With Lightweight Adaptive Task Attention for Few-Shot Fine-Grained Ship Classification in Remote Sensing Images

Author:

Gao Gui1ORCID,Zhou Ping1ORCID,Yao Libo2ORCID,Liu Jia3,Zhang Chuan1ORCID,Duan Dingfeng1ORCID

Affiliation:

1. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China

2. Institute of Information Fusition, Naval Aviation University of China, Yantai, China

3. Department of Engineering on Scientific and Technological Systems, DFH Satellite Company Ltd., Beijing, China

Funder

National Nature Science Foundation of China

Innovation Team of the Ministry of Education of China

Innovation Group of Sichuan Natural Science Foundation

Fundamental Research Funds for the Central Universities

CAST Innovation Foundation

State Key Laboratory of Geo-Information Engineering

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Reference53 articles.

1. Low-Shot Learning from Imaginary Data

2. F-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning

3. Model-agnostic meta-learning for fast adaptation of deep networks;finn;Proc Int Conf Mach Learn,2017

4. Instance Credibility Inference for Few-Shot Learning

5. Visualizing data using t-SNE;van der maaten;J Mach Learn Res,2008

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1. SGK-Net: A Novel Navigation Scene Graph Generation Network;Sensors;2024-07-03

2. An Improved Deep Neural Network for Small-Ship Detection in SAR Imagery;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

3. A Weak Supervision Learning Paradigm for Oriented Ship Detection in SAR Image;IEEE Transactions on Geoscience and Remote Sensing;2024

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