A multi-scale hierarchical node graph neural network for few-shot learning-Reference-Cited by-同舟云学术

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A multi-scale hierarchical node graph neural network for few-shot learning

Published:2023-12-20 Issue: Volume: Page:
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Zhang Yan^ORCID,Zhou Xudong,Wang Ke,Wang Nian^ORCID,Li Zenghui

Funder

Anhui Provincial Key Research and Development Plan

Key Technologies Research and Development Program of Anhui Province

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-17059-1.pdf

Reference39 articles.

1. Tao T, Liu K, Wang L, Wu H (2015) Image recognition and analysis of intrauterine residues based on deep learning and semi-supervised learning. In: IEEE access, pp 162785–162799

2. Mane S, Mangale S (2018) Moving object detection and tracking using convolutional neural networks. In: 2018 second international conference on intelligent computing and control systems (ICICCS), pp 1809–1813

3. Tang J, Zhang F, Zhou Y, Yin Q, Hu W (2019) A fast inference networks for sar target few-shot learning based on improved siamese networks. In: IGARSS 2019–2019 IEEE international geoscience and remote sensing symposium, pp 1212–1215

4. Tyukin IY, Gorban AN, Alkhudaydi MH, Zhou Q (2021) Demystification of few-shot and one-shot learning. In: 2021 international joint conference on neural networks (IJCNN), pp 1–7

5. Yang Y, Li Y, Zhang R, Wang J, Miao Z (2020) Robust compare network for few-shot learning. In: 2020 IEEE access, pp 137966–137974

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