BiLSTM-TANet: an adaptive diverse scenes model with context embeddings for few-shot learning-Reference-Cited by-同舟云学术

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BiLSTM-TANet: an adaptive diverse scenes model with context embeddings for few-shot learning

Published:2024-03 Issue:6 Volume:54 Page:5097-5116
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Zhang He,Liu Han^ORCID,Liang Lili,Ma Wenlu,Liu Ding

Funder

Major Research Program of National Natural Science Foundation of China

Major Instrument Project of National Natural Science Foundation of China

The 2023 General Special Scientific Research Program of the Department of Education of Shaanxi Province

The Doctoral Scientific Research Startup Foundation of Xi’an University of Technology

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10489-024-05440-y.pdf

Reference56 articles.

1. Ma Y, Zhao S, Wang W et al (2022) Multimodality in meta-learning: A comprehensive survey. Knowl-Based Syst 250:108976

2. Guo R, Liu H, Xie G et al (2021) Weld defect detection from imbalanced radiographic images based on contrast enhancement conditional generative adversarial network and transfer learning. IEEE Sens J 21(9):10844–10853

3. Li X, Sun Z, Xue JH et al (2021) A concise review of recent few-shot meta-learning methods. Neurocomputing 456:463–468

4. Yang J, Guo X, Li Y et al (2022) A survey of few-shot learning in smart agriculture: developments, applications, and challenges. Plant Methods 18(1):1–12

5. Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. Adv Neural Inf Process Syst 30:4077–4087

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