Knowledge-aware Few-shot Learning Framework for Biomedical Event Trigger Identification

Yin, Shujuan; Zhao, Weizhong; Jiang, Xingpeng; He, Tingting

doi:10.1109/bibm49941.2020.9313195

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…The Basic ideas of each few-shot learning are shown in Table 1. At present, with the continuous development and optimization in few-shot learning, a large quantity of application cases of few-shot learning have emerged in many industries, such as remote sensing computer vision (Sun et al 2021;Guo, Wang et al 2022), intelligent agriculture Nie, Wang et al 2022a;), biomedicine (Yin et al 2020), plant protection Li & Chao, 2021b), magnetic field physical and chemical parameter prediction (Nie et al 2021;Nie et al 2022), geological exploration (Liu et al 2022), radar ranging (Yue Yang et al 2021), point cloud segmentation (Guo et al 2020), etc. This article will outline and respond to the research questions below: 1) How is the present state of studies about few-shot learning?…”

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confidence: 99%

Few-shot learning in intelligent agriculture: A review of methods and applications

NİE,

YUAN,

Lİ

et al. 2023

J Agr Sci-Tarim Bili

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Due to the high cost of data acquisition in many specific fields, such as intelligent agriculture, the available data is insufficient for the typical deep learning paradigm to show its superior performance. As an important complement to deep learning, few-shot learning focuses on pattern recognition tasks under the constraint of limited data, which can be used to solve practical problems in many application fields with data scarcity. This survey summarizes the research status, main models and representative achievements of few-shot learning from four aspects: model fine-tuning, meta-learning, metric learning and data enhancement, and especially introduces the few-shot learning-driven typical applications in intelligent agriculture. Finally, the current challenges of few-shot learning and its development trends in intelligent agriculture are prospected.

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