TMNIO:Triplet merged network with involution operators for improved few‐shot image classification

Abstract Few‐shot learning enables machines to learn efficiently from limited labelled data. However, existing few‐shot learning methods may perform poorly when there is a lack of sufficient samples, and may encounter problems such as domain shift or overfitting when applied to new domains or tasks....

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Bibliografski detalji

Glavni autori:	Qi Lulu, Xu Ranhui, Zhao Shaojie, Zheng Mingming, Yu Weiqin
Format:	Artigo
Jezik:	Inglês
Izdano:	Wiley 2024-05-01
Serija:	IET Image Processing
Teme:	image classification few‐shot learning contrastive learning prototypical networks
Online pristup:	https://doi.org/10.1049/ipr2.13055
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