Task-Adaptive Embedding Learning with Dynamic Kernel Fusion for Few-Shot Remote Sensing Scene Classification

Task-Adaptive Embedding Learning with Dynamic Kernel Fusion for Few-Shot Remote Sensing Scene Classification

The central goal of few-shot scene classification is to learn a model that can generalize well to a novel scene category (UNSEEN) from only one or a few labeled examples. Recent works in the Remote Sensing (RS) community tackle this challenge by developing algorithms in a meta-learning manner. Howev...

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Bibliographic Details
Main Authors:	Pei Zhang, Guoliang Fan, Chanyue Wu, Dong Wang, Ying Li
Format:	Article
Language:	English
Published:	MDPI AG 2021-10-01
Series:	Remote Sensing
Subjects:	remote-sensing classification scene classification few-shot learning meta-learning vision transformers multiscale feature fusion
Online Access:	https://www.mdpi.com/2072-4292/13/21/4200

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