2022
DOI: 10.1007/978-3-031-09282-4_18
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A Hierarchical Prototypical Network for Few-Shot Remote Sensing Scene Classification

Abstract: Few-shot learning (FSL) aims at making predictions based on a limited number of labeled samples. It is a hot topic in many fields such as natural language processing, computer vision and more recently, remote sensing. In this work, we focus on few-shot remote sensing scene classification which aims to recognize unseen scene categories at training stage from few or even a single labeled sample at test stage. Although considerable progress has been achieved in this topic, less attention has been paid to leveragi… Show more

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Cited by 3 publications
(2 citation statements)
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“…3 The three core strategies for supervised hyperbolic learning in computer vision. Current literature performs hyperbolic learning of visual embeddings by learning to match training samples (i) to hyperbolic class hyperplanes, i.e., gyroplanes, (ii) to hyperbolic class prototypes, or (iii) by contrasting to other samples tion (Huang et al, 2023), skeletal data (Franco et al, 2023;Chen et al, 2023), LiDAR data (Tong et al, 2022;, point clouds (Montanaro et al, 2022;Anvekar & Bazazian, 2023;Lin et al, 2023b;Onghena et al, 2023), 3D shapes (Chen et al, 2020b;Onghena et al, 2023;Leng et al, 2023), and remote sensing data (Hamzaoui et al, 2023). In summary, hyperbolic geometry has impacted a wide range of research fields.…”
Section: Non-visual Hyperbolic Learningmentioning
confidence: 99%
“…3 The three core strategies for supervised hyperbolic learning in computer vision. Current literature performs hyperbolic learning of visual embeddings by learning to match training samples (i) to hyperbolic class hyperplanes, i.e., gyroplanes, (ii) to hyperbolic class prototypes, or (iii) by contrasting to other samples tion (Huang et al, 2023), skeletal data (Franco et al, 2023;Chen et al, 2023), LiDAR data (Tong et al, 2022;, point clouds (Montanaro et al, 2022;Anvekar & Bazazian, 2023;Lin et al, 2023b;Onghena et al, 2023), 3D shapes (Chen et al, 2020b;Onghena et al, 2023;Leng et al, 2023), and remote sensing data (Hamzaoui et al, 2023). In summary, hyperbolic geometry has impacted a wide range of research fields.…”
Section: Non-visual Hyperbolic Learningmentioning
confidence: 99%
“…The work presented by Hamzaoui et al (2022) proposed a hierarchical prototypical network (HPN) as a novel approach for few-shot learning, which is evaluated on the RESISC45 dataset. The HPN model is designed to perform analysis of high-level aggregated information in the image, followed by fine-level aggregated information computation and prediction, utilizing prototypes associated with each level of the hierarchy as described in (4).…”
Section: Few-shot Learning In Vhr Image Classificationmentioning
confidence: 99%