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
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2021-11, Vol.13 (21), p.4200
Main Authors: Zhang, Pei, Fan, Guoliang, Wu, Chanyue, Wang, Dong, Li, Ying
Format: Article
Language:English
Subjects:
Ablation
Adaptation
Algorithms
Classification
Deep learning
Embedding
few-shot learning
Image classification
Kernels
Learning
Machine learning
meta-learning
multiscale feature fusion
Remote sensing
remote-sensing classification
scene classification
Supervision
Training
vision transformers
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Summary: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. However, most prior approaches have either focused on rapidly optimizing a meta-learner or finding good similarity metrics while overlooking the embedding power. Here we propose a novel Task-Adaptive Embedding Learning (TAEL) framework that complements the existing methods by giving full play to feature embedding’s dual roles in few-shot scene classification—representing images and constructing classifiers in the embedding space. First, we design a Dynamic Kernel Fusion Network (DKF-Net) that enriches the diversity and expressive capacity of embeddings by dynamically fusing information from multiple kernels. Second, we present a task-adaptive strategy that helps to generate more discriminative representations by transforming the universal embeddings into task-adaptive embeddings via a self-attention mechanism. We evaluate our model in the standard few-shot learning setting on two challenging datasets: NWPU-RESISC4 and RSD46-WHU. Experimental results demonstrate that, on all tasks, our method achieves state-of-the-art performance by a significant margin.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs13214200