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• W3206568644 abstract "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." @default.
• W3206568644 created "2021-10-25" @default.
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• W3206568644 date "2021-10-20" @default.
• W3206568644 modified "2023-09-26" @default.
• W3206568644 title "Task-Adaptive Embedding Learning with Dynamic Kernel Fusion for Few-Shot Remote Sensing Scene Classification" @default.
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• W3206568644 doi "https://doi.org/10.3390/rs13214200" @default.
• W3206568644 hasPublicationYear "2021" @default.
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