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• W4281917143 abstract "Few-shot and cross-domain land use scene classification methods propose solutions to classify unseen classes or un-seen visual distributions, but are hardly applicable to real-world situations due to restrictive assumptions. Few-shot methods involve episodic training on restrictive training subsets with small feature extractors, while cross-domain methods are only applied to common classes. The underlying challenge remains open: can we accurately classify new scenes on new datasets? In this paper, we propose a new framework for few-shot, cross-domain classification. Our retrieval-inspired approach <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sup> exploits the interrelations in both the training and testing data to output class labels using compact descriptors. Results show that our method can accurately produce land-use predictions on unseen datasets and unseen classes, going beyond the traditional few-shot or cross-domain formulation, and allowing cross-dataset training." @default.
• W4281917143 created "2022-06-13" @default.
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• W4281917143 date "2022-06-01" @default.
• W4281917143 modified "2023-10-02" @default.
• W4281917143 title "Cross-dataset Learning for Generalizable Land Use Scene Classification" @default.
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• W4281917143 doi "https://doi.org/10.1109/cvprw56347.2022.00144" @default.
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