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• W3199011505 abstract "Traditional fine-grained image classification generally requires abundant labeled samples to deal with the low inter-class variance but high intra-class variance problem. However, in many scenarios we may have limited samples for some novel sub-categories, leading to the fine-grained few shot learning (FG-FSL) setting. To address this challenging task, we propose a novel method named foreground object transformation (FOT), which is composed of a foreground object extractor and a posture transformation generator. The former aims to remove image background, which tends to increase the difficulty of fine-grained image classification as it amplifies the intra-class variance while reduces inter-class variance. The latter transforms the posture of the foreground object to generate additional samples for the novel sub-category. As a data augmentation method, FOT can be conveniently applied to any existing few shot learning algorithm and greatly improve its performance on FG-FSL tasks. In particular, in combination with FOT, simple fine-tuning baseline methods can be competitive with the state-of-the-art methods both in inductive setting and transductive setting. Moreover, FOT can further boost the performances of latest excellent methods and bring them up to the new state-of-the-art. In addition, we also show the effectiveness of FOT on general FSL tasks." @default.
• W3199011505 created "2021-09-27" @default.
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• W3199011505 date "2021-11-01" @default.
• W3199011505 modified "2023-10-16" @default.
• W3199011505 title "Fine-grained few shot learning with foreground object transformation" @default.
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• W3199011505 doi "https://doi.org/10.1016/j.neucom.2021.09.016" @default.
• W3199011505 hasPublicationYear "2021" @default.
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