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- W4313016971 abstract "At present, most few-shot learning faces some difficulties. On the one hand, during feature extraction, the feature information extraction is insufficient due to the single extraction scale. Another problem is that it is difficult to accurately extract the important information content in the image. To this end, a few-shot learning method-MSIFA is proposed. In general, this strategy mainly utilizes the designed multi-scale feature generation module MSFGM to generate feature information about samples at multiple scales, so as to enrich the feature representation of samples. Next, the SAFAM module constructed by the self-attention mechanism extracts the important feature information of the samples at various scales. Afterwards, these important feature information is spliced and combined as a more accurate feature expression of the sample. Extensive experiments are performed on multiple standard datasets. Experimental results show that our method can not only greatly improve the classification performance of baseline methods, but also surpass most advanced few-shot learning methods." @default.
- W4313016971 created "2023-01-05" @default.
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- W4313016971 date "2022-01-01" @default.
- W4313016971 modified "2023-10-16" @default.
- W4313016971 title "Few-Shot Image Classification Method Based on Fusion of Important Features of Different Scales" @default.
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- W4313016971 doi "https://doi.org/10.1007/978-3-031-20865-2_38" @default.
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