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Boosting Few-Shot Image Classification with Feature Map Reconstruction Network via Statistical Information

Published: 07 September 2023 Publication History

Abstract

Few-shot image classification aims to recognize classes with limited labeled data. Many works have been proposed to solve this problem. Recently, the feature map reconstruction network has attracted great attention. However, due to the scarcity of data, it is difficult to find an exact solution to the reconstructed model. We try to use statistical information to draw the support set distribution and query set distribution closer, which helps the model obtain a precise solution. We consider first-order information, calculate the mean of each set and eliminate the bias between them. In addition to one-dimensional information, high-dimensional information (i.e. the covariance matrix) is used to represent the data distribution and make Euclidean distance of them as the loss. Our statistical information feature reconstruction network(SI-FRN) does not bring in any other supervised information. Compared to recent state-of-the-art few-shot image classification methods with reconstruction, our SI-FRN gets excellent performance on four benchmarks: mini-ImageNet, tiered-ImageNet, CUB and Aircraft.

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  1. Boosting Few-Shot Image Classification with Feature Map Reconstruction Network via Statistical Information

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      ICMLC '23: Proceedings of the 2023 15th International Conference on Machine Learning and Computing
      February 2023
      619 pages
      ISBN:9781450398411
      DOI:10.1145/3587716
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      Published: 07 September 2023

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      Author Tags

      1. computer vision
      2. few-shot learning
      3. image classification
      4. statistical information

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