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A conditioned feature reconstruction network for few-shot classification

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Abstract

Few-shot classification is one of the most daunting challenges in deep learning. The complexities of this task arise from the fact that category targets are often embedded within intricate and diverse background pixels, resulting in inconspicuous category features. Moreover, obtaining common category characteristics from a limited number of samples is difficult. Compounding the issue, models encounters categories that they have never seen before, rendering the prior guarantee of interclass variance infeasible. To address these dilemmas, this paper leverages the apriori conditioned information of few-shot tasks and introduces a Conditioned Feature Reconstruction Network (CFRN). The CFRN employs prototype reconstruction to minimize the prototype similarity among different classes and query reconstruction to maximize the similarity of (query, prototype) feature pairs. This approach increases the interclass variance while decreasing the intraclass variance, thereby enhancing separability and improving the saliency of the target features. An experimental validation demonstrates the effectiveness of the CFRN, which obtains state-of-the-art results on the mini-ImageNet, tiered-ImageNet, and CUB datasets.

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The data will be made available upon reasonable request.

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Authors and Affiliations

  1. College of Automation and Information Engineering, Xi’an University of Technology, No. 5, Jinhua South Road, Xi’an, 710048, Shaanxi Province, China

    Bin Song, Hong Zhu & Yuandong Bi

  2. Missile Control Division, China Aerospace Science and Industry Corporation Defense Technology Second Academy 706th Institute, No. 52, Yongding Road, Beijing, 100854, China

    Bin Song

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  1. Bin Song
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  2. Hong Zhu
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Contributions

Bin Song: design, implementation, formal analysis and writing. Hong Zhu: guidance, review and editing. Yuandong Bi: validation.

Corresponding author

Correspondence to Hong Zhu.

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No ethical approval or informed consent was necessary for this study, as the data were already publicly available and did not involve human or animal subjects.

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The paper is original in terms of its contents and is not under consideration for publication in any other journals/proceedings. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Song, B., Zhu, H. & Bi, Y. A conditioned feature reconstruction network for few-shot classification. Appl Intell (2024). https://doi.org/10.1007/s10489-024-05516-9

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