SAPENet: Self-Attention based Prototype Enhancement Network for Few-shot Learning

Xilang Huang, Seon Han Choi

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Few-shot learning considers the problem of learning unseen categories given only a few labeled samples. As one of the most popular few-shot learning approaches, Prototypical Networks have received considerable attention owing to their simplicity and efficiency. However, a class prototype is typically obtained by averaging a few labeled samples belonging to the same class, which treats the samples as equally important and is thus prone to learning redundant features. Herein, we propose a self-attention based prototype enhancement network (SAPENet) to obtain a more representative prototype for each class. SAPENet utilizes multi-head self-attention mechanisms to selectively augment discriminative features in each sample feature map, and generates channel attention maps between intra-class sample features to attentively retain informative channel features for that class. The augmented feature maps and attention maps are finally fused to obtain representative class prototypes. Thereafter, a local descriptor-based metric module is employed to fully exploit the channel information of the prototypes by searching k similar local descriptors of the prototype for each local descriptor in the unlabeled samples for classification. We performed experiments on multiple benchmark datasets: miniImageNet, tieredImageNet, and CUB-200-2011. The experimental results on these datasets show that SAPENet achieves a considerable improvement compared to Prototypical Networks and also outperforms related state-of-the-art methods.

Original languageEnglish
Article number109170
JournalPattern Recognition
StatePublished - Mar 2023

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) funded by the Korea Government (ministry of Science and ICT) under Grand 2022R1F1A1066267 .

Publisher Copyright:
© 2022 Elsevier Ltd


  • Few-shot learning
  • Image classification
  • k-Nearest neighbor
  • Multi-head self-attention mechanism


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