PrototypeFormer: Learning to Explore Prototype Relationships for
  Few-shot Image Classification
Feihong He Gang Li Lingyu Si Leilei Yan Fanzhang Li Fuchun Sun
Few-shot image classification has received considerable attention for
addressing the challenge of poor classification performance with limited
samples in novel classes. However, numerous studies have employed sophisticated
learning strategies and diversified feature extraction methods to address this
issue. In this paper, we propose our method called PrototypeFormer, which aims
to significantly advance traditional few-shot image classification approaches
by exploring prototype relationships. Specifically, we utilize a transformer
architecture to build a prototype extraction module, aiming to extract class
representations that are more discriminative for few-shot classification.
Additionally, during the model training process, we propose a contrastive
learning-based optimization approach to optimize prototype features in few-shot
learning scenarios. Despite its simplicity, the method performs remarkably
well, with no bells and whistles. We have experimented with our approach on
several popular few-shot image classification benchmark datasets, which shows
that our method outperforms all current state-of-the-art methods. In
particular, our method achieves 97.07% and 90.88% on 5-way 5-shot and 5-way
1-shot tasks of miniImageNet, which surpasses the state-of-the-art results with
accuracy of 7.27% and 8.72%, respectively. The code will be released later.