Few-Shot Learning Meets Transformer: Unified Query-Support Transformers for Few-Shot Classification. (arXiv:2208.12398v1 [cs.CV])

Few-shot classification which aims to recognize unseen classes using very
limited samples has attracted more and more attention. Usually, it is
formulated as a metric learning problem. The core issue of few-shot
classification is how to learn (1) consistent representations for images in
both support and query sets and (2) effective metric learning for images
between support and query sets. In this paper, we show that the two challenges
can be well modeled simultaneously via a unified Query-Support TransFormer
(QSFormer) …

arxiv classification cv few-shot learning learning query support transformer transformers