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Few-shot learning (FSL) is an important and topical problem in computervision that has motivated extensive research into numerous methods spanningfrom sophisticated meta-learning methods to simple transfer learning baselines.We seek to push the limits of a simple-but-effective pipeline for morerealistic and practical settings of few-shot image classification. To this end,we explore few-shot learning from the perspective of neural networkarchitecture, as well as a three stage pipeline of network updates underdifferent data supplies, where unsupervised external data is considered forpre-training, base categories are used to simulate few-shot tasks formeta-training, and the scarcely labelled data of an novel task is taken forfine-tuning. We investigate questions such as: (1) How pre-training on externaldata benefits FSL? (2) How state-of-the-art transformer architectures can beexploited? and (3) How fine-tuning mitigates domain shift? Ultimately, we showthat a simple transformer-based pipeline yields surprisingly good performanceon standard benchmarks such as Mini-ImageNet, CIFAR-FS, CDFSL and Meta-Dataset.Our code and demo are available at https://hushell.github.io/pmf.

Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference