Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains

We investigate cross-domain few-shot learning under the constraint that fine-tuning of backbones (i.e., feature extractors) is impossible or infeasible---a scenario that is increasingly common in practical use cases---and show how representing few-shot classification as a series of multiple instance