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 verification (MIV) tasks enables competitive classification performance using the static embeddings produced by frozen, "black-box" backbones. More specifically, based on this novel representation, we introduce a new approach to few-shot domain adaptation named the "MIV-head": a classification head that is backbone-agnostic and computationally efficient. We show that the core components of the MIV-head, when trained on few-shot data from a target domain, collectively yield strong performance on test data from that domain. Importantly, they do so without fine-tuning the backbone, requiring only the "meta-testing" phase. In experiments under various settings on an extension of the Meta-Dataset benchmark for cross-domain few-shot image classification, using representative off-the-shelf convolutional neural network and vision transformer backbones pretrained on ImageNet1K, we demonstrate that the MIV-head achieves highly competitive accuracy when compared to state-of-the-art "adapter" (or "partial fine-tuning") methods applied to the same backbones, while incurring substantially lower adaptation cost. Moreover, compared to our approach, well-known "classification head" approaches achieve a substantially lower level of accuracy. We share our code at: https://github.com/xxweka/MIV-head.

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