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

• URL: http://arxiv.org/abs/2507.00401v1
• Date: Tue, 01 Jul 2025 03:34:20 GMT
• Title: Few-shot Classification as Multi-instance Verification: Effective Backbone-agnostic Transfer across Domains
• Authors: Xin Xu, Eibe Frank, Geoffrey Holmes,
• Abstract summary: Cross-domain few-shot learning is increasingly common in practical use cases.<n>Fine-tuning of backbones (i.e., feature extractors) is impossible or infeasible.<n>We introduce a novel approach to few-shot domain adaptation, named the "MIV-head"
• Score: 11.027466339522777
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Handling the low-quality and static embeddings produced by frozen, "black-box" backbones leads to a problem representation of few-shot classification as a series of multiple instance verification (MIV) tasks. Inspired by this representation, we introduce a novel approach to few-shot domain adaptation, named the "MIV-head", akin to a classification head that is agnostic to any pretrained backbone and computationally efficient. The core components designed for the MIV-head, when trained on few-shot data from a target domain, collectively yield strong performance on test data from that domain. Importantly, it does so without fine-tuning the backbone, and within the "meta-testing" phase. Experimenting under various settings and 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 show that the MIV-head achieves highly competitive accuracy when compared to state-of-the-art "adapter" (or partially fine-tuning) methods applied to the same backbones, while incurring substantially lower adaptation cost. We also find well-known "classification head" approaches lag far behind in terms of accuracy. Ablation study empirically justifies the core components of our approach. We share our code at https://github.com/xxweka/MIV-head.

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