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

2025-06-30

AI Summary

This paper addresses cross-domain few-shot learning under frozen backbone networks (CNNs or Vision Transformers), proposing a general, fine-tuning-free solution. Methodologically, it reformulates few-shot classification as a multi-instance verification (MIV) task—the first such formulation—and introduces a lightweight, backbone-agnostic MIV-head module. This module operates solely during meta-testing, performing pairwise similarity verification between target-domain support and query instances without updating any backbone parameters. Evaluated on the extended Meta-dataset benchmark, our approach matches the performance of leading adaptation methods while substantially outperforming conventional linear classification heads; notably, its adaptation overhead is reduced by an order of magnitude. The work establishes an efficient, plug-and-play paradigm for few-shot transfer learning with black-box feature extractors, enabling rapid domain adaptation without backbone modification.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Adversarial Attacks & Robustness
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Cross-domain few-shot learning without fine-tuning backbones
• Handling low-quality static embeddings via multi-instance verification
• Achieving competitive accuracy with lower adaptation cost

Innovation

• Uses multi-instance verification for few-shot learning
• Introduces backbone-agnostic MIV-head for domain adaptation
• Achieves high accuracy without fine-tuning backbones