Decompose, Compare, and Decide: Multimodal LLMs are Implicit Few-Shot Learners
Researchers propose DeCoDe, a technique that decomposes few-shot image classification into pairwise comparisons, enabling off-the-shelf multimodal LLMs to become powerful few-shot classifiers without additional training. It outperforms state-of-the-art methods on twelve datasets, with code open-sourced.
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[Submitted on 30 Jun 2026]
Title:Decompose, Compare, and Decide: Multimodal LLMs are Implicit Few-Shot Learners
View a PDF of the paper titled Decompose, Compare, and Decide: Multimodal LLMs are Implicit Few-Shot Learners, by Yunhan Wang and 5 other authors
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Abstract:Multimodal Large Language Models (MLLMs) have demonstrated remarkable abilities when analyzing images, yet translating these capabilities to few-shot image classification remains challenging. To bridge this gap, we present DeCoDe, a simple yet effective technique that enables off-the-shelf MLLMs to act as strong few-shot classifiers without any additional training. Our approach builds on the idea of few-shot classification as a set of pairwise image comparisons, decomposing the task into a set of binary decisions. Given a query image and a support image from a candidate class, the MLLM is prompted to decide whether the two images depict the same class. The logit corresponding to an affirmative response is then used as a similarity score to assign the query image to the most likely class. While this already yields good results, we show that providing additional high-level information, such as the data domain, to the model further improves performance. Our evaluation provides an extensive analysis of various inference variants on a suite of twelve datasets, six established and six newly curated few-shot benchmarks spanning across diverse domains. The results show that the proposed simple decomposition technique can turn off-the-shelf MLLMs into powerful few-shot learners, significantly outperforming current state-of-the-art few-shot methods on both standard and novel domains. Code is available at this https URL.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.00125 [cs.CV]
(or arXiv:2607.00125v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.00125
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yunhan Wang [view email] [v1] Tue, 30 Jun 2026 20:00:50 UTC (5,564 KB)
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