Decompose, Compare, and Decide: Multimodal LLMs are Implicit Few-Shot Learners

arXiv cs.CV1mo4 min read

arXiv:2607.00125v1 Announce Type: new 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 suppor

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Decompose, Compare, and Decide: Multimodal LLMs are Implicit Few-Shot Learners

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