Connecting Giants: Synergistic Knowledge Transfer of Large Multimodal Models for Few-Shot Learning

• URL: http://arxiv.org/abs/2510.11115v1
• Date: Mon, 13 Oct 2025 08:06:23 GMT
• Title: Connecting Giants: Synergistic Knowledge Transfer of Large Multimodal Models for Few-Shot Learning
• Authors: Hao Tang, Shengfeng He, Jing Qin,
• Abstract summary: Few-shot learning addresses the challenge of classifying novel classes with limited training samples.<n>We propose a novel framework, Synergistic Knowledge Transfer, which effectively transfers diverse and complementary knowledge from large multimodal models.<n>We show that SynTrans, even when paired with a simple few-shot vision encoder, significantly outperforms current state-of-the-art methods.
• Score: 61.73934102302588
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot learning (FSL) addresses the challenge of classifying novel classes with limited training samples. While some methods leverage semantic knowledge from smaller-scale models to mitigate data scarcity, these approaches often introduce noise and bias due to the data's inherent simplicity. In this paper, we propose a novel framework, Synergistic Knowledge Transfer (SynTrans), which effectively transfers diverse and complementary knowledge from large multimodal models to empower the off-the-shelf few-shot learner. Specifically, SynTrans employs CLIP as a robust teacher and uses a few-shot vision encoder as a weak student, distilling semantic-aligned visual knowledge via an unsupervised proxy task. Subsequently, a training-free synergistic knowledge mining module facilitates collaboration among large multimodal models to extract high-quality semantic knowledge. Building upon this, a visual-semantic bridging module enables bi-directional knowledge transfer between visual and semantic spaces, transforming explicit visual and implicit semantic knowledge into category-specific classifier weights. Finally, SynTrans introduces a visual weight generator and a semantic weight reconstructor to adaptively construct optimal multimodal FSL classifiers. Experimental results on four FSL datasets demonstrate that SynTrans, even when paired with a simple few-shot vision encoder, significantly outperforms current state-of-the-art methods.

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