VaMP: Variational Multi-Modal Prompt Learning for Vision-Language Models

• URL: http://arxiv.org/abs/2511.22664v1
• Date: Thu, 27 Nov 2025 17:57:39 GMT
• Title: VaMP: Variational Multi-Modal Prompt Learning for Vision-Language Models
• Authors: Silin Cheng, Kai Han,
• Abstract summary: We propose a novel Variational Multi-Modal Prompt Learning (VaMP) framework that enables sample-specific, uncertainty-aware prompt tuning.<n>VaMP generates instance-conditioned prompts by sampling from a learned posterior distribution.<n>We show that VaMP achieves state-of-the-art performance, highlighting the benefits of modeling both uncertainty and task structure in our method.
• Score: 14.275832364410247
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Vision-language models (VLMs), such as CLIP, have shown strong generalization under zero-shot settings, yet adapting them to downstream tasks with limited supervision remains a significant challenge. Existing multi-modal prompt learning methods typically rely on fixed, shared prompts and deterministic parameters, which limits their ability to capture instance-level variation or model uncertainty across diverse tasks and domains. To tackle this issue, we propose a novel Variational Multi-Modal Prompt Learning (VaMP) framework that enables sample-specific, uncertainty-aware prompt tuning in multi-modal representation learning. VaMP generates instance-conditioned prompts by sampling from a learned posterior distribution, allowing the model to personalize its behavior based on input content. To further enhance the integration of local and global semantics, we introduce a class-aware prior derived from the instance representation and class prototype. Building upon these, we formulate prompt tuning as variational inference over latent prompt representations and train the entire framework end-to-end through reparameterized sampling. Experiments on few-shot and domain generalization benchmarks show that VaMP achieves state-of-the-art performance, highlighting the benefits of modeling both uncertainty and task structure in our method. Project page: https://visual-ai.github.io/vamp

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