Multi-modal Mutual-Guidance Conditional Prompt Learning for Vision-Language Models

• URL: http://arxiv.org/abs/2507.08410v1
• Date: Fri, 11 Jul 2025 08:45:27 GMT
• Title: Multi-modal Mutual-Guidance Conditional Prompt Learning for Vision-Language Models
• Authors: Shijun Yang, Xiang Zhang, Wanqing Zhao, Hangzai Luo, Sheng Zhong, Jinye Peng, Jianping Fan,
• Abstract summary: MuGCP (Multi-modal Mutual-Guidance Conditional Prompt Learning) is a novel paradigm designed for conditional prompt generation.<n> AMG module generates Visual Conditional Prompts (VCP), enhancing the model's performance in multi-modal tasks.<n>MPF mechanism integrates SCP andVCP with contextual prompts, ensuring seamless coordination.
• Score: 21.20658517302458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompt learning facilitates the efficient adaptation of Vision-Language Models (VLMs) to various downstream tasks. However, it faces two significant challenges: (1) inadequate modeling of class embedding distributions for unseen instances, leading to suboptimal generalization on novel classes; (2) prevailing methodologies predominantly confine cross-modal alignment to the final output layer of vision and text encoders, which fundamentally limits their capacity to preserve topological consistency with pre-trained multi-modal embedding spaces. To this end, we introduce MuGCP (Multi-modal Mutual-Guidance Conditional Prompt Learning), a novel paradigm designed for conditional prompt generation. MuGCP leverages Multi-modal Large Language Models (MLLMs) as conditional prompt learners to adaptively generate Semantic Conditional Prompts (SCP) that incorporate rich, fine-grained high-level semantic knowledge for image instances. To ensure effective alignment and interaction across the multi-modal space of Vision-Language Models (VLMs), we introduce the Attention Mutual-Guidance (AMG) module, which facilitates interactions between visual and semantic information. Through mutual guidance, the AMG module generates Visual Conditional Prompts (VCP), enhancing the model's performance in multi-modal tasks. Additionally, we present a Multi-Prompt Fusion (MPF) mechanism that integrates SCP and VCP with contextual prompts, ensuring seamless coordination among the different prompts and enhancing the modeling of class embeddings and instance-specific knowledge. Our MuGCP outperforms existing state-of-the-art methods on 14 different datasets. The code will be made available after publication.

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