ChordPrompt: Orchestrating Cross-Modal Prompt Synergy for Multi-Domain Incremental Learning in CLIP

• URL: http://arxiv.org/abs/2506.19608v1
• Date: Tue, 24 Jun 2025 13:22:06 GMT
• Title: ChordPrompt: Orchestrating Cross-Modal Prompt Synergy for Multi-Domain Incremental Learning in CLIP
• Authors: Zhiyuan Wang, Bokui Chen,
• Abstract summary: Continual learning empowers pre-trained vision-language models to adapt effectively to novel or previously underrepresented data distributions.<n>ChordPrompt introduces cross-modal prompts to leverage interactions between visual and textual information.<n>ChordPrompt outperforms state-of-the-art methods in zero-shot generalization and downstream task performance.
• Score: 12.031278034659872
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Continual learning (CL) empowers pre-trained vision-language models to adapt effectively to novel or previously underrepresented data distributions without comprehensive retraining, enhancing their adaptability and efficiency. While vision-language models like CLIP show great promise, they struggle to maintain performance across domains in incremental learning scenarios. Existing prompt learning methods face two main limitations: 1) they primarily focus on class-incremental learning scenarios, lacking specific strategies for multi-domain task incremental learning; 2) most current approaches employ single-modal prompts, neglecting the potential benefits of cross-modal information exchange. To address these challenges, we propose the \ChordPrompt framework, which facilitates a harmonious interplay between visual and textual prompts. \ChordPrompt introduces cross-modal prompts to leverage interactions between visual and textual information. Our approach also employs domain-adaptive text prompts to select appropriate prompts for continual adaptation across multiple domains. Comprehensive experiments on multi-domain incremental learning benchmarks demonstrate that \ChordPrompt outperforms state-of-the-art methods in zero-shot generalization and downstream task performance.

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