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

Created by
  • Haebom

Author

Zhiyuan Wang, Bokui Chen

Outline

This paper proposes the ChordPrompt framework, which enhances the adaptability of pre-trained vision-language models in Continuous Learning (CL) environments. To overcome the limitations of existing prompt learning methods, which focus on class-specific incremental learning and use single-modal prompts, ChordPrompt introduces cross-modal prompts that leverage the interaction between visual and textual prompts, and domain-adaptive text prompts for continuous adaptation across multiple domains. Experimental results on multi-domain incremental learning benchmarks show that ChordPrompt outperforms state-of-the-art methods in zero-shot generalization and subtask performance.

Takeaways, Limitations

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Takeaways:
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A novel prompt learning framework is presented that is effective for multi-domain task incremental learning scenarios.
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Improving the Continuous Learning Performance of Vision-Language Models by Leveraging Cross-Modal Prompts
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Enhance adaptability to diverse domains with domain-adaptive text prompts.
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Achieving state-of-the-art performance in zero-shot generalization and subtask performance.
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Limitations:
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Further analysis of the generalization performance of the proposed framework is needed.
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Scalability evaluation is needed for various vision-language models and datasets.
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Consideration should be given to the possibility of overfitting to specific domains or tasks.
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