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

TL;DR

ChordPrompt enhances continual learning in vision-language models by enabling cross-modal prompt interactions and domain adaptation, significantly improving performance across multiple domains without extensive retraining.

Contribution

The paper introduces ChordPrompt, a novel framework that leverages cross-modal prompts and domain-adaptive text prompts for multi-domain incremental learning in CLIP.

Findings

Outperforms state-of-the-art methods in zero-shot generalization.

Achieves superior downstream task performance across multiple domains.

Demonstrates effective cross-modal prompt synergy in continual learning.

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.