Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling

• URL: http://arxiv.org/abs/2412.07077v1
• Date: Tue, 10 Dec 2024 00:40:31 GMT
• Title: Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling
• Authors: Donggeun Kim, Yujin Jo, Myungjoo Lee, Taesup Kim,
• Abstract summary: We introduce a novel prompt ensemble learning approach called Group-wise Prompt Ensemble (GPE) Our method aims to enhance CLIP's zero-shot capabilities by incorporating new domain knowledge while improving its robustness against data distribution shifts. Our approach hinges on three main strategies: prompt grouping with masked attention to optimize CLIP's adaptability while safeguarding its zero-shot capabilities; the incorporation of auxiliary prompts for the seamless integration of new domain insights without disrupting the original model's representation; and an ensemble learning strategy that effectively merges original and new knowledge.
• Score: 5.6987175375687995
• License:
• Abstract: The advancement of vision-language models, particularly the Contrastive Language-Image Pre-training (CLIP) model, has revolutionized the field of machine learning by enabling robust zero-shot learning capabilities. These capabilities allow models to understand and respond to previously unseen data without task-specific training. However, adapting CLIP to integrate specialized knowledge from various domains while retaining its zero-shot capabilities remains a significant challenge. To address this, we introduce a novel prompt ensemble learning approach called Group-wise Prompt Ensemble (GPE). This method aims to enhance CLIP's zero-shot capabilities by incorporating new domain knowledge while improving its adaptability and robustness against data distribution shifts. Our approach hinges on three main strategies: prompt grouping with masked attention to optimize CLIP's adaptability while safeguarding its zero-shot capabilities; the incorporation of auxiliary prompts for the seamless integration of new domain insights without disrupting the original model's representation; and an ensemble learning strategy that effectively merges original and new knowledge. Through rigorous experimentation, including more challenging cross-dataset transfer evaluations, our GPE method redefines the benchmarks for the adaptability and efficiency of vision-language models, surpassing existing models across various scenarios.

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