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

Abstract: The advancement of vision-LLMs, 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-LLMs, surpassing existing models across various scenarios.

• The paper introduces Group-wise Prompt Ensemble (GPE) to retain pre-trained knowledge while enhancing domain-specific adaptability.
• It employs innovative prompt grouping and masked attention to maintain the model’s inherent zero-shot capabilities.
• Experimental results show GPE’s superiority in base-to-new class generalization and cross-dataset transfer compared to traditional methods.

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

The paper "Retaining and Enhancing Pre-trained Knowledge in Vision-LLMs with Prompt Ensembling" introduces a novel method called Group-wise Prompt Ensemble (GPE) for improving the adaptability and robustness of vision-LLMs, specifically focusing on the Contrastive Language-Image Pre-training (CLIP) model. The study addresses a critical challenge faced by these models: integrating domain-specific knowledge while retaining their inherent zero-shot learning capabilities.

Vision-LLMs like CLIP have demonstrated impressive abilities in interpreting and understanding complex interactions between visual and textual information without the need for additional training. However, adapting them to incorporate specialized knowledge from niche domains often results in a decline in their zero-shot learning performance. To mitigate this issue, the authors propose GPE as an efficient solution that leverages prompt ensembles to preserve pre-trained knowledge and enhance the adaptability of these models to domain-specific data without compromising their zero-shot capabilities.

Key Contributions and Strategies:

1. Prompt Grouping with Masked Attention: GPE employs a novel approach to prompt ensemble learning by dividing prompts into distinct groups and utilizing masked attention mechanisms. This strategy optimizes the model's adaptability while preserving its original zero-shot performance. By preventing modifications to the model's internal representation, this approach ensures that the pre-trained knowledge is retained.
2. Integration of Auxiliary Prompts: The inclusion of auxiliary prompts allows for the seamless incorporation of new domain insights, expanding the learning context of the model without disrupting its core representation.
3. Ensemble Learning Strategy: The ensemble learning strategy effectively combines original and new knowledge by promoting diversity among prompts. This ensures that each prompt contributes unique and complementary information to the final predictions, boosting the model's overall performance across diverse scenarios. The group-wise ensemble strategy, compared to pair-wise prompting, enhances the model's ability to generalize from base to novel classes.

Experimental Evaluation:

The authors perform rigorous experimentation, including base-to-new class generalization, extended cross-dataset transfer evaluations, and domain generalization settings. The results indicate that GPE significantly outperforms existing models across various metrics. In base-to-new class generalization, GPE achieves the highest harmonic mean, indicating balanced performance between base and novel classes. The extended cross-dataset transfer evaluation further demonstrates GPE’s ability to retain zero-shot performance when fine-tuned on niche datasets, surpassing baseline methods.

Theoretical and Practical Implications:

The proposed GPE method presents a robust framework for maintaining the delicate balance between adaptability to specific domains and retention of generalized zero-shot capabilities in vision-LLMs. By employing prompt ensembling, the paper sets a new standard for integrating domain-specific knowledge into pre-trained models. The versatility and scalability of GPE make it applicable to a wide range of real-world scenarios where domain shifts are prevalent.

Future Directions:

The research opens avenues for further exploration in enhancing prompt diversity and reducing information redundancy among prompts. Additionally, the approach can be extended to other modalities and refined to incorporate more sophisticated self-supervised learning techniques for broader applications. Integrating advancements in ensemble learning methods presents another frontier for achieving greater efficiency and generalization in deploying vision-LLMs across varied tasks.

In conclusion, this paper contributes a strategic advancement in harnessing prompt ensembles for efficient model fine-tuning and sets a benchmark for future research in vision-LLMs' adaptability and robustness in diverse applications.