Learning to Prompt with Text Only Supervision for Vision-Language Models

Abstract: Foundational vision-LLMs such as CLIP are becoming a new paradigm in vision, due to their excellent generalization abilities. However, adapting these models for downstream tasks while maintaining their generalization remains a challenge. In literature, one branch of methods adapts CLIP by learning prompts using visual information. While effective, most of these works require labeled data which is not practical, and often struggle to generalize towards new datasets due to over-fitting on the source data. An alternative approach resorts to training-free methods by generating class descriptions from LLMs and perform prompt ensembling. However, these methods often generate class specific prompts that cannot be transferred to other classes, which incur higher costs by generating LLM descriptions for each class separately. In this work, we propose to combine the strengths of these both streams of methods by learning prompts using only text data derived from LLMs. As supervised training of prompts is not trivial due to absence of images, we develop a training approach that allows prompts to extract rich contextual knowledge from LLM data. Moreover, with LLM contextual data mapped within the learned prompts, it enables zero-shot transfer of prompts to new classes and datasets potentially cutting the LLM prompt engineering cost. To the best of our knowledge, this is the first work that learns generalized prompts using text only data. We perform extensive evaluations on 4 benchmarks where our method improves over prior ensembling works while being competitive to those utilizing labeled images. Our code and pre-trained models are available at https://github.com/muzairkhattak/ProText.

• The paper introduces ProText, a method that learns prompts using exclusively LLM-generated text to enhance vision-language models.
• It employs a contextual mapping loss to convert detailed textual descriptions into transferable class-specific features without visual labels.
• Extensive evaluations show a +2.08% accuracy gain over baseline CLIP in cross-dataset settings, reducing annotation costs and boosting efficiency.

Learning to Prompt with Text-Only Supervision for Vision-LLMs: A Professional Overview

The paper "Learning to Prompt with Text-Only Supervision for Vision-LLMs" addresses a significant challenge in adapting vision-LLMs like CLIP for downstream tasks without sacrificing generalization. The authors propose a novel approach that combines the strengths of existing image-supervised prompt learning techniques and training-free prompt ensembling methods using LLMs.

Core Contributions

The paper introduces ProText, a method that leverages text-only supervision to facilitate prompt learning in vision-LLMs. The core contribution of this work is the development of a training framework that enables prompts to learn rich contextual features using only text data obtained from LLMs. This innovative approach bypasses the need for visual sample labels, which are often impractical or expensive to obtain, especially in domains like medical imaging or remote sensing.

Key Methodological Insights

1. Text-Only Data Utilization: The authors exploit the capabilities of LLMs to curate detailed class-specific descriptions that serve as the basis for prompt learning. By mapping class names to these descriptions, ProText effectively learns to translate the contextual richness of LLM-generated text into a form usable by vision-LLMs like CLIP.
2. Contextual Mapping Loss: The process employs a contextual mapping objective, allowing the learnable prompts to associate standard class-name templates with enriched class-specific textual features from LLMs. This enables the prompts to encapsulate versatile and transferable contextual information, fostering zero-shot learning across new classes and datasets.
3. Transferability Across Datasets: ProText’s training does not require visual data, thus preserving VLMs’ ability to adapt to unseen datasets without incurring additional LLM serving or prompt engineering costs. This aspect significantly reduces computational and economic barriers associated with traditional model training approaches.

Methodological Implications and Performance

ProText demonstrates its efficacy through extensive evaluations on four benchmarks, revealing substantial improvements over other prompt ensembling and image-supervised methods. For instance, in the cross-dataset transfer setting, ProText achieves an average accuracy gain of +2.08% over baseline CLIP, outperforming even the best image-supervised methods like MaPLe.

The approach holds promise for enhancing generalization capabilities without the risk of overfitting inherent in image-supervised learning. By tapping into the extensive knowledge embedded within LLMs, ProText equips vision-LLMs with a robust contextual understanding that extends beyond the limitations of training datasets.

Future Prospects

The introduction of text-only supervised prompt learning opens several research avenues. Future work can explore the integration of advanced LLM models and fine-tuning strategies to further enhance the efficiency of ProText. Moreover, expanding the method's applicability to more diverse and complex datasets could provide deeper insights into its scalability and adaptability.

In summary, this paper presents a compelling argument for the use of text-only supervision in enhancing the generalization and transferability of vision-LLMs, emphasizing the importance of leveraging LLMs not just for generating richer features but as a fundamental component in model adaptation strategies. As we move forward, such innovations are poised to redefine how we approach model tuning in the landscape of artificial intelligence.