Consistency-guided Prompt Learning for Vision-Language Models (2306.01195v4)

Abstract: We propose Consistency-guided Prompt learning (CoPrompt), a new fine-tuning method for vision-LLMs. Our approach improves the generalization of large foundation models when fine-tuned on downstream tasks in a few-shot setting. The basic idea of CoPrompt is to enforce a consistency constraint in the prediction of the trainable and pre-trained models to prevent overfitting on the downstream task. Additionally, we introduce the following two components into our consistency constraint to further boost the performance: enforcing consistency on two perturbed inputs and combining two dominant paradigms of tuning, prompting and adapter. Enforcing consistency on perturbed input serves to further regularize the consistency constraint, thereby improving generalization. Moreover, the integration of adapters and prompts not only enhances performance on downstream tasks but also offers increased tuning flexibility in both input and output spaces. This facilitates more effective adaptation to downstream tasks in a few-shot learning setting. Experiments show that CoPrompt outperforms existing methods on a range of evaluation suites, including base-to-novel generalization, domain generalization, and cross-dataset evaluation. On generalization, CoPrompt improves the state-of-the-art on zero-shot tasks and the overall harmonic mean over 11 datasets. Detailed ablation studies show the effectiveness of each of the components in CoPrompt. We make our code available at https://github.com/ShuvenduRoy/CoPrompt.

Consistency-Guided Prompt Learning for Vision-LLMs: An Expert Analysis

The paper, titled "Consistency-guided Prompt Learning for Vision-LLMs," introduces a novel fine-tuning framework for vision-language foundation models, specifically designed to enhance their generalization capabilities in few-shot learning scenarios while mitigating overfitting. The proposed method, dubbed CoPrompt, leverages a consistency constraint to align the embeddings generated by fine-tuned models with those from the original pre-trained models, thereby preserving the generalization capacity of these foundational architectures.

Framework and Methodology

CoPrompt employs a dual strategy to refine vision-LLMs, combining the strengths of prompt-based and adapter-based tuning techniques within its architecture. This dual approach is key to its success, as it simultaneously fine-tunes both input prompts and internal network parameters, fostering a more flexible adaptation to new tasks.

1. Consistency Constraint: The cornerstone of the CoPrompt framework is its emphasis on maintaining consistent representations between the fine-tuned and the pre-trained models. This is achieved by enforcing a constraints that align the embeddings of both models across the language and image components. Unlike conventional methodologies that potentially diverge the output representations of fine-tuned models from their pre-trained origins, CoPrompt's approach reduces such deviations, thus enhancing model robustness.
2. Input Perturbations: To bolster the regularizing effect of the consistency constraint, CoPrompt introduces two perturbations: the use of LLMs to generate descriptive text inputs and the application of image augmentation techniques. These perturbations act as a training regularizer, further aligning the invariant representations across varied input instances.
3. Integration of Prompts and Adapters: One of the novel contributions of CoPrompt is its integration of multi-modal prompt tuning with feature adapters. The framework uses LLM-generated prompts on the text side and learnable adapters near the prediction head. This paradigm not only improves downstream task performance but also extends flexibility in tuning different dimensions of the model, thus facilitating effective few-shot learning.

Empirical Evaluation

CoPrompt's effectiveness is substantiated through comprehensive experiments across several benchmarks, including base-to-novel class generalization, cross-dataset evaluation, and domain generalization tasks. Compared to existing techniques, CoPrompt sets a new performance benchmark:

• Base-to-Novel Generalization: It achieves substantial improvements over the state-of-the-art on 11 benchmark datasets, with a marked increase in the harmonic mean of base and novel categories.
• Cross-Dataset Evaluation: CoPrompt shows superior generalization, as evidenced by its ability to transfer learning across diverse datasets.
• Zero-shot Learning: The framework demonstrates improved zero-shot generalization without sacrificing base task performance, highlighting its capability to maintain the innate adaptability of pre-trained models.

Implications and Future Outlook

The introduction of CoPrompt marks a significant advance in the field of vision-LLM fine-tuning, providing a robust mechanism to enhance model versatility and performance. The dual approach of integrating consistency constraints with prompt-adapter tuning could be a promising direction for expanding the utility of foundation models beyond few-shot learning tasks to broader application areas.

In practical terms, the methodology holds promise for enhancing model performance in real-world applications where adaptable, robust machine learning solutions are necessary. Furthermore, the paradigm set by CoPrompt could lead to further research into hybrid strategies that blend multiple tuning techniques, particularly those that target the innate generalization capabilities of foundation models.

In conclusion, CoPrompt represents a promising advancement in the field of model fine-tuning, with implications that extend well into future developments in AI and machine learning applications. Its dual-faceted approach not only sets new performance standards but also paves the way for innovative adaptations of existing foundational models.