Vision-Language Prompt Learning for Few-Shot Generalization
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Solution Overview
Problem
Existing vision-language models face challenges in adapting to downstream tasks without compromising their original generalization ability, as prompt learning often leads to overfitting and reduced generalizability, and there is a lack of diversity in text-side labels for multi-modal features.
Innovation Solution
The PromptSRC framework employs self-regulating constraints through Mutual Agreement Maximization, prompt self-ensembling, and textual diversity to align prompted features with pre-trained VL representations, using Gaussian weighted sampling and diverse text label templates to enhance generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prompt learning is used to adapt vision-language models to downstream tasks, then task-specific performance is improved, but generalization ability deteriorates due to overfitting
Solution Approach 1:
The method performs preliminary action by generating diverse text label templates before the main training process. Multiple augmented text labels are created for each class category, which are then used to generate pre-trained textual features that guide the prompt learning process, preventing overfitting from the outset
Solution Approach 2:
The method implements feedback by computing a self-regularizing consistency loss that compares prompted features with pre-trained VL features. This loss function provides continuous feedback during training to maintain alignment between task-specific prompted representations and generalizable pre-trained representations
Solution Approach 3:
The method applies parameter changes by using Gaussian weighted sampling to combine pre-trained textual features from multiple text label templates. The Gaussian weighting dynamically adjusts the contribution of different text templates based on their similarity to the target class, optimizing the feature representation for both task-specific performance and generalization
2Adaptability or versatility
If multiple text label templates are used to enhance textual diversity, then generalization is improved, but computational complexity increases
Solution Approach 1:
The method extracts only the essential diverse text label templates needed for generating pre-trained textual features, rather than using all possible templates. This selective extraction reduces computational complexity while maintaining the benefits of textual diversity for generalization
Solution Approach 2:
The method uses parameter changes by applying Gaussian weighted sampling to efficiently combine features from multiple text templates. The Gaussian weighting function dynamically adjusts parameters based on template similarity, reducing the need to process all templates equally and thereby lowering computational complexity
Data Source
AI summary
A computer-readable storage medium and method for adapting a vision-language (VL) machine learning model from a pre-trained VL model. The pre-trained VL model includes a pre-trained image encoder and a pre-trained textual encoder. The method is applicable to few shot learning and training of learnable prompts. After feature extraction, a machine learning engine determines a cross-entropy loss from prompted image features and prompted textual features. The machine learning engine then determines a self-regularizing consistency loss from the prompted image features, pre-trained image features, the prompted textual features, and pre-trained textual features. The learnable text and image prompts are updated based on the cross-entropy loss in combination with the self-regularizing consistency loss. To preserve generalization for downstream tasks, the updating with the self-regularizing consistency loss is constrained to maximize mutual agreement between the prompted image and textual features and the pre-trained image and textual features.