Multi-Modal Prompt Learning for Flexible Vision-Language Transfer
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Solution Overview
Problem
Pre-trained vision-language models face challenges in fine-tuning for downstream tasks due to data scarcity and risk of overfitting, with current methods focusing on single-modal prompt learning leading to low flexibility and sensitivity in input text prompts, limiting their ability to dynamically adjust both vision and language representation spaces.
Innovation Solution
A multi-modal prompt learning method that generates learnable text and image prompt tokens, using a coupling function to condition vision prompts on text counterparts, allowing for mutual synergy and adaptation of both language and vision branches simultaneously through transformer layers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If prompt learning is used solely for text encoders, then the model can adapt to downstream tasks without fine-tuning, but the flexibility in dynamically adjusting both vision and language representation spaces is reduced
Solution Approach 1:
The patent merges prompt learning mechanisms into both the text encoder and image encoder, creating a unified multi-modal prompt learning system. This allows simultaneous adaptation of both vision and language representation spaces, resolving the contradiction between ease of adaptation and flexibility by integrating prompt tokens into both modalities rather than keeping them separate
2Measurement precision
If the model is fine-tuned for downstream tasks, then it can achieve better performance on specific tasks, but it risks overfitting and forgetting useful knowledge from pretraining
Solution Approach 1:
The patent segments the adaptation process by introducing learnable prompt tokens as separate, parameter-efficient components that can be trained independently from the main model weights. This segmentation allows the model to adapt to downstream tasks through lightweight prompt learning rather than full fine-tuning, thereby improving downstream performance while preventing catastrophic forgetting and overfitting of the pre-trained knowledge
Solution Approach 2:
The patent changes the parameter configuration by freezing most of the pre-trained model parameters and only training the prompt tokens and selected layers. This parameter change strategy enables the model to achieve better downstream task performance while maintaining the robustness of pre-trained knowledge, effectively resolving the contradiction between task-specific performance and generalization capability
3Measurement precision
If careful selection of prompt templates is performed, then the model performance improves, but the complexity of the system increases
Solution Approach 1:
The patent implements self-service by enabling the model to automatically learn and optimize its own prompt tokens through gradient-based training, eliminating the need for manual prompt template selection. The learnable prompt tokens are automatically adjusted during training to achieve optimal performance, thereby improving model performance while reducing system complexity by removing the manual prompt engineering step
Data Source
AI summary
A method and system for multi-modal prompt learning of vision-language models. Encodings of image-text pairs can be combined with image prompts and text prompts before being input into an image encoder and text encoder of a vision-language model respectively. The image prompt can be generated using the text prompt using a vision-language coupling function to encourage synergy between the two prompts. The combination of encodings and prompts can be fed through the transformer layers of the encoders, and the output of each layer can be combined with a new prompt before entering the next layer, up until a specific depth. The subsequent transformer layers can process the output and generate a final representation for the image and text which can then be used for downstream tasks.