Dynamic Prompt Tuning for Vision Models
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Solution Overview
Problem
Conventional image processing systems rely on static parameters and fixed algorithms, limiting their adaptability to diverse or changing image content, particularly in real-time applications like autonomous driving and quality control, and require extensive training data, which is costly and resource-intensive.
Innovation Solution
A method and system for dynamic prompt tuning in image processing, using a one-layer neural network combined with a continuous relaxation of a discrete distribution to optimize categorical decision-making, allowing for real-time adjustment of prompt parameters such as position and length, and integrating soft prompts with tokenized image data processed by a pretrained vision model with a frozen backbone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static parameters and fixed algorithms are used in conventional image processing systems, then the system structure is simple and easy to implement, but the adaptability to diverse or changing image content is limited
Solution Approach 1:
The patent implements dynamic prompt tuning where prompt parameters (position, length, content) are adjusted in real-time based on image characteristics and task requirements. The system transitions from static fixed algorithms to dynamic adaptive processing, allowing the same base model to adapt to diverse image content through dynamically generated prompts without requiring complex model architectures or extensive retraining for each scenario.
Solution Approach 2:
The system changes parameters of the processing approach by dynamically modifying prompt parameters (position, length, content) rather than changing the core model structure. This allows adaptability to diverse image content while keeping the base model frozen, effectively separating the adaptive element (prompts) from the fixed computational backbone.
2Adaptability or versatility
If conventional systems use fixed algorithms for image processing, then computational resources are conserved, but the ability to dynamically adapt to real-time variations in image data is reduced
Solution Approach 1:
The system performs preliminary action by pre-training a base vision model on extensive data and then using dynamic prompt tuning for real-time adaptation. The computationally intensive model training is done once in advance, while real-time processing only requires generating and applying prompts, significantly reducing ongoing computational resource consumption while maintaining real-time adaptation capability.
Solution Approach 2:
The patent extracts the adaptive functionality from the core model by separating it into independent prompt parameters. Instead of modifying the entire model for each adaptation scenario, only the prompt parameters are adjusted and extracted for real-time use, while the base model remains frozen and computationally efficient.
3Measurement precision
If extensive training data is used in conventional image processing systems, then model accuracy is improved, but storage costs and data acquisition resources increase
Solution Approach 1:
The system creates a frozen base model through extensive training on large datasets, then uses dynamic prompt tuning as a lightweight copy/adaptation mechanism for different scenarios. Instead of requiring extensive training data for each specific application, the system copies the general knowledge from the pre-trained model and adapts it through prompts, dramatically reducing the training data volume needed for each specific task.
Solution Approach 2:
The system performs the computationally intensive and data-intensive model training in advance to create a frozen base model. This preliminary action allows the system to achieve high accuracy through pre-acquired knowledge, while subsequent real-time applications require minimal additional training data, as adaptation is achieved through prompt tuning rather than retraining.
4Adaptability or versatility
If dynamic prompt tuning is implemented, then adaptability to unique image instances is enhanced, but computational complexity of the processing pipeline increases
Solution Approach 1:
The patent segments the image processing task into distinct components: image segmentation into patches, tokenization through embedding, dynamic prompt parameter computation, soft prompt generation, and integration with the frozen vision model. This segmentation allows each component to be optimized independently and simplifies the overall pipeline by clearly defining the role of each stage in the adaptive process.
Data Source
AI summary
Systems and methods are provided for dynamic prompt tuning in image processing, including decomposing a received image into segments sized to balance detail retention and computational efficiency for processing by an embedding algorithm designed for token generation, generating tokenized image data by transforming each of the decomposed segments into a sequence of tokens using an embedding process that includes a convolutional neural network, and dynamically computing parameters for inserting prompts into the sequence of tokens, including a position and length of the prompts, utilizing a one-layer neural network combined with a continuous relaxation of a discrete distribution for optimizing categorical decision-making. Soft prompts are created based on the dynamically computed parameters and the soft prompts are integrated with the tokenized image data. The integrated image data and prompts are processed using a pretrained vision model with a frozen backbone to enhance image feature recognition.