Task-Specific Prompt Modules for Multi-Task Image Model Training
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Solution Overview
Problem
Existing image processing models, such as convolutional neural networks (CNNs), are limited to performing a single target task, leading to low processing efficiency and complex training processes when multiple tasks are required.
Innovation Solution
An image processing model incorporating multiple prompt modules and modules that add task-specific prompt information to sample images, allowing for improved accuracy in executing multiple tasks by adjusting parameters based on prediction and reference results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If one neural network model is used to perform only one target task, then the model structure is simple and easy to train, but the processing efficiency is low when multiple tasks are required
Solution Approach 1:
The patent implements a single neural network model that can perform multiple target tasks (target detection, image recognition, semantic segmentation, depth estimation) by integrating multiple processing modules within one framework. This allows the system to handle multiple tasks efficiently without requiring separate models for each task, thereby improving processing efficiency while managing complexity through a unified architecture.
Solution Approach 2:
The neural network model is divided into multiple independent processing modules, each responsible for a specific target task. This segmentation allows each module to be trained and optimized independently for its specific function, while the overall model structure remains unified. This approach enables efficient multi-task processing by allowing parallel operation of different modules.
2Productivity
If multiple target tasks are performed using one neural network model, then processing efficiency improves, but the training process becomes complex
Solution Approach 1:
The training process is segmented into independent modules, where each processing module can be trained separately on its specific task. This modular training approach simplifies the overall training process by allowing independent optimization of each task-specific module, reducing the complexity of training a unified multi-task model while maintaining the ability to perform multiple tasks efficiently.
Solution Approach 2:
The patent employs pre-trained models for each processing module before integrating them into the unified framework. By pre-training each module independently on its specific task using available datasets and techniques, the system simplifies the final integration and training process, as each module already possesses task-specific knowledge that can be fine-tuned within the unified model.
3Measurement precision
If prompt information is added to sample images based on Y prompt modules, then the accuracy of prediction results for each image processing task is improved, but the complexity of the model increases
Solution Approach 1:
Prompt information is added locally to specific regions or features within sample images based on task requirements, rather than uniformly across the entire image. This localized prompting approach improves prediction accuracy for specific target tasks by providing relevant contextual information only where needed, while avoiding the unnecessary complexity of global prompt integration across all image regions.
Data Source
AI summary
This disclosure relates to the field of image processing, and discloses an apparatus, method and readable storage medium for image processing model training. The model includes multiple prompt modules and multiple image processing modules corresponding to multiple prompt modules. The method comprising: input N sample images into multiple prompt modules to add corresponding prompt information to the N sample images. Thus, N prompt sample images corresponding to each image processing module may be obtained. And then input the N prompt sample images into each image processing module, to obtain N prediction results corresponding to the each image processing module. Then parameters of multiple image processing modules and multiple prompt modules can be adjusted based on N prediction results and N reference results corresponding to each image processing module.