Multi-Task Mask Extractor for Generalized Image Segmentation
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Solution Overview
Problem
Existing image segmentation models are limited to a single image segmentation task and perform poorly on other tasks, lacking generalization.
Innovation Solution
A model construction method that trains a mask extractor using a training dataset with mask labels across multiple image segmentation tasks, enhancing its mask extraction capability across various tasks, and constructs an image segmentation model suitable for multiple tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a model is trained for a single image segmentation task, then it achieves high performance on that specific task, but it performs poorly on other segmentation tasks
Solution Approach 1:
The patent applies universality by designing a unified model architecture that can perform multiple image segmentation tasks (semantic segmentation, instance segmentation, panoptic segmentation) simultaneously. The model uses a shared backbone network and unified loss function that accommodates different task requirements, allowing one model to serve multiple functions without requiring separate specialized models for each task type.
Solution Approach 2:
The patent segments the segmentation task into distinct components (semantic mask, instance mask, panoptic mask) that can be processed independently yet integrated within a unified framework. This allows the model to handle different segmentation requirements through separate processing streams while maintaining overall task versatility.
2Measurement precision
If separate models are constructed for different segmentation tasks, then each model achieves optimal performance for its specific task, but the overall system complexity increases
Solution Approach 1:
The patent merges multiple segmentation task processing capabilities into a single unified model architecture. Instead of maintaining separate models for semantic, instance, and panoptic segmentation, the invention combines them into one model that processes all task types through shared computational resources and unified parameter sets, thereby reducing overall system complexity while maintaining task-specific performance.
Solution Approach 2:
The unified model serves as a universal processor for multiple segmentation tasks, eliminating the need for multiple specialized models. This multi-functional approach reduces the number of models required in the system while maintaining the ability to perform each segmentation task with high accuracy.
3Adaptability or versatility
If a unified model is designed for multiple tasks, then model versatility improves, but training difficulty and convergence challenges increase
Solution Approach 1:
The patent applies equipotentiality by designing a unified loss function that balances the training objectives of different segmentation tasks. The loss function is constructed to provide equal importance to semantic segmentation, instance segmentation, and panoptic segmentation, ensuring that no single task dominates the training process and all tasks converge uniformly during model training.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting the weights and hyperparameters of different task components during the training process. This allows the model to adapt to the specific requirements of each segmentation task while maintaining overall training stability and facilitating convergence across multiple task objectives.
Data Source
AI summary
The present application discloses a model construction method and apparatus, an image segmentation method and apparatus, a device and a medium, to improve the effect of image segmentation. The method includes: first training a mask extractor using a training dataset and mask labels that the training dataset has in several image segmentation tasks, so that the trained mask extractor has a good effect of mask extraction under all these image segmentation tasks. Thus, an image segmentation model constructed using the trained mask extractor has a good effect of image segmentation under all these image segmentation tasks.