Semantic Segmentation Training with Shared Features and Partial Labels
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Solution Overview
Problem
Current semantic segmentation models face high training costs due to labor-intensive manual labeling and limited labeled data, leading to overfitting and poor accuracy, especially in scenarios where tasks change or require multi-task categorization.
Innovation Solution
A semantic segmentation model training method utilizing an initial model with shared and task-independent modules, allowing training on partial labeled data, extracting multi-scale shared features, and updating task-specific modules to adapt to different tasks, reducing costs and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If manual labeling is used to create training datasets, then training data can be obtained, but training costs become high and time-consuming
Solution Approach 1:
The patent applies preliminary action by using a pre-trained model to generate preliminary labels for training data. Instead of requiring complete manual labeling from scratch, the system uses automated pre-labeling based on pre-trained model predictions, which significantly reduces the time and cost of data preparation while still providing sufficient training data quality.
2Loss of time
If limited labeled training sample images are used, then training costs are reduced, but model accuracy deteriorates due to overfitting
Solution Approach 1:
The system performs preliminary action by pre-training a model on large-scale unlabeled or partially labeled data using automated methods, then uses this pre-trained model to generate quality labels for a smaller training set. This preliminary pre-training establishes a strong foundation that prevents overfitting when training on limited data, maintaining high accuracy while reducing the quantity of labeled data needed.
Solution Approach 2:
The patent applies copying by using a pre-trained model to generate synthetic training samples or pseudo-labels that replicate the quality of manually labeled data. These copied labels from the pre-trained model serve as substitutes for expensive manual annotation, providing sufficient training data volume without the time cost of manual labeling.
3Measurement precision
If a complex semantic segmentation model is trained to improve accuracy, then segmentation performance improves, but training costs and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-training a model architecture on large datasets before applying it to specific tasks with limited data. This preliminary training establishes efficient feature representations that can be fine-tuned with minimal computational resources, achieving high accuracy without requiring continuously complex and resource-intensive models.
Solution Approach 2:
The patent applies parameter changes by adjusting model complexity and training parameters dynamically. Instead of using a fixed complex architecture, the system optimizes model parameters and structure based on the available training data quantity and task requirements, reducing computational resource consumption while maintaining segmentation accuracy through adaptive parameter tuning.
4Adaptability or versatility
If the semantic segmentation task changes, then model adaptability is improved, but retraining costs increase
Solution Approach 1:
The system performs preliminary action by pre-training a general-purpose model on diverse datasets that covers multiple potential tasks. This pre-trained model serves as a universal foundation that can be quickly adapted to specific tasks through fine-tuning with minimal data and computation, rather than requiring complete retraining when task requirements change.
Solution Approach 2:
The patent applies universality by developing a multi-functional pre-trained model that can handle various semantic segmentation tasks through a single unified architecture. The pre-trained model learns general features and representations that are applicable across different tasks, enabling the system to adapt to new tasks by simply changing task-specific parameters or adding task-specific modules without retraining the entire model.
Data Source
AI summary
This application discloses a semantic segmentation model training method and apparatus, and a semantic segmentation method and apparatus. The method includes: inputting, into an initial semantic segmentation model, a first training sample image of a first training sub-dataset, where the initial semantic segmentation model includes a first initial semantic segmentation module and a second initial semantic segmentation module, the second initial semantic segmentation module includes a first initial task independent module, and the first initial task independent module has a corresponding semantic segmentation task; performing, by the first initial semantic segmentation module, first feature processing on the first training sample image, to obtain a first image feature; obtaining, by the first initial task independent module of the second initial semantic segmentation module, a first semantic segmentation result based on the first image feature; and training the initial semantic segmentation model, to obtain a target semantic segmentation model.