Semantic Segmentation Network Using Uniform Clustering for Domain Adaptation
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Solution Overview
Problem
Current unsupervised domain adaptation methods for semantic segmentation fail to clearly define class boundaries, leading to inaccurate classification of small and dense objects, such as traffic lights being misclassified as background buildings, which is critical in real-world applications like automatic driving.
Innovation Solution
An unsupervised domain adaptation method based on uniform clustering is introduced, which establishes a source domain dataset with labels and a target domain dataset without labels, using a semantic segmentation network model and a discriminator for adversarial training, with an objective function that includes semantic segmentation loss, prototype-based uniform clustering losses, and adversarial loss to optimize network parameters and enhance class structure clarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If adversarial training is used to align source and target domains, then domain adaptation performance is improved, but class boundary clarity deteriorates
Solution Approach 1:
The patent segments the feature space into class-specific regions by introducing class-prototypes and computing class-conditional distributions. This segmentation allows the model to maintain clear class boundaries while performing domain adaptation, as each class is handled independently through its own prototype and distribution parameters.
Solution Approach 2:
The patent changes the parameter representation by introducing class-conditional distribution parameters (mean and covariance) for each class. This parameterization enables the model to explicitly control class boundary characteristics while adapting to different domains, resolving the contradiction between adaptation and boundary clarity.
2Measurement precision
If clustering structures are used for task-discriminative features, then classification accuracy is improved, but margin-awareness for small and dense objects deteriorates
Solution Approach 1:
The patent adds a new dimension to the feature space by introducing class-conditional distribution parameters (mean and covariance matrices). This dimensional extension allows the model to capture margin information and class structure simultaneously, enabling both accurate classification and margin-awareness for small and dense objects.
Solution Approach 2:
The patent combines multiple components (class-prototypes, class-conditional distributions, and domain adaptation mechanisms) into a composite framework. This composite approach integrates clustering structures with margin-awareness, allowing the model to achieve both classification accuracy and sensitivity to small and dense objects.
3Loss of energy
If synthetic datasets are used to reduce annotation costs, then labor cost is reduced, but domain discrepancy increases
Solution Approach 1:
The patent changes the parameter representation by introducing class-conditional distribution parameters that can be learned from synthetic data. This parameterization enables the model to adapt to domain discrepancies by learning domain-invariant class structures, allowing effective use of synthetic datasets while maintaining performance on real-world data.
Data Source
AI summary
The present disclosure discloses an unsupervised domain adaptation method, a device, a system and a storage medium of semantic segmentation based on uniform clustering; first, a prototype-based source domain uniform clustering loss and an empirical prototype-based target domain uniform clustering loss are established, to reduce intra-class differences of pixels responding to the same category; meanwhile, the pixels with similar structures but different classes are driven away from each other, wherein they tend to be evenly distributed, increasing the inter-class distance and overcoming the problem that the category boundaries are unclear during the domain adaptation process; next, the prototype-based source domain uniform clustering loss and the empirical prototype-based target domain uniform clustering loss are integrated into an adversarial training framework, which reduces the domain difference between the source domain and the target domain, thus improving the accuracy of semantic segmentation.