Semantic Segmentation Domain Adaptation Using Contrastive Features
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Solution Overview
Problem
Existing methods for domain adaptation of deep learning models trained for semantic segmentation tasks fail to effectively adapt to new domains with limited annotated data, leading to inaccurate prediction results due to differences in data distribution.
Innovation Solution
Utilizing a contrastive learning algorithm to train a feature extractor of the semantic segmentation neural network, pulling positive feature samples from the target domain towards those from the source domain to establish accurate decision boundaries for classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a deep learning model trained on source domain data is applied to target domain data, then the model can be deployed quickly, but the prediction accuracy deteriorates due to domain distribution differences
Solution Approach 1:
The patent performs preliminary domain adaptation training using contrastive learning on a small amount of target domain data before deployment. This preliminary action aligns the feature distributions of source and target domains, enabling the model to adapt quickly while maintaining high prediction accuracy on the target domain
Solution Approach 2:
The patent modifies the training parameters by introducing a contrastive loss function that changes the feature space representation. By adjusting the loss function parameters to minimize domain distribution differences, the model achieves better generalization to the target domain without requiring extensive retraining
2Quantity of substance
If extensive annotated data from source domain is used for training, then model training completeness is improved, but adaptability to target domain deteriorates when only limited target domain data is available
Solution Approach 1:
The patent introduces contrastive learning as an intermediary mechanism that bridges the source domain and target domain. By using a small amount of target domain data to learn domain-invariant features through contrastive loss, the model effectively transfers knowledge from the source domain while adapting to the target domain, achieving good adaptability with limited target data
Solution Approach 2:
The patent employs asymmetric data usage where extensive source domain data is used for initial training and limited target domain data is used for domain adaptation. This asymmetric approach leverages the abundance of source data while efficiently utilizing the scarce target data to achieve domain transfer
3Adaptability or versatility
If classification task domain adaptation methods are applied to semantic segmentation tasks, then method reusability is improved, but task performance deteriorates due to fundamental task differences
Solution Approach 1:
The patent adapts the contrastive learning approach specifically for semantic segmentation by applying it at the pixel-level feature representation stage. By modifying the local quality of the adaptation method to match the pixel-wise nature of segmentation tasks, the patent maintains method reusability while achieving segmentation-specific performance
Data Source
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AI summary
The present disclosure provides a method for domain adaptation of a semantic segmentation neural network, comprising: obtaining a training dataset, wherein the training dataset is annotated and includes training samples from a source domain and training samples from a target domain; and based on the training dataset, training a feature extractor of the semantic segmentation neural network using a contrastive learning loss function to obtain a trained feature extractor; wherein the trained feature extractor is used to extract features from input data from the target domain to obtain feature data for generating semantic segmentation prediction results.