Human Detection via Adversarial Domain Adaptation
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Solution Overview
Problem
Instance object detection and segmentation in road-scene images require labor-intensive annotation processes due to variations in architectural styles and environmental conditions across different domains, making it challenging to train fully supervised models effectively.
Innovation Solution
A method using adversarial learning to align image-level features between source and target domains, selecting unlabeled samples with higher diversity scores and lower prediction scores for annotation, and iteratively updating the label set to improve model performance through a semi-supervised domain adaptation and active learning framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fully supervised model training is performed in every domain, then detection accuracy is improved, but annotation labor intensity increases
Solution Approach 1:
The patent performs preliminary domain adaptation by aligning feature distributions between source and target domains before annotation. A domain discriminator is trained to distinguish between source and target domain features, and a feature alignment mechanism adjusts the source domain features to match target domain characteristics. This preliminary preparation enables the model to achieve high detection accuracy in target domains without requiring extensive manual annotations, as the aligned features provide a strong foundation for subsequent training.
Solution Approach 2:
The patent introduces an intermediary feature alignment mechanism that mediates between source domain training data and target domain detection requirements. The feature alignment module acts as a bridge, transforming source domain features into a representation space that is compatible with target domain characteristics. This intermediary layer enables knowledge transfer from source to target domains while adapting to domain-specific variations, thereby reducing the need for labor-intensive re-annotation in each domain.
2Reliability
If domain-specific annotation is performed for each city and condition, then detection performance is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal domain adaptation framework that can handle multiple target domains (different cities, weather conditions, lighting environments) using a single unified approach. The feature alignment mechanism and domain discriminator are designed to work across various domains without requiring domain-specific customization. This universal framework maintains high detection performance across diverse scenarios while simplifying the training system architecture, as the same core algorithms can be applied to adapt to any new domain without redesigning the entire training pipeline.
3Quantity of substance
If semi-supervised domain adaptation is applied, then annotation requirement is reduced, but model training complexity increases
Solution Approach 1:
The patent performs preliminary feature alignment and domain adaptation before the actual training process. By pre-aligning the feature distributions and pre-training the domain discriminator on source domain data, the system prepares the training infrastructure in advance. This preliminary action reduces the complexity of the subsequent training process, as the aligned features and pre-computed domain statistics can be directly utilized during training, avoiding the need for complex real-time domain adaptation computations.
Solution Approach 2:
The patent implements feedback mechanisms through the domain discriminator, which continuously monitors and provides guidance on feature distribution alignment. The domain discriminator's output feeds back into the feature alignment process, enabling iterative refinement of the feature representation. This feedback loop simplifies the training process by automatically adjusting feature alignments based on actual domain differences, reducing the need for manual tuning and complex hyperparameter optimization.
Data Source
AI summary
Systems and methods for human detection are provided. The system aligns image level features between a source domain and a target domain based on an adversarial learning process while training a domain discriminator. The target domain includes humans in one or more different scenes. The system selects, using the domain discriminator, unlabeled samples from the target domain that are far away from existing annotated samples from the target domain. The system selects, based on a prediction score of each of the unlabeled samples, samples with lower prediction scores. The system annotates the samples with the lower prediction scores.