Domain Adaptation for Object Detection Without Target Retraining
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Solution Overview
Problem
Existing instance segmentation models lack generalization capability across different domains, requiring additional labeled training data and retraining when applied to unseen target domains, which is time-consuming and resource-intensive.
Innovation Solution
A domain adaptation method using a domain vector and domain tensor to enhance Mask R-CNN, allowing prediction to be target domain-specific without retraining on the target domain, leveraging unlabeled images through Deep sets to infer the domain vector.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If instance segmentation models are trained on source domain data, then detection performance is improved on source domain, but generalization capability to target domain deteriorates
Solution Approach 1:
The patent applies local quality by making different parts of the model domain-specific through domain vectors. The backbone network learns domain-invariant features while domain-specific heads (bounding box regression, classification, segmentation) use domain vectors to adapt to target domain characteristics. This allows the model to maintain source domain performance while adapting to target domain specifics.
Solution Approach 2:
The patent changes parameters by introducing domain vectors that encode domain-specific characteristics. These domain vectors are concatenated with feature representations and fed into domain-specific heads, allowing the model to dynamically adjust its behavior based on the target domain without retraining the entire model.
2Adaptability or versatility
If additional labeled training data is prepared for target domain, then model adaptation to target domain is improved, but time consumption and resource usage increase
Solution Approach 1:
The patent applies preliminary action by pre-training the backbone network on source domain data before deployment. The domain-specific heads are designed to work with domain vectors that can be inferred from unlabeled target domain images, eliminating the need for time-consuming labeled target domain data preparation and retraining.
Solution Approach 2:
The model performs self-service by using unlabeled target domain images to infer domain vectors through the backbone network. This self-adaptation mechanism allows the model to adjust to the target domain automatically without requiring external labeled data or manual retraining intervention.
3Measurement precision
If model retraining is performed on target domain, then detection accuracy on target domain is improved, but computational resources and training time are consumed
Solution Approach 1:
The patent segments the model into domain-invariant backbone network and domain-specific heads. Only the domain-specific heads require adaptation using domain vectors, while the backbone network remains frozen. This segmentation reduces computational resources compared to full model retraining while maintaining detection accuracy.
Solution Approach 2:
The backbone network serves multiple functions by learning domain-invariant features that work across different domains. This universal feature extraction capability eliminates the need for domain-specific retraining of the entire model, reducing computational resource consumption while maintaining accuracy.
Data Source
AI summary
A computer-implemented method for domain adaptation of an object detection model includes obtaining a domain vector for a domain from one or more images in the domain, the domain vector representing the property of the domain. The domain vector is input into a fully connected layers in the object detection model. A domain-specific result of the object detection model is provided as output. The method can further include computing a domain tensor and inputting the domain tensor into convolutional layers in the object detection model.