Transformer Feature Conditioning for Semantic Segmentation Adaptation
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Solution Overview
Problem
Current domain adaptation methods for semantic segmentation models are not suitable for online adaptation on single test images and often require large batches of unlabeled target domain data, which is not feasible in real-world scenarios, leading to unreliable predictions due to noisy pseudo-labels and limited computing resources in devices like XR devices and personal robots.
Innovation Solution
A transformer machine learning model is integrated with an existing semantic segmentation model to learn a mapping between unsupervised and supervised outputs by conditioning on an intermediate feature map, enabling test-time adaptation and improving prediction accuracy through parallel processing and global context capture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If domain adaptation methods are used to improve prediction accuracy on target domain data, then segmentation performance is improved, but the requirement for large batches of unlabeled target domain data and high computational resources makes it inapplicable to online adaptation on single test images and devices with limited resources
Solution Approach 1:
The system segments the adaptation task into two independent components: an unsupervised segmentation model that processes single test images without requiring target domain data, and a supervised refinement model that provides guidance. This segmentation allows the system to achieve domain adaptation on single images with limited resources by decoupling the adaptation requirement from batch processing requirements.
Solution Approach 2:
The patent introduces an intermediary mechanism where the unsupervised segmentation model's output is refined using a supervised model's predictions. This intermediary refinement process allows the system to leverage supervised guidance without requiring large batches of labeled target domain data, enabling adaptation on single test images while maintaining prediction accuracy.
2Adaptability or versatility
If large batches of unlabeled target domain data are used for adaptation, then model generalizability is improved, but the data requirement and computational resources exceed the capacity of devices like XR devices and personal robots
Solution Approach 1:
The system applies partial action by using only the necessary minimal components for adaptation: an unsupervised model for basic segmentation and a supervised refinement model. This partial approach achieves sufficient generalizability without requiring excessive data or computational resources, making it suitable for deployment on resource-constrained devices.
Solution Approach 2:
The patent employs lightweight model architectures that can be deployed on devices with limited resources. The unsupervised segmentation model and supervised refinement model are designed to be computationally efficient, enabling adaptation capabilities on devices like XR headsets and personal robots that have constrained memory and processing power.
3Adaptability or versatility
If unsupervised segmentation models are used to process single test images, then online adaptation capability is achieved, but prediction reliability deteriorates due to noisy pseudo-labels
Solution Approach 1:
The system implements a feedback mechanism where the unsupervised segmentation model's predictions are refined by a supervised refinement model. The supervised model provides corrective feedback on the unsupervised model's outputs, reducing the impact of noisy pseudo-labels and improving prediction reliability while maintaining online adaptation capability.
Solution Approach 2:
The unsupervised segmentation model performs preliminary segmentation on the test image before the supervised refinement model processes it. This preliminary action provides an initial structure that the supervised model can refine, allowing the system to achieve reliable predictions on single test images by combining the speed of unsupervised processing with the accuracy of supervised refinement.
Data Source
AI summary
Systems and techniques are provided for processing data (e.g., image data). For instance, according to some aspects of the disclosure, a method may include receiving, at a transformer of a machine learning system, learnable queries, keys, and values obtained from a feature map of a segmentation model of the machine learning system. The method may further include learning, via the transformer, a mapping between an unsupervised output and a supervised output of the segmentation model based on the feature map.