Image Segmentation for Domain Generalization
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Solution Overview
Problem
Existing machine learning models face challenges in domain generalization, particularly in maintaining performance across underrepresented or unseen domains, due to limited training data and variations in conditions such as backgrounds, lighting, and blurriness.
Innovation Solution
The system employs image segmentation to generate masks for source and target images, allowing for the removal of the target image foreground and insertion of the source image foreground into the target image background, creating augmented images that are then used to update the training data of the machine learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the machine learning model is trained with limited training data from specific domains, then the model can be trained efficiently, but the model performance deteriorates when applied to underrepresented or unseen domains
Solution Approach 1:
The patent segments images into foreground and background regions using image segmentation masks. This allows the model to learn domain-invariant features by separating the object of interest from domain-specific background characteristics, enabling better generalization to unseen domains while maintaining training efficiency
Solution Approach 2:
The patent creates augmented training images by copying foreground regions from source images and pasting them onto target image backgrounds. This synthetic data augmentation technique generates diverse training examples without requiring additional real-world data collection, improving model robustness to domain shifts while maintaining efficient training
2Adaptability or versatility
If the training data includes diverse domains and conditions, then the model's ability to generalize improves, but the complexity of data collection and processing increases
Solution Approach 1:
The patent performs image segmentation and foreground-background separation as preliminary processing steps before model training. By pre-processing images to extract domain-invariant foreground features and separate them from domain-specific backgrounds, the system simplifies subsequent training while enhancing domain generalization capability
Solution Approach 2:
The patent uses synthetic augmented images as an intermediary between source and target domains. These augmented images serve as training examples that bridge domain gaps by combining foregrounds from one domain with backgrounds from another, enabling the model to learn domain-invariant representations without requiring extensive real-world multi-domain data
Data Source
AI summary
Methods and systems are disclosed for generating training data for a machine learning model for better performance of the model. A source image is selected from an image database, along with a target image. An image segmenter is utilized with the source image to generate a source image segmentation mask having a foreground region and a background region. The same is performed with the target image to generate a target image segmentation mask having a foreground region and a background region. Foregrounds and backgrounds of the source image and target image are determined based on the masks. The target image foreground is removed from the target image, and the source image foreground is inserted into the target image to create an augmented image having the source image foreground and the target image background. The training data for the machine learning model is updated to include this augmented image.