One-Shot Image Segmentation via Dual Prediction and Attention Fusion
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Solution Overview
Problem
Conventional few-shot segmentation techniques often fail to accurately predict segmentation masks for query images, especially when the support image is identical to the query image, and they do not efficiently utilize visual similarity information between the support and query images, leading to errors in regions that are not difficult to segment.
Innovation Solution
A neural network with a query branch and a support branch that extract features from both query and support images, generating foreground and background attention maps, which are fused with the respective features and decoded using a shared decoder to predict segmentation masks for both query and support images, along with input channel averaging to prevent overfitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional few-shot segmentation techniques are used, then the model can perform segmentation with limited support images, but the accuracy deteriorates when the support image is identical to the query image and fails to utilize visual similarity information effectively
Solution Approach 1:
The neural network is divided into separate query branch and support branch, each processing respective inputs independently before fusion. This segmentation allows specialized feature extraction for both query and support images, improving the model's ability to handle few-shot scenarios while maintaining accuracy through branch-specific processing.
Solution Approach 2:
The query features and support features are fused together using attention mechanisms, combining information from both branches to generate the final segmentation mask. This merging enables the model to leverage visual similarity information between support and query images, resolving the accuracy deterioration issue in few-shot scenarios.
2Device complexity
If only query image segmentation is predicted, then the model structure is simpler, but the ability to propagate similarity information from support to query features is reduced
Solution Approach 1:
The support branch generates support features and attention maps that provide feedback information about visual similarities to the query branch. This feedback mechanism enables effective similarity information propagation from support to query features, improving segmentation accuracy without requiring excessive model complexity.
Solution Approach 2:
The shared decoder handles both query and support image predictions, making it multi-functional. This universal component reduces overall model complexity while maintaining the capability to propagate similarity information effectively through the query and support branches.
3Measurement precision
If foreground and background features are processed separately, then the attention mechanism can focus on specific regions, but the overall feature representation may be less comprehensive
Solution Approach 1:
The feature extraction is segmented into foreground and background processing paths, allowing separate attention mechanisms to focus on respective regions. This segmentation enables precise attention mapping while maintaining comprehensive feature representation through subsequent fusion of both foreground and background features.
Solution Approach 2:
Foreground features and background features are merged through the attention mechanism and decoder to produce the final segmentation mask. This merging ensures comprehensive feature representation while preserving the precision gains from separate processing, as both foreground and background information contribute to the final output.
Data Source
AI summary
Embodiments of the present invention provide systems, methods, and computer storage media for one-shot and few-shot image segmentation on classes of objects that were not represented during training. In some embodiments, a dual prediction scheme may be applied in which query and support masks are jointly predicted using a shared decoder, which aids in similarity propagation between the query and support features. Additionally or alternatively, foreground and background attentive fusion may be applied to utilize cues from foreground and background feature similarities between the query and support images. Finally, to prevent overfitting on class-conditional similarities across training classes, input channel averaging may be applied for the query image during training. Accordingly, the techniques described herein may be used to achieve state-of-the-art performance for both one-shot and few-shot segmentation tasks.