Attention-Based Joint Image and Feature Adaptive Semantic Segmentation
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Solution Overview
Problem
Current image semantic segmentation methods face challenges due to domain gaps between source and target domains in cross-modal scenarios, where pixel-level labeling is labor-intensive and limited by the scarcity of labeled samples, leading to suboptimal performance.
Innovation Solution
An attention-based joint image and feature adaptive semantic segmentation method that combines image adaptation and feature adaptation procedures in an end-to-end manner, using generative adversarial networks and attention modules to transform image styles and align feature spaces, thereby reducing domain gaps and enhancing segmentation model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If cross-modal training strategy is used to pretrain on source modality dataset and then apply to target modality dataset, then the number of usable samples is improved, but the domain gap between source and target domains leads to suboptimal segmentation performance
Solution Approach 1:
The patent introduces a domain adaptation module as an intermediary between the source domain pretrained model and the target domain application. This module includes image translation networks and feature alignment networks that mediate the domain gap, transforming source domain features into target domain-like representations while preserving semantic information, thus resolving the contradiction between utilizing limited labeled samples and maintaining segmentation accuracy.
Solution Approach 2:
The patent changes the parameter space by introducing domain adaptation parameters that adjust the feature representations. Through feature alignment networks and domain classifiers, the model learns to transform features from the source domain parameter space to the target domain parameter space, enabling the model to adapt to the target domain's specific characteristics while maintaining the benefits of source domain pretraining.
2Shape
If image adaptation method is used to transform target domain images into source domain-like images, then the image appearance difference is reduced, but the feature space alignment is insufficient leading to domain gap persistence
Solution Approach 1:
The patent merges image adaptation and feature adaptation into a unified domain adaptation framework. The image translation network transforms image appearances while the feature alignment network simultaneously aligns feature spaces. This combination ensures that both image-level and feature-level representations are adapted, eliminating the domain gap more effectively than image adaptation alone.
Solution Approach 2:
The patent adds a feature space alignment dimension to the traditional image appearance transformation. While image adaptation operates at the pixel level (2D transformation), the feature adaptation module operates at the feature representation level (higher-dimensional transformation), providing multi-level domain adaptation that addresses both appearance and feature consistency.
3Reliability
If feature adaptation method is used to align feature space between source and target domains, then the domain gap at feature level is reduced, but the image appearance transformation is insufficient
Solution Approach 1:
The patent combines feature adaptation with image adaptation by integrating the feature alignment network with the image translation network. The feature alignment network aligns feature representations while the image translation network simultaneously transforms image appearances, ensuring that both feature-level and pixel-level adaptations occur together, thus resolving the limitation of feature adaptation alone.
Solution Approach 2:
The patent complements the feature space (higher-dimensional) adaptation with image appearance (lower-dimensional/pixel-level) transformation. This multi-scale approach ensures that feature representations are aligned while image appearances are also transformed, providing comprehensive domain adaptation that addresses both abstract feature characteristics and concrete visual appearances.
4Adaptability or versatility
If sequential combination of image adaptation and feature adaptation is used, then both adaptation procedures are applied, but the collaboration between procedures is not fully exploited
Solution Approach 1:
The patent merges image adaptation and feature adaptation into a unified end-to-end training framework where both procedures are trained simultaneously rather than sequentially. The shared feature extractor and coordinated optimization allow the procedures to collaborate and refine each other, fully exploiting their synergistic effects and improving training efficiency compared to sequential approaches.
Data Source
AI summary
The invention discloses an attention-based joint image and feature adaptive semantic segmentation method. First, the image adaptation procedure is used to transform the source domain image Xs to a target-domain-like image Xs-t with an appearance similar with the target domain image Xt, to reduce the domain gap between the source domain and the target domain at the image appearance level; then using the feature adaptation procedure to align the features between Xs-t and Xt in the semantic prediction space and the image generation space, respectively, to extract the domain-invariant features, to reduce the domain difference between Xs-t and Xt. In addition, the present invention introduces an attention module in the feature adaptation procedure to help the feature adaptation procedure pay more attention to image regions worthy of attention. Finally, combining the image adaptation procedure and the feature adaptation procedure in the end-to-end manner. The present invention effectively solves the problem of domain gap existing in the cross-modal image semantic segmentation, improves the performance of the semantic segmentation model, and achieves the optimal effect in multiple public data sets.