Multi-Scale Convolution-Transformer Segmentation for Reduced Resource Use
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Solution Overview
Problem
Existing image segmentation methods, such as CNN-based approaches, are time-consuming to train and inflexible, and transformer networks are resource-intensive for high-resolution images, making them challenging for efficient and flexible image segmentation.
Innovation Solution
A convolution and transformer-based approach that extracts features at multiple scale resolutions, performs self-attention and cross-attention transforms, and generates instance and semantic masks for improved image segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CNN-based approaches are used for image segmentation, then segmentation accuracy can be achieved, but training time increases and flexibility decreases
Solution Approach 1:
The method segments the image processing task into multiple scale resolutions (first scale, second scale, and original resolution), extracting features at each level independently before combining them. This segmentation of the processing task allows parallel computation at different scales, reducing overall training time while maintaining segmentation accuracy through multi-scale feature integration.
Solution Approach 2:
The system dynamically adapts the processing resolution based on the specific segmentation task requirements. By using multiple scale resolutions rather than a fixed high resolution, the system can adjust computational resources dynamically, achieving accurate segmentation while reducing training time for less demanding scenarios.
2Adaptability or versatility
If transformer networks are used for image segmentation, then flexibility and attention mechanisms improve, but resource consumption increases for high-resolution images
Solution Approach 1:
The transformer network processes features at multiple scale resolutions rather than a single high resolution. By segmenting the feature space into different scales, the computational resources required for transformer attention mechanisms are significantly reduced while maintaining flexibility in handling various object sizes and complexities.
Solution Approach 2:
The system applies transformer attention mechanisms selectively at lower scale resolutions where global context is more important, rather than applying full attention at all resolutions. This partial application of the computationally intensive transformer operations reduces resource consumption while preserving the flexibility benefits where they are most needed.
3Measurement precision
If multi-scale feature extraction is performed, then segmentation accuracy improves, but computational complexity increases
Solution Approach 1:
The computational task is segmented into feature extraction at three different scale resolutions, with each scale handling specific aspects of the segmentation problem. This segmentation allows the system to manage computational complexity by distributing work across scales rather than concentrating it all at one high resolution level.
Solution Approach 2:
Features extracted at multiple scale resolutions are merged and combined in the final segmentation process. By merging features from different scales, the system achieves improved segmentation accuracy while the shared feature extraction infrastructure across scales helps manage overall computational complexity through resource sharing.
Data Source
AI summary
Techniques are provided for image processing. For instance, a process can include obtaining an image; extracting a first set of features at a first scale resolution; extracting a second set of features at a second scale resolution (lower than the first scale resolution); performing a self-attention transform to generate similarity scores for the second set of features; adding the similarity scores to the second set of features to generate a first feature extractor output; up-sampling the first feature extractor output to generate a second feature extractor output; adding the second feature extractor output to the first set of features to generate a third feature extractor output; receiving an instance query; performing a cross-attention transform on the instance query and the first feature extractor output to generate a set of weights; and matrix multiplying the set of weights and the third feature extractor output to generate instance masks.