Atrous Self-Attention Transformer for Low-Resolution Image Segmentation
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Solution Overview
Problem
Existing deep learning-based image segmentation methods require high computational resources and high-resolution input, making them unsuitable for mobile devices and situations with limited computational complexity or low-resolution input.
Innovation Solution
The proposed solution involves an image segmentation apparatus that uses recursive atrous self-attention transformer blocks to generate image features through atrous self-attention operations based on multiple dilation rates, and optionally incorporates convolutional self-attention transformer blocks to enhance feature representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning-based image segmentation methods are used, then segmentation accuracy is improved, but computational cost increases
Solution Approach 1:
The patent divides the image processing task into multiple stages with different resolution levels. The high-resolution input image is processed through a series of transformer blocks that operate at progressively lower resolutions, segmenting the computational workload to reduce overall complexity while maintaining accuracy through multi-scale feature extraction
Solution Approach 2:
The patent introduces a resolution dimension as a key differentiator. Instead of processing everything at full resolution, it creates a hierarchy of resolution levels (high-resolution input, intermediate representations, and low-resolution processing). This dimensional approach allows the model to capture fine details where needed while reducing computational burden at lower resolutions
2Measurement precision
If deep learning-based image segmentation methods are used, then segmentation accuracy is improved, but computational complexity increases
Solution Approach 1:
The computational complexity is segmented across multiple resolution levels and transformer blocks. Each block processes a specific resolution tier, dividing the complex task into manageable units that can be processed independently, reducing the complexity burden on any single computational unit
Solution Approach 2:
The patent applies transformer blocks selectively at different resolution levels rather than uniformly across all inputs. Lower-resolution blocks handle the majority of computational tasks, while higher-resolution blocks process only the most critical regions, applying computational resources partially rather than excessively
3Measurement precision
If high-resolution input is used, then segmentation accuracy is improved, but adaptability to low-resolution inputs decreases
Solution Approach 1:
The patent creates a universal processing pipeline that handles multiple input resolutions through the same architecture. The transformer blocks are designed to operate effectively at different resolution levels, making the system universally applicable whether the input is high-resolution or low-resolution without requiring separate models or significant performance degradation
Solution Approach 2:
By introducing resolution as a dimensional variable in the processing pipeline, the system can adapt to different input resolutions through the hierarchical transformer structure. Each resolution level is processed through appropriate transformer blocks that maintain effectiveness across the resolution spectrum, enabling the system to handle both high and low-resolution inputs appropriately
Data Source
AI summary
Systems and methods for image segmentation are described. Embodiments of the present disclosure receive an image depicting an object; generate image features for the image by performing an atrous self-attention operation based on a plurality of dilation rates for a convolutional kernel applied at a position of a sliding window on the image; and generate label data that identifies the object based on the image features.