Vision Transformer Global Context Attention Mechanism
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Solution Overview
Problem
Vision transformers face challenges in capturing long-range spatial dependencies in a computationally expensive manner due to their limited ability to cross-interact with non-local image regions, which restricts their effectiveness in higher resolution applications.
Innovation Solution
The implementation of a vision transformer architecture that incorporates both local and global self-attention mechanisms, where each stage includes a local self-attention module for short-range interactions and a global self-attention module that accesses features from outside the local window, allowing for the modeling of both short-range and long-range dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If vision transformers use local window self-attention to process image patches, then computational cost is reduced, but the ability to capture long-range spatial dependencies deteriorates
Solution Approach 1:
The patent divides the self-attention mechanism into two segments: local self-attention that operates within fixed windows to capture short-range dependencies efficiently, and global self-attention that operates across the entire image to capture long-range dependencies. This segmentation allows the model to process different spatial scopes separately, reducing overall computational cost while preserving both local and global contextual information.
Solution Approach 2:
The patent introduces an intermediary mechanism where local self-attention features serve as the base representation, and global self-attention acts as a mediator to inject long-range contextual information into these local features. This intermediary approach allows efficient local processing to be enhanced by global context without requiring direct computation between all image patches.
2Measurement precision
If vision transformers process higher resolution images with fixed resolution architecture, then image detail processing is improved, but adaptability to different resolutions deteriorates
Solution Approach 1:
The patent employs dynamic resolution processing where the image is first downsampled to a fixed resolution for transformer processing, then the results are upsampled back to the original high resolution. This dynamic approach allows the model to maintain a fixed internal architecture while adapting to various input resolutions, preserving image details through the upsampling operation.
Solution Approach 2:
The patent changes the resolution parameter dynamically by downsampling input images to a standardized fixed resolution before processing, and subsequently restoring the original resolution through upsampling. This parameter transformation allows the fixed-resolution transformer architecture to handle high-resolution images effectively while maintaining adaptability to different input sizes.
3Loss of information
If vision transformers shift windows to cross-interact with non-local regions, then global context capture is improved, but computational expense increases
Solution Approach 1:
The patent segments the attention computation into local and global components, where local self-attention handles within-window interactions and global self-attention handles cross-window interactions. This segmentation eliminates the need to shift windows and recompute attention, as global attention directly computes interactions across all regions in a single pass, reducing computational expense while capturing global context.
Data Source
AI summary
Vision transformers are deep learning models that employ a self-attention mechanism to obtain feature representations for an input image. To date, the configuration of vision transformers has limited the self-attention computation to a local window of the input image, such that short-range dependencies are modeled in the output. The present disclosure provides a vision transformer that captures global context, and that is therefore able to model long-range dependencies in its output.