Hybrid Convolution-Attention Model for Computer Vision
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Solution Overview
Problem
Current computer vision processing techniques, such as convolutional neural networks (CNNs) and self-attention mechanisms, operate under different design paradigms, limiting their ability to fully leverage each other's strengths for image processing tasks.
Innovation Solution
A hybrid model that projects input visual data into intermediate feature maps using 1×1 convolution operations, generates attention-weighted maps through attention and aggregation operations, and combines these with convolved feature maps via shift and summation operations, allowing for the integration of both paradigms with minimal computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If convolution neural networks are used for image processing, then processing speed and computational efficiency are improved, but the ability to capture global context and adapt to different regions is limited
Solution Approach 1:
The patent combines convolutional neural networks and self-attention mechanisms into a hybrid architecture. The convolutional layers maintain computational efficiency for local feature extraction, while the self-attention mechanism is integrated to capture global context and adaptive relationships between different regions of the input data, resolving the contradiction between processing speed and global context capture capability.
Solution Approach 2:
The hybrid model enables the system to perform multiple functions simultaneously: efficient local feature extraction through convolution and global context analysis through self-attention. This multi-functionality allows the model to adapt to different processing requirements and maintain both speed and comprehensive feature capture capability.
2Adaptability or versatility
If self-attention mechanisms are used for image processing, then the ability to focus on different regions adaptively is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent merges self-attention with convolutional operations, where the self-attention mechanism processes features extracted by convolutional layers. This integration allows the system to leverage the computational efficiency of convolutions while adding the adaptive focusing capability of self-attention, managing computational complexity through structured integration rather than standalone self-attention blocks.
Solution Approach 2:
The model segments the processing task into distinct stages: first performing convolutional operations for efficient local feature extraction, then applying self-attention mechanisms to the convolved features for adaptive regional focusing. This segmentation of functions reduces overall computational complexity by avoiding redundant operations and allowing each component to optimize for its specific function.
3Measurement precision
If both convolution and self-attention modules are integrated, then feature capture comprehensive is improved, but model complexity increases
Solution Approach 1:
The patent integrates convolutional and self-attention modules into a unified hybrid architecture where both mechanisms operate synergistically. The convolutional layers provide efficient local feature extraction while the self-attention mechanism enhances global context understanding and adaptive regional focusing. The integration is designed to be computationally efficient, avoiding redundant operations and maintaining manageable model complexity while achieving comprehensive feature capture.
Data Source
AI summary
A method for computer vision processing. The method includes projecting input visual data into a plurality of intermediate feature maps by performing a plurality of 1×1 convolution operations; generating an attention weighted map by performing attention and aggregation operations on the plurality of intermediate feature maps; generating a convolved feature map by performing shift and summation operations on the plurality of intermediate feature maps; and adding the attention weighted map and the convolved feature map based on at least one scalar.