Positional Local Self-Attention for Efficient Computer Vision
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Solution Overview
Problem
Conventional convolutional neural networks in computer vision tasks are computationally inefficient and lack expressivity due to their reliance on global attention, which is expensive and limited to post-downsampling usage, preventing widespread application across all layers.
Innovation Solution
Replace spatial convolutional layers with positional local self-attention layers that utilize memory blocks and local attention mechanisms to encode positional information, allowing for a fully attentional model that is more computationally efficient and expressive.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If global attention is used in neural networks, then attention mechanism can capture global dependencies, but computational cost becomes excessively high requiring significant spatial down-sampling
Solution Approach 1:
The patent applies local quality by transitioning from global attention to local attention mechanisms. Each position in the sequence attends only to a local neighborhood rather than the entire sequence, reducing computational complexity while maintaining the ability to capture local dependencies and patterns in the data.
Solution Approach 2:
The patent segments the attention mechanism into local windows or neighborhoods. By dividing the global attention computation into multiple local attention computations, the system reduces the computational burden while preserving the essential attentional functionality for capturing local patterns.
2Adaptability or versatility
If conventional convolutional layers are used, then spatial translation invariance is achieved, but expressivity is limited and parameter count increases
Solution Approach 1:
The patent replaces the mechanical convolution operation with an attention-based mechanism. Instead of using fixed kernel convolutions that slide across the input, the system uses self-attention mechanisms that dynamically weigh relationships between positions, achieving both translation invariance and enhanced expressivity with fewer parameters.
Solution Approach 2:
The patent changes the fundamental parameterization from convolutional kernels to attention weights. This parameter change allows the model to achieve the same translation invariance property as convolutions while having fewer parameters and greater expressivity, as attention mechanisms can adaptively capture spatial relationships without requiring large numbers of filter parameters.
3Productivity
If positional local self-attention is implemented, then computational efficiency improves and parameter requirements reduce, but implementation complexity increases
Solution Approach 1:
The patent implements a universal attention mechanism that can replace multiple different operations (convolutions, pooling, etc.) with a single self-attention layer. This multi-functional approach, while increasing implementation complexity, achieves superior computational efficiency and performance across various computer vision tasks.
Data Source
AI summary
A system implemented as computer programs on one or more computers in one or more locations that implements a computer vision model is described. The computer vision model includes a positional local self-attention layer that is configured to receive an input feature map and to generate an output feature map. For each input element in the input feature map, the positional local self-attention layer generates a respective output element for the output feature map by generating a memory block including neighboring input elements around the input element, generates a query vector using the input element and a query weight matrix, for each neighboring element in the memory block, performs positional local self-attention operations to generate a temporary output element, and generates the respective output element by summing temporary output elements of the neighboring elements in the memory block.