Attention Vector Generation Using Local Receptive Field
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Solution Overview
Problem
Existing attention mechanisms in convolutional neural networks (CNNs) face a trade-off between processing efficiency and inference accuracy, particularly when extracting complex relationships across multiple regions and feature dimensions.
Innovation Solution
The proposed technique implements an attention mechanism through a feature transformation process with a local receptive field, allowing for efficient attention application by acquiring an attention vector and applying it to the input data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If self-attention mechanism processes all region combinations to extract complex relationships, then inference accuracy is improved, but processing amount and computational cost increase significantly
Solution Approach 1:
The patent divides the input data into multiple regions and processes them in a segmented manner using window-based self-attention. Instead of processing all region combinations simultaneously, the system divides the sequence into windows of size w, processes each window independently, and then combines the results. This segmentation reduces the computational complexity from O(N^2) to O(N*w) where N is the total number of elements.
Solution Approach 2:
The patent applies partial self-attention by only processing certain region combinations within each window rather than all possible combinations across the entire input. The window-based approach selectively processes local relationships while maintaining global context through overlapping windows, achieving a balance between computational efficiency and relationship extraction completeness.
2Productivity
If attention mechanism uses fixed weight for feature extraction, then computational cost is reduced, but adaptability to different input features deteriorates
Solution Approach 1:
The patent implements dynamic weight adjustment through self-attention mechanisms that compute attention weights based on the input data itself. The attention weights are calculated as softmax(QK^T/√d) where Q and K are query and key projections of the input features. This allows the model to automatically adapt to different input patterns and prioritize important features dynamically, replacing fixed weight approaches with data-driven adaptive weights.
Data Source
AI summary
An information processing apparatus comprises an acquisition unit configured to acquire input data, and an application unit configured to obtain an attention vector by performing a feature transformation process having a local receptive field on the input data, and apply attention to the input data based on the input data and the attention vector.