Self-Attention Image Processing via Grid Segmentation
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Solution Overview
Problem
Existing image processing techniques face challenges in efficiently processing high-dimensional feature vectors and reducing computational complexity, particularly when using self-attention mechanisms.
Innovation Solution
An information processing apparatus that generates patch tokens and prefix tokens from an input image, extends the prefix token to match the size of patch token blocks segmented by a grid pattern, and performs arithmetic operations based on a self-attention mechanism for elements at common positions in the blocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If self-attention mechanism is applied to process high-dimensional feature vectors from image grids, then processing accuracy is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the image into multiple grids and processes each grid independently to generate feature vectors. This segmentation allows the self-attention mechanism to operate on smaller, manageable subsets of data rather than the entire high-dimensional feature space, thereby reducing computational complexity while maintaining processing accuracy through localized feature extraction and subsequent aggregation of grid-level results
Solution Approach 2:
The patent transforms the high-dimensional feature vectors from image grids into a different dimensional representation space. By projecting features into this alternative dimensionality, the self-attention mechanism can process relationships between features more efficiently, reducing the computational burden associated with direct high-dimensional operations while preserving the essential semantic information needed for accurate processing
2Measurement precision
If image is divided into fine grids for detailed feature extraction, then feature representation quality is improved, but processing time increases
Solution Approach 1:
The patent divides the image into multiple grids and processes each grid independently and in parallel. This segmentation enables fine-grained feature extraction within each small grid, improving feature representation quality, while the parallel processing capability across multiple grids reduces overall processing time compared to sequential processing of the entire image
Solution Approach 2:
The patent applies feature extraction and self-attention mechanisms selectively to specific regions or grids rather than uniformly processing the entire image at maximum detail. This partial action approach maintains high feature representation quality in critical regions while reducing processing time in less important areas, achieving an optimal balance between quality and efficiency
Data Source
AI summary
An information processing apparatus includes: a generation unit that generates a patch token and a prefix token corresponding to an input image; an extension unit that extends the prefix token to a prefix token block having a size corresponding to a number of a plurality of patch token blocks into which the patch token is segmented in accordance with a predetermined grid pattern; and an arithmetic unit that performs an arithmetic operation on the prefix token block and the patch token blocks, on the basis of a self-attention mechanism, for each group of elements located at a common position in the respective blocks. According to the information processing apparatus, it is possible to properly perform processing based on the self-attention mechanism on the input image.