Polynomial Self-Attention for Image Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional image classification methods using convolutional neural networks (CNNs) face computational inefficiencies due to the need to process each input point only as a function of its neighboring region, limiting the ability to account for long-range dependencies in data inputs, which is disadvantageous for accurate image classification.
Innovation Solution
The method generates raw matrices with one spatial value and one characteristic value from raw images, enabling element-wise multiplication instead of matrix multiplication, allowing for the consideration of long-range dependencies in real-time and improving image classification accuracy by forming and comparing these matrices with an image classification model using techniques like self-attention weighting and pooling operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional CNNs use pairwise functions to capture spatial interdependencies, then local feature extraction is improved, but computational cost increases and long-range dependencies cannot be captured in real time
Solution Approach 1:
The patent segments the computational process by separating local feature extraction (performed by CNNs) from global dependency modeling (performed by the self-attention mechanism). This allows each component to specialize: CNNs handle local patterns efficiently while the attention mechanism handles global relationships, resolving the contradiction between local accuracy and global efficiency
Solution Approach 2:
The self-attention mechanism acts as an intermediary between local CNN features and global image classification. It takes the feature maps from CNNs and computes pairwise similarities to capture long-range dependencies, thereby mediating between the efficient local processing and the required global understanding without sacrificing computational feasibility
2Ease of operation
If pairwise functions are used to capture spatial interdependencies, then local neighborhood processing is improved, but the ability to account for long-range dependencies deteriorates
Solution Approach 1:
The patent merges CNN-based local feature extraction with self-attention-based global dependency modeling into a unified architecture. The CNNs extract local features while the self-attention mechanism simultaneously captures long-range dependencies by computing relationships across all spatial positions, combining both local and global processing capabilities in one system
Solution Approach 2:
The self-attention mechanism introduces a new computational dimension by computing pairwise similarities across all spatial positions simultaneously, rather than processing only neighboring regions. This dimensional expansion from local to global scope allows the model to capture long-range dependencies while maintaining the benefits of local feature extraction
Data Source
AI summary
A classification apparatus for allocating a raw image to a respective class out of a plurality of classes, the apparatus comprising one or more processors and a memory storing in non-transient form data defining program code executable by the one or more processors to implement an image classification model, the apparatus being configured to: receive a raw image; generate a plurality of raw matrices from the raw image, wherein each of the plurality of raw matrices is formed by one spatial value and one characteristic value; and allocate the raw image to the respective class depending on the plurality of raw matrices. By forming each of the raw matrices by one spatial value and one characteristic value the computational cost may be reduced.