Few-Shot Computer Vision Model for Multi-Label Feature Identification
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Solution Overview
Problem
Existing few-shot learning models require each example in the support set to have a single label, limiting their ability to adapt quickly to new inputs and recognize relationships between diverse examples, especially in scenarios with insufficient training data, such as rare bird species classification.
Innovation Solution
A computer vision model that identifies a combination of graphic elements in a query image by generating graphic features vectors, using a relation network to find overlapping features with a support set, and inverting a graphic features matrix to derive the exact features present in the query image, even when the query includes features not present in the support set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional few-shot learning models are used with single-label support set examples, then the model structure remains simple, but the model cannot effectively recognize relationships between diverse examples and adapt quickly to new inputs
Solution Approach 1:
The patent segments the feature identification process into multiple independent neural network components: a support set feature extractor, a query feature extractor, and a relationship predictor. Each component handles a specific aspect of the few-shot learning task, allowing the model to process multiple labels and diverse examples effectively while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent implements universal feature extractors that can process both support set examples and query inputs through the same neural network architecture. The model uses shared weight matrices and bias terms across different label types, enabling it to adapt to new inputs and recognize relationships between diverse examples without requiring separate specialized components for each label.
2Measurement precision
If extensive training data is used, then the model achieves high accuracy in object categorization, but the model cannot be applied to scenarios with insufficient training data such as rare bird species classification
Solution Approach 1:
The patent performs preliminary extraction of feature representations from the small support set of labeled examples before performing the actual classification. By pre-processing the support set examples to extract meaningful feature vectors and relationship patterns, the model prepares a compact knowledge representation that can be efficiently applied to the query input, achieving high accuracy even with minimal training data.
Solution Approach 2:
The patent introduces an intermediate relationship prediction layer that acts as a mediator between the support set features and the query classification. This intermediate layer learns to predict relationships between support examples and query inputs based on feature similarity, enabling the model to generalize from few examples to new categorization tasks without requiring extensive training data.
Data Source
AI summary
A technique making use of a few-shot model to determine graphical features present in an image based on a small set of examples with known graphical features. Where a support set including a number of images that each have a known combination of graphical features, the image recognition can identify unknown combinations of those graphical features in any number of query images. In an embodiment of the present disclosure examples of a filled-out form are used to interpret any number of additional filled out versions of the form.