Few-Shot Object Classification via Semantic Attention Fusion
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Solution Overview
Problem
Current AI systems struggle to learn and recognize new visual categories effectively using only one or a few examples, falling short of human performance in few-shot learning tasks.
Innovation Solution
A neural network system is trained using a multi-branch architecture that processes both image and semantic information, generating prototype feature vectors and combining them via a convex combination with semantic attention coefficients, leveraging richer semantic information like labels and descriptions to improve classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning techniques are used to achieve high performance in visual recognition tasks, then classification accuracy is improved, but the requirement for large amounts of training data increases
Solution Approach 1:
The patent introduces semantic information (textual descriptions, attributes, and labels) as an intermediary to bridge the gap between limited visual training data and effective classification. This semantic mediator provides additional contextual knowledge that compensates for the scarcity of training images, enabling the model to achieve high classification accuracy without requiring large volumes of training data.
Solution Approach 2:
The patent combines multiple types of information (visual image features and semantic text features) to create a composite representation for classification. By fusing these different information types, the system creates a more robust and informative feature space that improves classification performance while reducing dependency on large training datasets.
2Measurement precision
If multiple semantic information sources are integrated to improve few-shot learning performance, then classification accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the processing of different semantic information types into separate dedicated modules: one module processes textual descriptions, another processes attributes, and a third processes labels. This segmentation allows each module to specialize in extracting features from its specific input type, improving overall accuracy while managing system complexity through modular design.
Solution Approach 2:
The patent employs a universal fusion mechanism that integrates features from multiple semantic sources (textual descriptions, attributes, and labels) through a unified attention-based framework. This multi-functional approach allows the same computational infrastructure to handle diverse information types, improving classification accuracy without proportionally increasing system complexity.
Data Source
AI summary
Embodiments may provide learning and recognition of classifications using only one or a few examples of items. For example, in an embodiment, a method of computer vision processing may be implemented in a computer comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the method may comprise training a neural network system implemented in the computer system to classify images into a plurality of classes using one or a few training images for each class and a plurality of associated semantic information, wherein the plurality of associated semantic information is from a plurality of sources and comprises at least some of class/object labels, textual description, or attributes, and wherein the neural network is trained by modulating the training images by sequentially applying the plurality of associated semantic information and classifying query images using the trained neural network system.