Image Classification via High-Dimensional Vector Matching
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Solution Overview
Problem
Current visual recognition systems face challenges in accurately predicting category labels for images, especially for zero-shot predictions and labels that are semantically or syntactically related to the correct label, with existing systems often generating inaccurate or generic labels.
Innovation Solution
A modified visual recognition system is trained to generate high-dimensional representations of input images, using a transformation layer to produce representations that maximize cosine similarity with known category labels, allowing for accurate prediction of specific and generic labels by associating each term in a vocabulary with a respective high-dimensional representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional visual recognition systems are used for zero-shot predictions, then the system can handle unseen categories, but the prediction accuracy deteriorates and generates generic labels
Solution Approach 1:
The patent transforms the classification problem from traditional category scoring to high-dimensional vector space matching. By representing both images and category labels as high-dimensional vectors and using cosine similarity for comparison, the system operates in an additional dimensional space that enables accurate zero-shot predictions without sacrificing precision
Solution Approach 2:
The patent changes the fundamental parameters of the visual recognition system by replacing traditional classification layers with a transformation layer that outputs high-dimensional vectors. The loss function is also changed to maximize cosine similarity between image vectors and label vectors, fundamentally altering the optimization objective to improve both adaptability and precision
2Device complexity
If traditional classification methods are used, then the system structure remains simple, but the ability to predict semantically related labels deteriorates
Solution Approach 1:
The patent introduces high-dimensional vector representations as an intermediary between image features and category labels. This intermediary layer captures semantic relationships by transforming both images and labels into a shared vector space where semantic similarity is reflected in vector proximity, enabling prediction of semantically related labels
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying images. One of the methods includes obtaining data that associates each of a plurality of object category labels with a respective high-dimensional representation of the object category label, wherein the high-dimensional representation of the object category label is a numeric representation of the object category label in a high-dimensional space; receiving an input image; processing the input image using one or more core layers to generate an alternative representation of the input image; processing the alternative representation of the input image using a transformation layer to determine a high-dimensional representation for the input image; selecting, from the high-dimensional representations associated with the object category labels, a closest high-dimensional representation to the high-dimensional representation for the input image; and selecting the category label associated with the closest high-dimensional representation as a predicted label for the input image.