Semantic Concept Distribution Modeling in Embedding Spaces
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Solution Overview
Problem
Conventional visual-semantic embedding spaces inaccurately represent semantic concepts as single points, failing to capture complex relationships and multiple meanings, leading to inaccurate image labeling.
Innovation Solution
Modeling semantic concepts as distributions in an embedding space, allowing overlap between concepts, such as Gaussian distributions or mixtures, to create a more accurate representation of semantic relationships and enable better image annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If semantic concepts are represented as single points in the embedding space, then the embedding space structure is simple and easy to implement, but the representation accuracy of semantic concepts deteriorates
Solution Approach 1:
The patent changes the representation parameter from a single point (zero-dimensional) to a distribution (multi-dimensional), allowing semantic concepts to be represented as continuous clusters with probability densities. This enables the embedding space to capture the complexity and ambiguity of semantic concepts while maintaining computational tractability through parametric distribution representations.
2Device complexity
If semantic concepts are represented as single points, then the computational complexity is low, but the ability to capture complex relationships and multiple meanings deteriorates
Solution Approach 1:
The patent adds dimensional information to the embedding space by representing semantic concepts as distributions rather than points. This extra dimension allows the model to capture multiple meanings and complex relationships between concepts, such as hierarchical relationships and semantic ambiguity, without exponentially increasing computational complexity.
3Measurement precision
If semantic concepts are represented as continuous clusters with overlap, then the representation accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent applies partial action by focusing computational resources on the most relevant regions of the embedding space. When performing operations, the system only needs to consider regions where distributions have significant overlap or interaction, rather than computing across the entire embedding space, thus reducing computational complexity while maintaining accuracy.
Data Source
AI summary
Modeling semantic concepts in an embedding space as distributions is described. In the embedding space, both images and text labels are represented. The text labels describe semantic concepts that are exhibited in image content. In the embedding space, the semantic concepts described by the text labels are modeled as distributions. By using distributions, each semantic concept is modeled as a continuous cluster which can overlap other clusters that model other semantic concepts. For example, a distribution for the semantic concept “apple” can overlap distributions for the semantic concepts “fruit” and “tree” since can refer to both a fruit and a tree. In contrast to using distributions, conventionally configured visual-semantic embedding spaces represent a semantic concept as a single point. Thus, unlike these conventionally configured embedding spaces, the embedding spaces described herein are generated to model semantic concepts as distributions, such as Gaussian distributions, Gaussian mixtures, and so on.