Multi-Attribute Image Estimation With Segmented Output Layers
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Solution Overview
Problem
Conventional methods for estimating multiple attributes, such as gender and age from a face image, face increased processing load and suffered from inadequate estimation accuracy due to large fully connected numbers and errors propagating between attribute estimations.
Innovation Solution
A single learned model is used to estimate multiple attributes by separating output layers for each attribute, allowing simultaneous estimation of gender and age with reduced fully connected numbers and independent age estimation, thereby improving accuracy and reducing processing load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single learned model is used to estimate multiple attributes collectively, then the model structure is simple, but the fully connected number increases and processing load increases
Solution Approach 1:
The output layer is segmented into multiple independent output layers, each responsible for estimating a specific attribute. This segmentation reduces the fully connected number by avoiding the need to compute all attribute combinations simultaneously, while maintaining a relatively simple overall model structure.
2Device complexity
If a single learned model is used to estimate multiple attributes collectively, then the model structure is simple, but the estimation accuracy deteriorates
Solution Approach 1:
The output layer is segmented into multiple independent output layers, each responsible for estimating a specific attribute. This segmentation reduces the fully connected number by avoiding the need to compute all attribute combinations simultaneously, while maintaining a relatively simple overall model structure.
3Power
If multiple learned models are used to estimate attributes sequentially, then the processing load is reduced, but estimation accuracy deteriorates due to error propagation
Solution Approach 1:
Multiple attribute estimation tasks are merged into a single learned model with multiple output layers. This allows the model to learn shared features across different attributes simultaneously, improving estimation accuracy by preventing error propagation while maintaining efficient processing through unified model architecture.
4Power
If multiple learned models are used to estimate attributes sequentially, then the processing load is reduced, but the overall model complexity increases
Solution Approach 1:
Multiple attribute estimation tasks are merged into a single learned model with multiple output layers. This allows the model to learn shared features across different attributes simultaneously, improving estimation accuracy by preventing error propagation while maintaining efficient processing through unified model architecture.
Data Source
AI summary
An estimation system includes: an acquisition processor that acquires a captured image of an object to be estimated; and an estimation processor that uses a single learned model generated on the basis of learning data, in which an image of the object to be estimated is associated with each of a plurality of attributes of the object to be estimated, to estimate a first attribute included in the plurality of attributes, from a first output value of a first output layer corresponding to the first attribute and to estimate a second attribute included in the plurality of attributes, from a second output value of a second output layer corresponding to the second attribute, with the captured image acquired by the acquisition processor as an input image.