Transformer Face Feature Extraction With Attribute Multitask Learning
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Solution Overview
Problem
Existing methods for improving feature extraction performance in face recognition using transformer models have not adequately incorporated multitask learning frameworks.
Innovation Solution
An image processing apparatus that performs multitask learning by extracting a feature amount of an object and determining its attribute using a transformer model, comprising an obtaining unit, division unit, conversion unit, addition unit, encoding unit, and determination unit to enhance feature extraction performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a transformer model is used for face recognition, then feature extraction accuracy is improved, but the ability to perform multiple tasks simultaneously is insufficient
Solution Approach 1:
The transformer model is enhanced with multiple output heads that enable simultaneous performance of face recognition and attribute determination tasks. The shared encoder processes input images to generate feature representations that are then fed to task-specific heads, allowing the single model to perform multiple functions effectively.
Solution Approach 2:
The model architecture is segmented into a shared encoder portion and task-specific decoder portions. The encoder extracts general features from input images, while separate heads handle different tasks (face recognition and attribute determination), allowing each component to specialize while benefiting from shared feature extraction.
2Reliability
If separate models are used for face recognition and attribute determination, then task specialization is achieved, but system complexity increases
Solution Approach 1:
Two separate models for face recognition and attribute determination are merged into a single unified transformer model. The model shares a common encoder that extracts features for both tasks, reducing overall system complexity while maintaining task specialization through separate output heads.
Solution Approach 2:
A single transformer model is designed to perform both face recognition and attribute determination tasks simultaneously through multi-task learning. The shared encoder provides universal feature extraction capabilities that benefit both tasks, eliminating the need for separate specialized models.
3Measurement precision
If multitask learning is implemented, then overall prediction performance is improved, but training complexity increases
Solution Approach 1:
The training process is segmented into loss function components corresponding to each task. The total loss is divided into face recognition loss and attribute determination loss, with each component optimized separately but contributing to the overall gradient updates. This modular approach to loss computation simplifies the multitask training process.
Solution Approach 2:
A unified loss function combines multiple task objectives into a single optimization target. The loss function aggregates errors from both face recognition and attribute determination tasks, enabling simultaneous optimization of multiple tasks through standard gradient descent without requiring complex separate training procedures.
Data Source
AI summary
An image processing apparatus includes an obtaining unit, a division unit, a conversion unit, an addition unit, an encoding unit, an extraction unit, and a determination unit. The obtaining unit obtains an image including an object. The division unit divides the obtained image into a plurality of partial images. The conversion unit converts the plurality of partial images into tokens being vectors having a fixed dimension. The addition unit adds a class token, which has the fixed dimension corresponding to the tokens, to a sequence of the tokens. The encoding unit updates the sequence of tokens to which the class token is added based on relevance between the tokens. The extraction unit extracts a feature amount of the object using the updated class token. The determination unit determines an attribute of the object using the updated class token.