Domain Adaptation Object Recognition via Generative Model
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Solution Overview
Problem
Conventional object recognition technologies face limitations in adapting to environmental changes, such as illumination and pose variations, requiring large amounts of data for robust feature extraction and struggling to automatically correct all preprocessing issues.
Innovation Solution
A domain adaptation-based object recognition apparatus and method that learns a generative model to generate features or images similar to a gallery image style using a probe image, enabling effective recognition by adapting to external environment changes through a combination of a data collector, generative model learning unit, classification model learning unit, and recognition verifier.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional feature extraction techniques are used to compensate for environment changes, then some robustness is achieved, but they cannot compensate for all actual changes and require large amounts of data
Solution Approach 1:
The patent introduces a domain adaptation layer as an intermediary between the pre-trained model and the target domain data. This layer learns to transform features from the source domain distribution to match the target domain distribution, enabling the model to adapt to environmental changes without requiring large amounts of target domain training data.
Solution Approach 2:
The patent modifies the parameter distribution of features through domain adaptation. By learning a transformation that aligns the statistical parameters (mean, covariance) of source domain features with those of target domain features, the system achieves robustness to environmental changes while requiring minimal target domain data.
2Productivity
If conventional object recognition is performed based on previously registered information, then recognition speed is maintained, but the system cannot adapt to external environment changes
Solution Approach 1:
The patent performs preliminary domain adaptation by learning the style characteristics of gallery images in advance. The generative model is pre-trained to understand the distribution and stylistic features of the target domain, so when actual recognition is needed, the system can quickly adapt to environmental changes without sacrificing recognition speed.
Solution Approach 2:
The patent introduces a dynamic domain adaptation mechanism that can adjust to different environmental conditions. The system maintains the ability to perform fast recognition while incorporating adaptive components that can dynamically adjust to external environment changes based on the input probe image characteristics.
3Reliability
If feature extraction is made robust through preprocessing, then some environmental variations are handled, but preprocessing issues cannot be fully corrected
Solution Approach 1:
The patent replaces traditional mechanical preprocessing operations with a learning-based approach. Instead of using fixed preprocessing algorithms that struggle to correct all issues, the system uses a neural network-based domain adaptation layer that learns to correct preprocessing deficiencies and environmental variations, achieving higher precision in handling diverse conditions.
Data Source
AI summary
A domain adaptation-based object recognition apparatus includes a memory configured to store a domain adaptation-based object recognition program and a processor configured to execute the program. The processor learns a generative model for generating a feature or an image similar to a gallery image on the basis of domain adaptation in association with an input probe image and learns an object recognition classification model by using a learning database corresponding to the gallery image and the input probe image, thereby performing object recognition using the input probe image.