Neural Network Object Recognition via One-Shot Semi-Supervised Adaptation
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Solution Overview
Problem
Conventional neural network object recognition techniques face challenges with small training datasets, high costs, and inflexibility in adapting to new objects, leading to overfitting and limited ability to handle variations in objects and backgrounds.
Innovation Solution
A one-shot, semi-supervised system for object recognition and segmentation that allows users to customize neural networks by adding desired objects to the training database, using local and global neural network adaptation techniques to generate reliable annotations and improve segmentation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional neural networks are trained on large datasets, then recognition accuracy is improved, but training cost and time increase significantly
Solution Approach 1:
The system performs preliminary action by pre-training the neural network on a generic dataset containing common objects before the user needs to recognize specific objects. This pre-training establishes a foundation of general object recognition knowledge, allowing the network to achieve decent accuracy on small, user-specific datasets without requiring extensive training time on large custom datasets.
2Productivity
If conventional neural networks use fixed training datasets, then training efficiency is improved, but adaptability to new objects deteriorates
Solution Approach 1:
The system implements dynamics by enabling the training dataset to evolve and expand over time. Users can continuously add new objects to their personal dataset, and the neural network re-trains incrementally to incorporate these new objects. This dynamic approach maintains training efficiency by building upon existing knowledge while progressively adapting to new objects and environments.
3Ease of manufacture
If conventional neural networks are trained on small datasets, then training cost is reduced, but overfitting increases and generalization ability decreases
Solution Approach 1:
The system performs preliminary action by pre-training the neural network on a large, diverse generic dataset containing thousands of object categories before deployment. This pre-training establishes robust generalization capabilities and prevents overfitting. When users provide small datasets of their specific objects, the network leverages this pre-acquired knowledge to achieve reliable recognition without requiring large amounts of user-specific training data.
Data Source
AI summary
A system, article, and method of neural network object recognition for image processing includes customizing a training database and adapting an instance segmentation neural network used to perform the customization.