Multi-task Self-training Neural Network for General Visual Representations
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Solution Overview
Problem
Learning a single general model that can perform well across multiple computer vision tasks remains a challenging problem in the field, as existing technologies struggle to effectively utilize unlabeled or partially labeled datasets for universal visual representation learning.
Innovation Solution
The implementation of multi-task self-training (MuST) for an image representation neural network, which leverages knowledge from specialized teacher models to train a single general student model, allowing it to generate feature representations that can be used for various computer vision tasks, including image recognition, detection, segmentation, and 3D geometry estimation, and enables efficient adaptation to new tasks with limited data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single general model is trained to perform multiple computer vision tasks, then versatility and adaptability improve, but performance on individual tasks deteriorates compared to specialized models
Solution Approach 1:
The patent applies preliminary action by pre-training the image representation neural network on multiple diverse datasets (ImageNet, COCO, ADE20K, etc.) before deployment. This pre-training establishes a strong foundational knowledge base that enables the model to perform multiple tasks effectively without requiring task-specific training, thus resolving the contradiction between versatility and performance.
Solution Approach 2:
The patent implements universality by designing a single image representation neural network that can perform multiple computer vision tasks including image classification, object detection, semantic segmentation, and 3D geometry estimation. The model uses a unified architecture with shared parameters across all tasks, eliminating the need for separate specialized models while maintaining competitive performance through multi-task learning objectives.
2Reliability
If specialized models are trained for each specific task, then performance on that task improves, but device complexity and training time increase
Solution Approach 1:
The patent merges multiple task-specific models into a single unified image representation neural network. By combining the functionalities of classification, detection, segmentation, and 3D estimation models into one architecture with shared feature extraction layers, the patent reduces overall system complexity while maintaining performance through multi-task learning mechanisms that leverage commonalities across tasks.
Solution Approach 2:
The patent creates a universal model that replaces multiple specialized models. The single neural network is designed to handle diverse computer vision tasks using shared parameters and task-specific output heads, thereby reducing the number of models needed, simplifying deployment, and decreasing training time while maintaining competitive performance across all tasks.
3Reliability
If labeled training data is used for supervised learning, then model performance improves, but data requirements and annotation costs increase
Solution Approach 1:
The patent applies preliminary action by pre-training the model on large-scale labeled datasets (ImageNet, COCO, ADE20K) to establish robust feature representations. This pre-training phase captures general visual patterns that can be transferred to downstream tasks, reducing the amount of task-specific labeled data needed. The model then fine-tunes on smaller labeled datasets or even performs zero-shot inference on unseen tasks.
Solution Approach 2:
The patent implements self-service through self-training mechanisms where the model generates its own training signals. By using pseudo-labels generated from model predictions and unlabeled data, the system continuously self-improves without requiring extensive manual annotation. This self-supervised learning approach leverages the model's own predictions as training targets, reducing dependency on large quantities of labeled data.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an image representation neural network.