Modulated Neural Network for Fast Video Object Segmentation
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Solution Overview
Problem
Neural network-based image segmentation on low-computational power devices, such as mobile phones, is slow due to the need for large models that exceed memory and computational resources.
Innovation Solution
A modulated neural network system that includes a visual guide modulator and a spatial guide modulator, which adjust intermediate feature maps of a segmentation network using channel-wise and location-wise parameters, allowing for faster image segmentation by focusing on specific objects and tracking them across video sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large neural network models are used for image segmentation, then segmentation accuracy is improved, but computational resources and memory requirements increase
Solution Approach 1:
The patent divides the neural network into a main segmentation network and separate modulator networks. The modulator networks generate parameters that adjust the main network's behavior, allowing the main network to be smaller while achieving accuracy comparable to larger models. This segmentation of functionality resolves the contradiction by separating the core segmentation task from the adaptation task.
Solution Approach 2:
The modulator networks generate parameter adjustments (scale and shift parameters) that dynamically change the activation values of the main segmentation network. By changing parameters rather than using a completely different large model, the system achieves improved segmentation accuracy on specific datasets while maintaining smaller computational requirements.
2Measurement precision
If large neural network models are used for image segmentation, then segmentation accuracy is improved, but memory requirements increase
Solution Approach 1:
The system segments the model into a compact main segmentation network and smaller modulator networks. The modulator networks store parameters that can be applied to the main network without requiring the main network to be permanently large. This reduces memory requirements while maintaining segmentation accuracy.
Solution Approach 2:
The modulator networks dynamically adjust the main network's parameters during inference based on the specific task requirements. This dynamic adaptation allows the system to use smaller memory footprint when not processing specific datasets, while achieving high accuracy when needed through parameter modulation.
3Productivity
If conventional image segmentation methods are used, then implementation simplicity is maintained, but processing speed is slow
Solution Approach 1:
The patent segments the processing into two stages: a fast main segmentation network that provides baseline performance, and modulator networks that refine the results. This segmentation allows the system to achieve high processing speed through the efficient main network while adding complexity only where necessary for improvement.
Solution Approach 2:
The modulator networks apply partial adjustments to the main network's output rather than completely reprocessing everything. By applying modifications only to specific regions or features identified by the main network, the system achieves faster processing speeds while maintaining improved accuracy.
Data Source
AI summary
A modulated segmentation system can use a modulator network to emphasize spatial prior data of an object to track the object across multiple images. The modulated segmentation system can use a segmentation network that receives spatial prior data as intermediate data that improves segmentation accuracy. The segmentation network can further receive visual guide information from a visual guide network to increase tracking accuracy via segmentation.