Wrapped CNN Layers for Global-Local Vision Refinement
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Solution Overview
Problem
Existing iterative improving methods for neural networks, such as feedback mechanisms and recurrent neural networks (RNNs), lack the ability to effectively utilize both global and local information, leading to weak generalization and task specificity, hindering their application across different tasks.
Innovation Solution
A neural network framework incorporating wrapped convolutional neural network (CNN) layers with a note-taking module, amendment module, and summarization module to leverage both local and global information, allowing iterative improvement in computer vision tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If feedback mechanisms or RNNs are used for iterative improvement, then the neural network can perform iterative refinement, but the model cannot effectively utilize both global and local information
Solution Approach 1:
The patent segments the feature extraction process into distinct local feature extractors and a global feature aggregator. Each local feature extractor processes specific regions independently, while the global aggregator combines these local features with global context, enabling the model to utilize both local and global information simultaneously during iterative refinement.
Solution Approach 2:
The patent introduces a new dimensional structure by adding global feature aggregation alongside local feature extraction. This creates a multi-dimensional feature space where local features (spatial dimension) and global features (contextual dimension) coexist, allowing the model to leverage both types of information without conflict.
2Measurement precision
If task-specific iterative improving methods are used, then performance on specific tasks improves, but the method lacks generalization ability across different tasks
Solution Approach 1:
The patent designs a universal iterative improvement framework that can be applied across different computer vision tasks. The architecture uses task-agnostic local feature extractors and a global feature aggregator that work together in a standardized manner, allowing the same framework to generalize across segmentation, detection, and classification tasks while maintaining task-specific performance through configurable loss functions and data.
3Duration of action of moving object
If RNN architecture is used for iterativeness, then sequential processing is achieved, but the ability to use both global and local information is lost
Solution Approach 1:
The patent introduces global feature aggregation as an intermediary mechanism that bridges local feature extraction and sequential processing. Instead of relying solely on RNN's sequential memory, the global aggregator acts as a mediator that explicitly combines local features with global context at each iteration, preserving both sequential processing capabilities and access to global information.
Data Source
AI summary
A system including a computer vision model configured to perform a machine learning task is described. The computer vision model includes multiple wrapped convolutional layers, in which each wrapped convolutional layer includes a respective convolutional layer configured to receive, for each time step of multiple time steps, a layer input and to process the layer input to generate an initial output for the current time step, and a respective note-taking module configured to receive the initial output and to process the initial output to generate a feature vector for the current time step, the feature vector representing local information of the wrapped convolutional layer. The model includes a summarization module configured to receive the feature vectors and to process the feature vectors to generate a revision vector for the current time step, the revision vector representing global information of the plurality of wrapped convolutional layers.